**Preface to "Circular Economy in Low-Carbon Transition"**

Climate change, environmental pollution, the energy crisis and resource depletion have become more and more serious. The circular economy, as a new business model that is different from the economy, can achieve the reuse and recycling of waste for waste minimization, improve the efficiency of resource utilization, and mitigate carbon emissions. It is no doubt that promoting the development of the circular economy can facilitate the transition to low-carbon processes and carbon-neutral development. However, there are still several questions that need to be answered: (1) How can the circular economy contribute to a low-carbon transition? (2) How do we address the fact that the circular economy model may also cause some new environmental problems, and how should we identify what the most environmentally friendly solution is among multiple alternatives for the circular economy? (3) Governmental regulation, policies and incentives play a significant role in promoting the development of the circular economy, so what are the policy instruments that can contribute to its development? (4) How can technological progress and solutions contribute to the circular economy? (5) How can environmental impact assessments contribute to the circular economy? (6) How can we achieve a circular economy or low-carbon transition through changes in consumption behaviors? In order to answer the above-mentioned questions, we launched a Special Issue in *Energies*. There are a total of six papers published in this Special Issue. This e-book collects these papers to build a platform for sharing advanced concepts, tools and methods for the users to take actions to achieve a circular economy.

There are six chapters in this book. Chapter 1 is a short commentary about the circular economy and the book. Chapter 2 focuses on a comparative environmental assessment of heat pumps and gas boilers based on a life cycle tool, and it demonstrates how to identify the most environmentally friendly solution when there are multiple solutions for achieving a circular economy. Chapter 3 introduces a modern policy instrument, a digital product passport, to contribute to the development of a circular economy. Chapter 4 used Aspen Plus for the simulation of a biomass gasification system for combined heat and power and conducts an economic assessment of this system to show how technological solutions and tools can be used to achieve a circular economy in a low-carbon transition. Chapter 5 aims to assess the environmental impacts of short and long food supply chains in some EU countries and uses LCA to compare eco-efficiency. Chapter 6 presents a comprehensive bibliometric analysis of carbon labeling schemes from 2007 to 2019, and the readers can learn how carbon labeling schemes can help to change human behaviors for carbon emission mitigation.

> **Anna Mazzi and Jingzheng Ren** *Editors*

## *Editorial* **Circular Economy in Low-Carbon Transition**

**Anna Mazzi 1,\* and Jingzheng Ren 2**


The circular economy represents a fundamental pillar for modern business models and sustainable development targets: the mandatory claim "reduce, reuse, recycle" is the answer to the global criticalities of natural resources depletion and waste increase [1,2]. At the same time, energy production and consumption play key roles in the face of challenges of industrialization and rapid population growth: with the depletion of traditional fossil fuels, renewable and low-carbon energy sources have attracted more and more attention for their advantages such as high renewability, great development potential, and possible emissions-mitigation [3,4]. To implement the circular economy in the low-carbon transition, new supply chain opportunities can be explored; at the same time, new dilemmas must be carefully solved through the life-cycle approach, to avoid the environmental burdens shifting [5,6]. The international community—including scientists, policymakers, industries, and markets—must develop new tools and competencies to support interdisciplinary innovation through the adoption of a comprehensive perspective, and to generate sustainable values from green low-carbon behavior [7,8].

This book contains the successful invited submissions [9–13] to the Special Issue of *Energies* (ISSN 1996-1073) on the subject area of "Circular Economy in Low-Carbon Transition" in the section "Energy Economics and Policy". This Special Issue contributes to outline a roadmap of circular economy in the low-carbon transition, through the exchange of experiences in different contexts with both environmental and socio-economic points of view.

We sincerely thank the editorial staff and reviewers for their efforts and help to collect, select, and review the papers. We believe that the published articles will inspire both scientists and practitioners to explore new directions to the circular economy in new carbon transition.

Qualitative and quantitative measurements in resources/energy utilization, multicriteria impact assessment in energy systems, and closing the loop initiatives enrich the international debate relating the topic. New research trends underlined by this Special Issue encourage continued discussion about the role of energy policies and technologies to achieve the SDGs and the climate actions using a life-cycle approach. The common objective must be the overall reduction in impacts and the formulation of substantially sustainable solutions, rather than downloading the problems along the supply chain or postponing the damages in the next decades.

**Author Contributions:** Conceptualization, A.M. and J.R.; writing—original draft preparation, A.M.; writing—review and editing, J.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Citation:** Mazzi, A.; Ren, J. Circular Economy in Low-Carbon Transition. *Energies* **2021**, *14*, 8061. https:// doi.org/10.3390/en14238061

Received: 24 November 2021 Accepted: 30 November 2021 Published: 2 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Data Availability Statement:** Not applicable.

#### **Conflicts of Interest:** The authors declare no conflict of interest.

## **References**


## *Article* **A Comparative Environmental Assessment of Heat Pumps and Gas Boilers towards a Circular Economy in the UK †**

**Selman Sevindik 1, \* , Catalina Spataru 1 , Teresa Domenech Aparisi <sup>2</sup> and Raimund Bleischwitz 2**

	- Cologne, Germany, 1–5 September 2020 and ICEESEN Conference, Kayseri, Turkey, 19–21 November 2020.

**Abstract:** This research compares the potential environmental impacts of heat pumps with gas boilers and scenario analysis through utilising the life cycle approach. The study analyses the current situation with the baseline model and assesses future applications with Circular Economy (CE), Resource Efficiency (RE) and Limited Growth (LG) scenarios. Then, hybrid applications of lowcarbon technologies and different manufacturing scenarios are investigated according to baseline and CE scenarios. Our results show that the use and manufacturing phases are responsible for 74% and 14% of all environmental impacts on average as expected. Even though the electricity mix of the UK has decarbonised substantially during the last decade, heat pumps still have higher lifetime impacts than gas boilers in all environmental categories except climate change impact. The carbon intensity of heat pumps is much lower than gas boilers with 0.111 and 0.097 kg CO2e for air source heat pumps and ground source heat pumps, whereas the boiler stands as 0.241 kg CO2e. Future scenarios offer significant reductions in most of the impact categories. The CE scenario has the highest potential with a 44% reduction for heat pumps and 27% for gas boilers on average. RE and LG scenarios have smaller potential than the CE scenario, relatively. However, several categories expect an increase in future scenarios such as freshwater ecotoxicity, marine ecotoxicity and metal depletion categories. High deployment of offshore wind farms will have a negative impact on these categories; therefore, a comprehensive approach through a market introduction programme should be provided at the beginning before shifting from one technology to another. The 50% Hybrid scenario results expect a reduction of 24% and 20% on average for ASHP and GSHP, respectively, in the baseline model. The reduction is much lower in the CE scenario, with only a 2% decrease for both heat pumps because of the reduction in heat demand in the future. These results emphasise that even though the importance of the use phase is significant in the baseline model, the remaining phases will play an important role to achieve Net-Zero targets in the future.

**Keywords:** built environment; circular economy; gas boilers; heat pumps; life cycle assessment

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

In 2018, 55% of the global population lived in cities, and this is expected to reach 68% by 2050 [1]. Cities are globally responsible for 75% of primary energy consumption [2] and 60–80% of greenhouse gas emissions [3]. Of all industrial sectors, the built environment is responsible for 36% of global energy consumption and 39% of energy-related greenhouse gas (GHG) emissions [4]. Use emissions such as heating, cooling, lighting, and cooking account for 72% of these emissions and the remaining comes from embodied emissions. Building-related emissions have decreased by 13% since 2013 and are around 20% below 1990 levels in the UK [5].

**Citation:** Sevindik, S.; Spataru, C.; Domenech Aparisi, T.; Bleischwitz, R. A Comparative Environmental Assessment of Heat Pumps and Gas Boilers towards a Circular Economy in the UK. *Energies* **2021**, *14*, 3027. https://doi.org/10.3390/en14113027

Academic Editor: Anna Mazzi

Received: 1 April 2021 Accepted: 14 May 2021 Published: 24 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

3

Heating is responsible for nearly half of UK energy usage and a third of carbon emissions, and currently 70% of heating purposes provided by natural gas [6]; therefore, electrification of the heating scheme is crucial via low-carbon technologies. In 2017, The Clean Growth Strategy was introduced in the UK, which was preceded by The Climate Change Act in 2008. The main proposals for the strategy aim to reduce UK emissions through energy efficiency in industry and housing, low-carbon transportation, clean power generation and enhancing natural resources, which account for 38%, 24%, 21% and 15% of UK emissions, respectively [6].

Efficiency improvements in buildings have been the major strategy during the last half-century. Very low heat conductivity in the building fabric is provided with low U-value insulation materials through passive strategies. Thermal mass also became a common topic in order to reduce heating and cooling loads and shifting peak demand. It is possible to limit the indoor temperature fluctuation to ±4 ◦C with a heavyweight construction which helps to reduce discomfort in buildings [7]. On the other hand, ambitious building regulations have created a demand for low-exergy heating systems such as heat pumps. It is possible to reach energy and GHG emissions saving with a low-temperature hydronic heating system, even though it is a gas boiler. However, heat pumps maximise the saving as their efficiencies are higher in low temperatures [8].

A heat pump is a low-carbon technology that exploits heat from air, ground or water sources by heat transfer and provides heating, cooling, and domestic hot water (DHW). It could utilise electricity, mechanical or thermal energy in various applications such as residential, commercial, industrial or district heating. An electricity-driven heat pump could provide a three to four times higher amount of heat than the electricity consumed; therefore, it is expected to play a significant role in the decarbonisation of heating in buildings [9].

Heat pumps offer higher efficiencies than gas boilers; however, various refrigerants perform differently in various evaporator and condenser temperatures; therefore, choosing the right refrigerant according to system description and temperature requirements is crucial [10]. Current heat pumps in the UK market utilise R410A refrigerant, which has high global warming potential (GWP 2088); however, the use of R134a (GWP 1300) and R32 (GWP675) has also been increasing [11]. The number of studies investigating natural refrigerants such as ammonia (GWP 0.1) has increased, and the results show that ammonia could be used as a refrigerant in both large and small applications; however, the cost of the system is more expensive than traditional ones [12]. Therefore, more support is needed from the government in order to introduce the system to the market.

Energy efficiency through low-carbon heating is one of the key policies requiring the improvement of the standards of 1.2 million new boilers installed each year in England, including the installations of control devices to save energy. Moreover, reforming the Renewable Heat Incentive (RHI) and spending GBP 4.5 billion to support low-carbon heating technologies is expected between 2016 and 2021. GBP 184 million of investment has been scheduled for innovations in energy efficiency and low-carbon heating options [6]. However, the UK Government was planning to replace RHI according to the new Net-Zero target for 2050; therefore, RHI is extended until March 2022 and consultation for a new support scheme has been introduced [13]. It aims to support energy efficiency and low-carbon heating in buildings with a GBP 9bn investment during the next ten years.

The total share of renewables in heating and cooling has been increasing during the last decade in the UK; however, it was still only 7.5% in 2017 and the UK was at the end of the list among the EU member states with the Netherlands (5.9%) and Ireland (6.9%) [14]. The total number of heat pumps reached 9.5 million in the EU, which represented 4% of the building stock and the capacity of 82.7 GW [15]. The highest number of heat pumps sold in 2017 in the EU was for France (240,000 units), while only 20,000 units were sold in the UK. According to the Climate Change Committee [16], this number should have been 30,000 in 2020 and much more ambitious long-term targets should be set by the government. The number of heat pumps sold in the UK is still low and the UK Government have plans for

not only single heat pump applications but also hybrid use with gas boilers [17]. The CCC also suggested that new homes should not be connected to the gas grid after 2025 and hybrid applications of heat pumps should start and reach 10 million by 2035 [18].

Domestic RHI was introduced in 2014 and, according to Ofgem, in 5 years, 55,000 domestic heat pumps have been deployed [19]. According to the CCC (CCC, 2018), at least 2.5 million heat pumps need to be deployed by 2030 in order to continue further progress of decarbonisation [20]. New residential applications are the majority of the heat pump market, which was around 10,500 units per year. However, this represents a small proportion when it is compared with the number of new residential units completed, which is around 175,000 per year approximately [15]. Even though current numbers are quite limited for now, more incentives and advances in the manufacturing process could help to increase the deployment rate. The UK Government's ten-point Industrial Revolution plan aims to have 600,000 heat pump installations per year by 2028 [21]. Moreover, the UK Government started a consultation in 2019 for future home standards to upgrade Part L and Part F of the building regulations [22]. This consultation was concluded in January 2021 and proposed a timetable for the implementation of future home standards. A total of 70% of respondents to this consultation believe that heat pumps will play a significant role in this standard, and there is already support from stakeholders [23]. According to the UK Government, future buildings should have 75–80% fewer CO<sup>2</sup> emissions than current built ones with these standards [24]. The RIBA Council introduced a challenge for designers, architects and industry to reduce operational energy demand, embodied carbon and water use through higher benchmarks for buildings [25]. As space heating plays a significant role in operational energy and carbon emissions, the importance of heat pumps as a low-carbon technology becomes more crucial to reach these benchmarks. According to a study conducted by the Department of Energy and Climate Change, in a mass-market scenario, cost reductions of around 18% are expected compared to current costs for Ground Source Heat Pumps (GSHP) and 20% for Air Source Heat Pumps (ASHP) [26,27], which could lead to a higher deployment rate in the future. However, reuse and recycle options of these systems should be considered before moving to mass deployment. Therefore, there is a need for harmonisation of current built environment theory with the theory underpinning CE in order to achieve circular chains.

This study extends the analysis presented in two conferences [28,29]. The aim of this research is a comparison of different environmental impact categories for key technologies to decarbonise heating in domestic buildings in the UK. Heat pumps and gas boilers are key technologies in the decarbonisation of buildings and have been selected as a relevant case to test our hypotheses and methods. Their impacts on low-carbon heating targets have been assessed through a Life Cycle Assessment (LCA) analysis for the current year, and future scenarios have been developed to assess their environmental impacts through LCA to understand the impacts of the replacement of existing technologies with new ones. The functional unit of the study is decided as 'generating 1 kWh of thermal energy for domestic heating', but cumulative results have also been presented to investigate lifetime environmental burdens associated with these heating technologies. Hybrid applications of heat pumps with gas boilers also assessed as hybrid technologies will play a significant role in the future according to government targets.

#### **2. Methods**

Life Cycle Assessment (LCA) is an analytical tool to assess the environmental impacts of a product or process through analysing the entire life cycle (raw material acquisition, production, use and disposal phases). Its aim was to reduce cost while improving performance; therefore, it has been widely used during the last couple of decades [30]. A Life Cycle Assessment (LCA) approach has been undertaken to evaluate the environmental impacts of low-carbon heating technologies for the domestic sector in accordance with ISO 14,040 and ISO 14,044 standards [31,32]. The analysis has four stages: (i) defining goal and scope to identify the purpose of the study and products; (ii) inventory analysis to collect data of the

unit processes of products and analyse; (iii) impact assessment to identify environmental impacts; and (iv) interpretation to evaluate results and compare with potential solutions. The first step of this study focuses on the current situation of heating technologies, then, future scenarios try to evaluate their impact according to government plans and targets.

#### *2.1. Goal and Scope*

The goal of this study is to evaluate the potential environmental impacts of residential space heating in the UK through developing life cycle models for an air-source heat pump (ASHP), ground-source heat pump (GSHP) and natural gas boilers (NGB). This comprises a scenario analysis with the objective of achieving the Net-Zero target by 2050.

The functional unit of the study is decided as 'generating 1 kWh of thermal energy for domestic heating'. However, cumulative results have also been presented to investigate lifetime environmental burdens associated with these heating technologies. The LCA software SimaPro 8.0.3 [33] has been used to model the products and the ReCiPe Midpoint (H) method [34] has been used to calculate environmental loads.

#### *2.2. Inventory Data and Assumptions*

#### 2.2.1. System Description and Boundary

System specification and material requirements of heat pumps and gas boilers and data for these products have been taken from a previous study [35] that analysed the environmental implications of these products in the UK. Heat pumps are decided as air to water and ground to water, and heating is provided by underfloor heating. The underfloor heating system is designed as a screed system covering 150 m<sup>2</sup> area. Material requirements of heat pumps and gas boilers are illustrated (Table 1). The capacity of the systems and operation period have been assumed as 10 kW and 2000 h/year. The total space heating demand was assumed to be 20,000 kWh/year for both heat pumps and gas boilers, which represents an average UK household yearly heating demand. All technologies are considered maintenance free; however, it is assumed that the refrigerant needs to be topped up 6% yearly as losses occur. The total lifetime of both heat pumps and gas boilers was assumed as 20 years.


**Table 1.** Material requirements for heating technologies. Data source: [35].

The system boundary of gas boilers and heat pumps includes extraction and production of raw materials, transportation of raw materials for assembly, manufacturing of

heat pumps and gas boilers, manufacturing of underfloor heating system for heat pumps, manufacturing of heat collector for GSHP, transportation of products to the distributor, transportation of products to the installation site, installation of GSHP as it requires drilling, operation period which includes natural gas processing for boilers and electricity generation for heat pumps, maintenance of refrigerant for heat pumps and disposal of materials (reuse, recycling, landfilling, etc.) (Table 2). The installation phase is only considered for GSHP as it requires drilling, which is an extensive installation when compared with ASHP and gas boilers. As two types of heat collector exist for GSHP (horizontal and vertical), this study only included the horizontal one for simplicity. The difference between the two types is the amount of pipework for heat collectors, the heat carrier liquid and the type of machines to dig the ground. The maintenance stage is only considered for heat pumps as there will be losses in refrigerant during the operation period; therefore, annual top-up is required. Additionally, the underfloor heating system is only included for heat pumps as replacing the gas boiler will require either resizing the radiators or the installation of an underfloor heating system to achieve higher efficiency. Therefore, in this study, the underfloor heating system is included in the system boundary of heat pumps. However, renewing the gas boiler does not require any system change; therefore, no new heating system is proposed.



#### 2.2.2. Transport

Heat pump installations in the UK market heavily rely on imports. A total of 69% of ASHP and 59% of GSHP are manufactured outside of the UK [13]. Europe is the dominant market as 70% of imported products are manufactured there. When individual countries are investigated, Sweden has the highest imported heat pump amount followed by South Korea, Spain, Italy, Czech Republic, and Germany. This study, therefore, selects Europe as the manufacturing location for heat pumps. Ecoinvent generic values (100–200 km) have been used for raw materials and assembly transport assumptions [36]. Heat pumps are assumed to be manufactured in Europe and transported to the UK (Table 3). Within this process, raw materials are transported 200 km by railway and 100 km by a large truck (16–32 tonne). After the assembly of the heat pump, it is transported to the distributor 500 km by railway and 200 km by a large truck (16–32 tonne). Then, the installation site distance has been assumed as 200 km and the products have been transported by a small truck (3.5–7.5 tonne). The underfloor heating system (UHS) and heat collectors (HC) are assumed to be manufactured in the UK; therefore, transport distances for manufacturing have been assumed as 200 km by railway and 100 km by a large truck (16–32 tonne), and installation distance has been assumed as 200 km. Natural gas boilers are assumed to be manufactured in the UK; therefore, transport for raw materials has been assumed as 200 km by railway and 100 km by a large truck (16–32 tonne). Distances from manufacturer to distributor and installation site have been assumed as 200 km.


**Table 3.** Transport assumptions. Data source: [36].

#### *2.3. Scenario Analysis*

The study offers a scenario analysis to assess the environmental impacts of heat pumps and gas boilers through LCA to understand the implications of the replacement of existing technologies with new ones. In this section, three scenarios have been developed for the year 2050. In the next section (Section 2.4), three more alternative scenarios have been developed for hybrid applications of technologies and another transport scenario. The latter is separated from the first three because they are assessed according to both the baseline model and also the Circular Economy (CE) scenario.

Circular Economy (CE) scenario: High technology development and high consumer engagement are supported by policies; therefore, more efficient houses and low-carbon technologies expect a reduction in energy demand. The decarbonisation of electricity is provided by increased offshore wind capacity, and the share of natural gas is nearly eliminated. Larger roles for heat pumps are provided and gas boilers are replaced with lowcarbon technologies such as stand-alone heat pumps (10.7 million) or hybrid heat pumps (7.1 million). The number of gas boilers will reduce to 5 million and the remaining heating demand will be provided by district heating and biomass. A reduction in material demand and better waste treatment options are assumed with high policy support (Table 4).

**Table 4.** Summary of system specifications and assumptions for scenarios.


Resource Efficiency (RE) scenario: A reduction in energy demand is expected but this decrease is lower than the CE scenario. The decarbonisation level of electricity is similar to the CE scenario. The deployment of heat pumps is limited (8.5 million), and the number of gas boilers is similar to the CE scenario; therefore, applications of hydrogen play a significant role in this scenario. High technology development and policy support are expected, but consumer support is relatively limited compared to the CE scenario.

Limited Growth (LG) scenario: Limited energy efficiency and technology development is assumed; therefore, residential heat demand is expected to reduce with the lowest number among other scenarios. The decarbonisation of electricity is not finished, and the deployment of heat pumps is very limited; therefore, the majority of heating demand will be provided by gas boilers. Slow adaptation to circular economy principles and low consumer engagement are expected.

#### 2.3.1. Electricity Mix

The use phase of heating technologies has a significant impact on LCA analysis, accounting for 74% of overall impacts; therefore, updating the electricity mix of the UK to the current situation would help to see more accurate results. The current electricity mix of the UK for the year 2018 has been identified for the use phase of heat pumps (Figure 1) [41]. In 2018, 40.2% of electricity was produced from natural gas. Nuclear and wind accounted for 19.9% and 17.4%, respectively. The remaining comes from bioenergy, solar and coal.

According to National Grid, more than one-third of UK electricity was produced from natural gas and offshore wind capacity, around 10 GW, in 2018 [19]; however, more deployment of wind energy is expected in the future. The UK Government has revised its offshore wind capacity target from 30 GW to 40 GW by 2030 [42]. Therefore, National Grid's electricity mix scenarios have been adopted for the UK's future electricity mix ('Community Renewables', 'Two Degrees', and 'Steady Progression' scenarios adapted to 'Circular Economy', 'Resource Efficiency' and 'Limited Growth' scenarios, respectively) (Figure 1). All three scenarios assume a significant increase in wind energy but in different shares. In 2050, the RE scenario assumes that 56% of electricity will be produced from wind energy and the remaining will come from nuclear, solar and bioenergy, which account for 19%, 8% and 7%, respectively. In the CE scenario, wind energy reaches 60% of total electricity production. Solar and bioenergy increase to 10% each; therefore, the share of nuclear energy reduces to 12%. The LG scenario, however, assumes the least wind energy share with 53%; therefore, natural gas still has a share of 10% of total electricity production.

#### 2.3.2. Efficiency of Technologies

One of the main impacts on energy demand in heat pumps is the Coefficient of Performance (COP), which identifies the ratio of energy needed according to its efficiency. Seasonal Performance Factors (SPFs) represent the average COP for heat pumps during the heating season. According to the Department for Business, Energy and Industrial Strategy (BEIS) monthly reports since 2014, average ASHP and GSHP efficiencies are calculated as 3.2 SPF and 3.5 SPF [39]. These values vary for legacy applications and new installations. Legacy ASHP applications have an average of 2.5 SPF and new installations have 3.4 SPF. Legacy GSHP installations, on the other hand, have an average of 2.9 SPF and the new ones have 3.8 SPF. Field trials in the UK and Europe show similar results with an average SPF of 2.6 and 3.2 for ASHP and GSHP, respectively [17]. Therefore, average SPFs for heat pumps have been considered as 2.8 for ASHP and 3.4 for GSHP. The efficiency of NGB is considered as 90% for the baseline model (Table 4).

Current heat pump efficiencies vary between manufacturer test data and field trials. Correct sizing and better installation of heat pumps provide higher efficiencies; thus, it could be possible to reach manufacturers' test efficiencies in the future. Over the years, the efficiency of heat pumps is expected to increase with the help of advances in the market. The CCC assumes a 0.5 increase in the COP of heat pumps between 2020 and 2030 [40]. Therefore, future scenarios in this study assume higher efficiencies varying between 3.6–4.2 for ASHP and 4.2–4.6 for GSHP (Table 4). GSHPs are expected to have a lower increase in COP than ASHPs due to their high outlet temperature and modest difference in the ground temperature around the heat collector [43]. Therefore, efficiency improvement in GSHP is expected to be lower than in ASHP.

**Figure 1.** Electricity mix of UK in different scenarios used in LCA analysis. Data sources: [19,41].

#### 2.3.3. Decommissioning

The lifetime of both technologies has been assumed to be 20 years, and at the end of their life cycle metal components are recycled and the rest is landfilled. UK and Europe recycling rates have been reviewed, and steel, copper, aluminium and plastics have been assumed as 75%, 61%, 69% and 32% recycled [37]. A total of 80% of refrigerants are assumed to be reused after 20% losses during the decommissioning [35].

Gas boilers and heat pumps are electrical and electronic equipment (EEE) covered by the WEEE Regulations under category 1 and category 12. They both have similar targets as 85% recovery and 80% recycling rates [38]. However, these benchmarks will likely increase if the UK continues to progress towards Net-Zero targets. Therefore, all scenarios expect an increase in the recycling rate; however, the CE scenario assumes the highest recycling rates with 100% for all components. High policy support and public engagement could help to achieve 100% recycle and recovery options.

#### 2.3.4. Efficiency Improvements in Residential Sector

The need for space heating could be less in the future. Thermal efficiency improvements through retrofitting existing houses and setting higher benchmarks for new buildings mean that by 2050, domestic buildings could use up to 26 per cent less energy for heat compared to today [19]. The CCC [20] assumes a 15% reduction in energy consumption in the residential sector by 2030 through energy efficiency improvements in existing buildings. The Royal Institute of British Architects [27] has set a challenge for designers to reach at least a 75% reduction in operational energy in domestic buildings by 2030. Therefore, different measures have been taken for future scenarios. RE and CE scenarios assume a 15% and 25% reduction in heat demand in an average household. The LG scenario, however, does not consider any energy improvement measures as the economy faces limited economic growth (Table 4).

#### *2.4. Hybrid and Transport Scenarios*

In the previous section, model simulations are conducted based on individual heating technologies without focusing on hybrid applications. However, the UK Government and National Grid have decarbonisation targets for heating, and scenarios show that there will be a need for hybrid options in the future. Additionally, Asia is a dominant market, and some companies manufacture their heat pumps in Asian countries. Moreover, South Korea is the second country that the UK has the highest heat pump imports from [13]. Therefore,

this section investigates the impact of changing the manufacturing location and hybrid options according to the baseline scenario and CE scenario, which was modelled in the previous section. Three scenarios are investigated as:



**Table 5.** Transport assumptions of manufacturing in Europe and Asia [36].

The changes for these three scenarios are applied to both the baseline and the CE model to compare the impacts of these scenarios in the current year and an alternative of the year 2050.

#### **3. Results**

#### *3.1. Baseline Results*

The simulation results have been illustrated per functional unit, and the lifetime results are divided into the amount of total space heating demand for both heat pumps and gas boilers as the functional unit is decided as 'generating 1 kWh of thermal energy for domestic heating'. However, lifetime environmental impacts are also provided in the graphs to show the impact of technologies during their lifetime.

Environmental impacts of the baseline scenario for air source heat pump (ASHP), ground source heat pump (GSHP) and natural gas boiler (NGB) have been illustrated in Figure 2. ASHP has the highest impacts on average, and GSHP and NGB have 17% and 51% lower results than ASHP on average, respectively. When individual impact categories were investigated, the results illustrated that NGB has the lowest impact in all categories except Climate Change (CC)—(CC (Climate Change), OD (Ozone Depletion), TA (Terrestrial Acidification), FEU (Freshwater Eutrophication), MEU (Marine Eutrophication), HT (Human Toxicity), POF (Photochemical Oxidant Formation), PMF (Particulate Matter Formation), TE (Terrestrial Ecotoxicity), FE (Freshwater Ecotoxicity), ME (Marine Ecotoxicity), IR (Ionising Radiation), ALO (Agricultural Land Occupation), ULO (Urban Land Occupation), NLT (National Land Transformation), WD (Water Depletion), MD (Metal Depletion), FD (Fossil Depletion))—and Fossil Depletion (FD) categories.

**Figure 2.** Lifecycle environmental impacts of heat pumps and gas boilers for baseline scenario (HP: Heat pump, NGB: Gas boiler, UHS: Underfloor heating system, HC: Heat collector).

This study illustrates that emissions for ASHP and GSHP are reduced to 0.111 kg CO2e/kWh and 0.097 kg CO2e/kWh (Figure 2), respectively, compared with the literature [35], where there was a reduction from 0.276 kg CO2e/kWh and 0.189 kg CO2e/kWh. This is mainly because of the decarbonisation of the electricity mix through the high deployment of wind energy to replace coal and some part of natural gas during the last decade. The carbon intensity of the gas boiler is more than double both heat pumps with 0.241 kg CO2e/kWh. NGB has 96.2 t CO2e lifetime emissions, much higher than ASHP (42.3 t CO2e) and GSHP (38.8 t CO2e).

The two highest contributor phases of life cycle analysis are the 'use' and 'manufacturing' phases, which are responsible for 74% and 14% of all environmental impacts on average. The manufacturing of heating technologies, underfloor heating systems and heat collector phases accounts for 17%, 20% and 12% for ASHP, GSHP and NGB, respectively. It is important to keep in mind that the manufacturing of heat pumps occurs outside of the UK, which does not have an impact on the UK's territorial emissions; however, it will have an impact on consumption-based emissions of the UK or global emissions. The disposal phase accounts for 6%, 7% and 3% of total impacts for ASHP, GSHP and NGB, respectively; however, these impacts are negative due to contributions from the reuse of refrigerants and recycling of materials at the end of their life cycle. The refrigerant and maintenance phases account for only 3% for both heat pumps and no impact for the gas boiler as there is no refrigerant use in boilers. The transport phase, on the other hand, is only responsible for 1% of total environmental impacts.

When heat pumps are compared, GSHP has 17% lower results than ASHP as it requires less electricity because of its higher efficiency. The impact of heat collectors is relatively low in most of the categories, except the Terrestrial Acidification (TA), Photochemical Oxidant Formation (POF) and Particulate Matter Formation (PMF) categories. The reduction in the use phase in these categories is higher than the impact of manufacturing the heat collectors, so overall the environmental impact of GSHP remains lower than ASHP. The POF category is the only category in which GSHP has 3% higher results than ASHP because the impact of the manufacturing of heat collectors is greater than the reduction in the use phase. The highest difference between heat pumps occurs in the Ozone Depletion (OD) category with 36% because of lower refrigerant requirements. Metal Depletion (MD), Freshwater Eutrophication (FEU), Marine Eutrophication (MEU), Human Toxicity (HT) and Freshwater Ecotoxicity (FE) are the remaining categories that have more than 20% difference. Even though the disposal phase does not have a significant impact overall, there are several categories in which the disposal phase has higher impacts for heat pumps, such as TA, POF, PMF and ULO categories, accounting for 29%, 18%, 35%, and 22%, respectively.

#### *3.2. Results from Future Scenarios*

Scenario analysis aims to investigate the impact of changes planned in line with the government's targets and national policies. The Circular Economy (CE) scenario results expect the highest reductions for all heating technologies, and the Limited Growth (LG) scenario expects the lowest. Overall reductions in CE, RE and LG scenarios are 44%, 42% and 31% for heat pumps and 27%, 18% and 12% for the gas boiler (Figure 3).

**Figure 3.** Lifecycle environmental impact change of future scenarios according to the baseline scenario.

In the CE scenario, the highest changes are in CC, TA, POF, PMF, NLT and FD categories with an average of 75% reduction in heat pumps. The lowest change occurs in the OD category with a 2% reduction only as the amount of refrigerant is the same in future scenarios. Even though the RE and LG scenario have lower results than the CE scenario, trends are the same. However, several categories expect an increase for all scenarios such as FE, ME, and MD. The main source of this impact is the heavy metals utilised in the high deployment of wind energy that will be provided by offshore wind farms; therefore, emissions to the water will be expected. Another toxicity category, human toxicity, also expects a lower reduction for all scenarios from 8% to 14% for heat pumps. Additionally, the major source of metal depletion comes from the life cycle of electricity because the high deployment of renewables requires more metal resources. On the other hand, there are several categories in which the RE scenario performs better than the CE scenario, such as MEU, TE, FE, ME and ALO. The main reason for this impact is that the CE scenario has the highest renewable share in the electricity mix and this has higher toxicity and land occupation results; however, the RE scenario has a lower renewable share and higher nuclear energy in the electricity mix. Therefore, negative impacts created by renewable energy are greater in the CE scenario. The LG scenario still has natural gas in the mix; therefore, the LG scenario still performs worse than both scenarios.

The reductions in NGB are very limited when compared with heat pumps. This is due to limited efficiency in the gas boiler. The reductions come from efficiency improvements in houses which will require less heat demand; therefore, the gas boiler expects similar reductions in all phases.

When the contributors to changes in future scenarios are investigated, only the use and disposal phases have an impact on categories. Figure 4 shows their weighted results, illustrating the importance of the use phase. Even though some categories are expecting significant increases in the disposal phase (ranging from 535–1286%), their weighted results are less than 1% when they are compared with the use phase. The highest disposal phase impact occurs in the OD category, with an increase of 20% and 9% for CE and RE scenarios and a decrease of 3% for the LG scenario.

#### *3.3. Transport and Hybrid Scenarios*

#### 3.3.1. Results from Baseline Model

The Transport (SK) scenario results illustrate that changing the manufacturing location does not have a significant impact on most categories according to the baseline scenario. ASHP results show that even though the average change is less than 1%, there are some categories that have higher results (Figure 5). The highest impact category is MEU with a 30% decrease from the baseline scenario. TA, HT and PMF categories are other high impact categories with 13%, 11% and 8%, whereas with an increase, unlike the MEU category.

During life cycle phase analysis, only changes in the manufacturing of heat pumps, refrigerant and transport phases were considered. The manufacturing phase increases with an average of 27% in all categories, and the highest change occurs in the TE category with a 358% increase for ASHP (Figure 6). TA and PMF categories show increases of 226% and 58%, respectively. There are also several categories with negative impacts such as MEU and OD categories with a 92% and 19% decrease, respectively. The transport phase, on the other hand, increases 17% on average in all categories and the highest contribution comes from TA, PMF, MEU and POF categories with 77%, 49%, 40% and 39%, respectively. The refrigerant phase, however, has a negative impact, and results decrease only 2% on average and the highest change occurs in IR, NLT and TE categories with a decrease of 18%, 8% and 7%, respectively. PMF and TA categories have also seen a 4% increase.

**Figure 4.** Lifecycle environmental impact change of phases in future scenarios.

**Figure 5.** Lifecycle environmental impact change of Transport (SK) and Hybrid scenarios according to baseline scenario.

**Figure 6.** Lifecycle environmental impact change of phases for Transport (SK) and Hybrid scenarios according to baseline.

The results of GSHP show similarities with ASHP, with a decrease of 1% on average (Figure 5). The highest impact category is MEU with a 23% decrease, followed by a 9% and 8% increase in TA and HT categories, respectively. The changes in GSHP are relatively lower than ASHP as heat collectors in GSHP will still be manufactured in Europe in this scenario; therefore, the weight of the change becomes smaller in this technology.

The results of phases are also similar in manufacturing and refrigerants with a 26% increase and 2% decrease on average for GSHP (Figure 6). The highest impact categories are TE, TA, PMF and MEU categories in the manufacturing phase, and IR, TE, NLT, PMF and ULO categories in the refrigerant phase, like the ASHP results. The main difference between ASHP and GSHP occurs in the transport phase and the average change is 7%. Even though the highest categories are the same, the changes are less than ASHP.

The 50% Hybrid scenario results expect an increase of 32% and 20% on average in ASHP and GSHP, respectively (Figure 5). GSHP offers a lower increase or less reduction in all categories, resulting in fewer advantages than ASHP. The highest change occurs in CC and FD categories with a 76% and 79% increase for ASHP, and 97% and 89% increase for GSHP, respectively. The MD category also expects an increase of 3% and 7% for heat pumps. The remaining categories result in a decrease, and the highest decrease occurs in TE, IR, ALO, NLT, and WD categories, varying between 49% and 38% for both heat pumps. Some categories have a less than 5% impact change, such as OD, FEU and HT categories.

In the 50% Hybrid scenario, the highest changes occur in the disposal phase with an average of 15% and 200% decrease for ASHP and GSHP (Figure 6). Even though the overall change is greater in GSHP, most of the contribution comes from MD and NLT categories with a decrease of 2951% and 531%. The reason for this reduction is that the amount of metals required for ASHP is greater than GSHP; therefore, this value is a positive value for ASHP. Thus, negative metal depletion values coming from NGB reduce the impact of ASHP. When other phases are analysed, the use phase expects a decrease of 33% and 29% for ASHP and GSHP, and the manufacturing phase expects an increase of 33% and 53%, respectively. The transport phase has an average change of 5% increase for both heat pumps.

Even though the use phase offers a reduction in all categories, the CC and FD categories expect an increase in all phases except the disposal phase. As gas boilers perform worse than heat pumps only in this category in the baseline scenario (Figure 2), the hybrid scenario offers the worst results in these categories. Moreover, the MD category also expects an increase even though it is less than 10%. However, in other categories, the use phase eliminates the increases created by manufacturing and transport phases as the weight of the use phase is very large and creates negative results overall in all categories.

The 75% Hybrid scenario results offer less reduction than the half-hybrid scenario with an 11% and 9% decrease in ASHP and GSHP (Figure 5). Similarly, GSHP performs worse than ASHP in this scenario with an increase in CC and FD categories and a decrease in other categories; however, this scenario offers less decrease overall as the contribution of the gas boiler is less than the 50% Hybrid scenario. The highest changes occur in CC and FD categories with a 38% and 37% increase for ASHP, and 49% and 45% increase for GSHP, respectively. The highest decreases occur in TE, IR, ALO, and NLT categories, varying between 24% and 19% for both heat pumps.

#### 3.3.2. Results from CE 2050 Model

The Transport (SK) scenario results show that changing the manufacturing location could increase the environmental impacts on average 3% and 1% for ASHP and GSHP, respectively, according to the CE 2050 model (Figure 7). The highest changes for ASHP occur in TA and PMF with a 68% and 34% increase. Additionally, results suggest a decrease in several categories with less than 3% except the MEU category, which has a 53% reduction in the CE 2050 model. GSHP results show lower values than ASHP in all categories, but the highest contributors are the same impact categories.

**Figure 7.** Lifecycle environmental impact change of Transport (SK) and Hybrid scenarios according to CE scenario.

The life cycle phase results illustrate that the highest contributor phases to the changes from the CE 2050 model are the manufacturing of heat pumps, refrigerant, and transport phases, similar to the baseline model (Figure 8). The results of changes in these phases are the same with the baseline model; therefore, the changes in these phases have the same impacts in both the baseline and CE 2050 model.

**Figure 8.** Lifecycle environmental impact change of phases for Transport (SK) and Hybrid scenarios according to CE scenario.

Even though hybrid scenarios in the CE 2050 model have similar results as the baseline model in most of the categories, there is a significant difference in CC and FD categories as they are very sensitive to the use phase results. In the 50% Hybrid scenario, the highest changes occur in the FD category with a 490% and 333% increase for ASHP and GSHP, similar to the baseline model (Figure 7). The other category suggesting an increase is CC with 409% and 360% for both heat pumps. The impact of the MD category is lower than the baseline model in the CE model. Most of the remaining categories have a reduction of around 16–47%.

The results of phases in the 50% Hybrid scenario illustrate that the highest changes occur in the manufacturing phase, with a 33% and 53% increase on average for both heat pumps (Figure 8). The transport phase creates an increase of 5% and 6% for ASHP and GSHP, respectively. The disposal phase, on the other hand, expects a decrease of 4% for ASHP and an increase of 8% for GSHP. However, the use phase suggests a decrease of around 5% on average for both heat pumps. Similar to the baseline model, the use phase offers a reduction in all categories and an increase for CC and FD categories with 482% and 563% for ASHP, and 533% and 622% for GSHP, respectively. The only exception is for the POF category, which was expecting a reduction in the baseline model but expecting an increase in the CE 2050 model. The main reason for this is that the result of NGB for this category is lower than heat pumps in the baseline model; however, in the CE 2050 model, NGB has a higher value and increases the average of hybrid results.

TA, FEU, and PMF categories have a reduction varying between 9% and 18%, whereas the remaining categories expect higher reductions varying between 30% and 48%. In the CE 2050 model, hybrid scenarios offer an overall increase in contrast to the baseline model mainly because the change in the CC category is greater than the baseline model and the weight of the use phase is lower in the CE 2050 model.

Similar to the baseline model, the 75% Hybrid scenario results offer less increase than the half-hybrid scenario with a 4% and 10% increase overall in ASHP and GSHP, respectively. The highest change occurs in CC and FD categories with a 205% and 246% increase for ASHP, and 181% and 167% increase for GSHP, respectively. The highest decreases occur in TE, IR, ALO and WD categories, varying between 19% and 22% for both heat pumps.

The changes in manufacturing, transport and disposal phases are similar to the baseline model in both hybrid scenarios, so there is no difference between the baseline and CE model and 50% and 75% Hybrid scenarios in these phases, except the use phase.

The results of hybrid scenarios offer a benefit to reduce the negative impacts caused by heat pumps in most of the categories. Even though this creates an increase in CC and FD categories and GHG emissions, negative consequences could be prevented. Moreover, replacing gas boilers with heat pumps requires a transition period, and hybrid applications could help to create a smoother transition.

#### *3.4. Data Quality and Limitations*

In order to validate the study, results are compared with the adopted study [35]. Impact categories vary between different calculation methods, but several impact categories are common in most of the studies so only these categories are compared. The CC impact result of ASHP is 0.225 kg CO2e/kWh in the baseline model, which is 18% lower than the adopted study (0.276 kg CO2e/kWh). The GSHP result is 0.168 kg CO2e/kWh for the baseline model and the result from the adopted study is 0.189 kg CO2e/kWh, which is lower around 11%. The OD category of the adopted study was 0.3 mg R11eq, which is 2% higher than this study (0.294 mg CFC-11eq). Additionally, TA category results for ASHP and GSHP were 0.86 and 0.59 g SO2eq, which is 2% and 8% lower than this study's results, respectively (0.842 and 0.638 g SO2eq). FEU and HT categories have higher differences that vary between 20% and 47%. The major reason causing these differences is the different methodology used for the models. This study used ReCiPe Midpoint (H) methodology;

however, the adopted study used CML 2 Baseline 2001 methodology. Moreover, the adopted study used GaBi software, and this study used SimaPro software.

The limitation of the Transport (SK) scenario is that even though South Korea is used as a manufacturing location, rest-of-the-world (RoW) data for production assumptions and input data have been used in SimaPro due to the lack of data availability. Transport simulations are specific to South Korea; however, manufacturing data are not specific.

The impacts of the electricity mix, heat demand, efficiencies of technologies, lifetime of the products and disposal phase have been assessed for a sensitivity analysis. The parameters have been decided as:


The results of sensitivity analysis indicate that electricity use has a significant impact on heat pump results. Doubling the renewable share in the electricity mix creates positive and negative impacts in several categories for ASHP (Figure 9). The highest influences occur on IR, NLT, FD, and CC categories with a decrease of around 41%, 41%, 40% and 34%, respectively. However, it could increase the results of TE, ALO, WD, FE, MEU and ME categories with 97%, 95%, 76%, 52%, 42% and 42%. The renewable share has no impact on NGB as it uses natural gas only.

A 50% increase in SPF creates an average of 29% reduction overall, and the highest changes occur in TE and ALO categories, accounting for 70% and 50%, respectively. The remaining categories expect a reduction range from 8% to 39%. Increasing boiler efficiency from 90% to 95% reduces all impact categories by an average of 4%.

A 25% reduction in demand has both negative and positive impacts on categories. Even though the lifetime results expect a reduction in this analysis, functional unit results fluctuate as the lifetime results are divided into heat demand, which is 25% reduced. Therefore, some categories react differently in lifetime and functional unit results. The highest changes occur in TE and ALO categories, similar to SPF improvements for heat pumps. A similar issue occurs for the gas boiler and creates an increase of 4% overall, even though lifetime results are reduced.

Increasing the lifetime of products to 25 years increases the lifetime impact results as expected, with an increase of an average 16%, 15% and 22% for ASHP, GSHP and NGB. However, functional unit results expect a decrease of 7%, 8% and 2% for the technologies, respectively.

A 25% increase in the recycling rates of materials also has a significant impact in several categories for heat pumps such as TE, MEU and ALO categories, with a reduction of 56%, 26% and 25%, and the WD category with an increase of 36% for heat pumps. However, its impact is relatively low for gas boilers.

**Figure 9.** Impacts of different parameters on environmental results for heating technologies in sensitivity analysis.

#### **4. Conclusions**

This study assesses the environmental impacts of heat pumps vs. gas boilers through three main scenarios: Circular Economy (CE), Resource Efficiency (RE) and Limited Growth (LG), and three alternative scenarios: Transport (SK), 50% Hybrid and 75% Hybrid. The findings illustrate that replacing gas boilers with heat pumps could help to reduce lifetime GHG emissions by 78% (CE scenario), 77% (RE scenario) and 65% (LG scenario). The overall average impact is expected to be lower around 43% (CE scenario), 42% (RE scenario)

and 31% (LG scenario). However, the following categories MEU, TE, FE, ME, ALO and WD perform 5% lower in the CE than in the RE scenario.

Heat pumps provide significant reductions in GHG emissions and the fossil depletion category; however, they do not provide sustainable solutions in other impact categories. Moreover, future scenarios expect reductions in most of the categories; however, several categories expect an increase in contrast to remaining impact categories in all scenarios, such as freshwater ecotoxicity, marine ecotoxicity and metal depletion categories. It is important to point out that the high deployment of renewables, especially offshore wind farms, will have a positive impact in most of the categories, but also create toxicity problems and material scarcities.

Hybrid scenario results (50% Hybrid and 75% Hybrid) expect an increase in GHG emissions as boilers use fossil fuel, whereas the negative impacts coming from the remaining categories decrease. Therefore, a transition period that includes hybrid applications rather than replacing gas boilers individually should be provided in order to reduce negative impacts. In both hybrid scenarios, the overall results suggest a reduction in the baseline model (22% for 50% Hybrid scenario and 10% for 75% Hybrid scenario); however, the changes are 15% lower in the CE scenario. In the CC category, the changes are greater in the CE model as heat demand in the future will be relatively small; therefore, the importance of each phase will be higher to reduce the negative impacts. As the UK increases its ambitions to reach the 'Net-Zero' target, actions for each phase should be considered thoroughly.

In the Transport (SK) scenario, changing the manufacturing location from Europe to Asia creates a 1% reduction in the baseline model and a 2% increase in the CE model. The reason for this slight increase is that the weight of the use phase is lower in the CE scenario due to efficiency improvements in houses and low-carbon technologies, so the remaining phases comprise higher shares. As the main contributor to these changes is the manufacturing phase, better production lines through adapting CE principles could help to reduce the impact of the manufacturing phase. It is also important to reiterate that, even though the impact of the manufacturing phase is relatively smaller than the remaining phases (14% of the overall impact), the manufacturing of heat pumps has an impact in those locations where manufacturing takes place; therefore, this does not count in territorial emissions.

Future scenarios show how decision making could have a significant impact on environmental impacts. The CE scenario provides the best outcome among all scenarios without affecting economic growth. Reducing GHG emissions and preventing negative consequences are highlighted in the CE scenario. Achieving the Net-Zero target requires strong commitments, and the results of future scenarios emphasise that the importance of impacts proposed by changes will reduce in time. Therefore, quick implementation of changes and stronger commitments are required in other areas as well, mainly energy efficiency improvement in houses (insulation, etc.), better-installed heat pumps with higher efficiencies and greener manufacturing solutions.

High demand for specific materials could enhance scarcities and environmental degradation related to resource extraction and processing. Circular economy principles through reuse and recycle options become more important in these situations. However, new strategies are needed to reach the 'Net-Zero' target as it requires stronger commitments and more rapid market dissemination. Therefore, a comprehensive approach through a market introduction programme should be provided at the beginning before shifting from one technology to another. It is important to stress that different heating technologies require different material demands and waste streams. High deployment of heat pumps in the CE scenario (17.7 million) will require high demand for metals and minerals, even though they do not have significant impacts on GHG emissions in the manufacturing phase. It would be of utmost importance to develop CE standards for the production of heat pumps, e.g., through procurement or eco-design, and include the use of secondary materials and the re-usability of all components. Thus, a more comprehensive circular framework for decision-making tools could be created for sustainable design practice. A

holistic approach should be considered where both territorial and consumption-based emissions are considered together for policies and future planning.

**Author Contributions:** Conceptualization and methodology, S.S., C.S. and T.D.A.; software, S.S., T.D.A.; data collection, analysis and validation, S.S.; visualization, S.S.; writing—original draft preparation, S.S., C.S.; writing—review and editing, S.S., C.S., T.D.A., R.B.; supervision, C.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Publicly available datasets were analyzed in this study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**


#### **References**


**Thomas Adisorn \* , Lena Tholen and Thomas Götz**

Energy, Transport and Climate Policy Division, Wuppertal Institute, 42103 Wuppertal, Germany; lena.tholen@wupperinst.org (L.T.); thomas.goetz@wupperinst.org (T.G.)

**\*** Correspondence: thomas.adisorn@wupperinst.org; Tel.: +49-202-2492-246

**Abstract:** The Digital Product Passport (DPP) is a concept of a policy instrument particularly pushed by policy circles to contribute to a circular economy. The preliminary design of the DPP is supposed to have product-related information compiled mainly by manufactures and, thus, to provide the basis for more circular products. Given the lack of scientific debate on the DPP, this study seeks to work out design options of the DPP and how these options might benefit stakeholders in a product's value chain. In so doing, we introduce the concept of the DPP and, then, describe the existing regime of regulated and voluntary product information tools focusing on the role of stakeholders. These initial results are reflected in an actor-centered analysis on potential advantages gained through the DPP. Data is generated through desk research and a stakeholder workshop. In particular, by having explored the role the DPP for different actors, we find substantial demand for further research on a variety of issues, for instance, on how to reduce red tape and increase incentives for manufacturers to deliver certain information and on how or through what data collection tool (e.g., database) relevant data can be compiled and how such data is provided to which stakeholder group. We call upon other researchers to close the research gaps explored in this paper also to provide better policy direction on the DPP.

**Keywords:** resource efficiency; product policy; energy efficiency; digitalization; life cycle assessment; easy-to-repair design

#### **1. Introduction**

At the international level, with the Agenda 2030 [1] the global community has defined 17 Sustainable Development Goals (SDGs) for socially, economically and ecologically sustainable development [2]. Sustainable development in general and the SDGs in particular require suitable indicators and corresponding data in order to initiate necessary policy action and to measure progress.

On the level of the European Union (EU) and with regard to product policy, the provision of data and the organization of a comprehensive information flow is promoted, among other things, by the "European Green Deal" [3] and the "Circular Economy Action Plan" [4] of the EU. Another impetus that makes the topic of product policy and data collection/provision even more relevant is the topic of digitalization, which has been heavily discussed for years (cf. [5]). In this context, a concept that is gaining attention in the political agenda is the development of a Digital Product Passport (DPP), which is not only topic in the two already mentioned EU strategies but also confirmed in the "Council conclusions on Making the Recovery Circular and Green" drafted under the German EU Council Presidency [6]. For providing input to the German Council Presidency of the second half of 2020, the authors of this article developed a scoping paper on the DPP, which this article is based on [7]. From the anchoring in high-level policy strategies, one can derive the high expectations on the DPP as an essential new tool for enabling a holistic and comprehensive recording of sustainability aspects in the future. Among other things,

**Citation:** Adisorn, T.; Tholen, L.; Götz, T. Towards a Digital Product Passport Fit for Contributing to a Circular Economy. *Energies* **2021**, *14*, 2289. https://doi.org/10.3390/ en14082289

Academic Editor: Anna Mazzi

Received: 1 April 2021 Accepted: 15 April 2021 Published: 19 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the DPP is intended to provide consistent "track and trace" information on the origin, composition, repair and dismantling options of a product, as well as on its handling at the end of its service life. The aim of the DPP is not only to promote a circular economy and thus support a low-carbon transition but also to overcome existing obstacles like the lack of information. The DPP has the potential to provide different actors (such as consumers and waste management companies) with relevant information on a product and thus force decisions towards sustainable development (for consumers during the purchase and use phase, for waste management companies during disassembling and recycling). For this undertaking, e.g., Gligoric et al. have been developing smart tags based on printed sensors to product or object identification on a per item-level [8], while Donetskaya and Gatchin in their conference paper come up with some requirements for the content of a DPP [9]. Depending on its exact design, it may help companies along the value chain to develop sustainable business models. For instance, Longo et al. argue to manufacture batteries and vehicles "with fewer, renewable, recyclable/recycled, and non-hazardous materials and characterized by lower energy and environmental impacts during their life cycle" [10] and Wielgosi ´nski et al. call for a reduction of waste streams by having raw materials circulated in the domestic market [11]. To make businesses deliver to these objectives, the obligation to generate high quality product information can be a valuable contribution in a policy mix for an effective circular approach [12].

At the European level, the DPP is most prominently discussed in the context of the Sustainable Products Initiative (SPI) [13] in combination with the expansion of the EU Ecodesign Directive beyond energy-related products to include as wide a range of products as possible in order to define appropriate minimum sustainability and information requirements for specific product groups. Following this, DPP and SPI are also closely related to other recent EU initiatives such as in particular "Consumer policy-strengthening the role of consumers in the green transition" [14]. The central objective of the latter is to revise EU policy within the framework of the "European Consumer Agenda" [15], to enable consumers to play a more active role in the timely transition to a more sustainable economy ("green transition") by providing reliable and useful product information. Among other things, minimum requirements for sustainability logos and quality labels as well as reliable environmental information, e.g., on service life and repair options, are to prevent claims from being glossed over in the sense of "greenwashing" (i.e., giving a false impression of the actual environmental impact) or products being sold with a shortened service life. In addition, as part of the EU initiative "Environmental performance of products & businesses-substantiating claims" [16], companies will in the future be increasingly required to substantiate information on the environmental footprint of products or services using standardized quantification methods. The aim here is also to make environmental claims more reliable, comparable and verifiable throughout the EU and thus to reduce "greenwashing" and strengthen trust in environmentally relevant information. While DPP's overall contribution to facilitating circularity appears to be relatively clear and policy is currently moving the topic more into the spotlight, a widely applicable and holistic DPP-approach has not yet been established in practice. Accordingly, there are no finalized concepts at the political level as to how a DPP affects different stakeholders. However, there are some approaches and ideas on how the DPP could be implemented.

For instance, at the level of the EU's Member States, the German Government has picked up EU discussions on the DPP. According to the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) [17], the digital product passport is defined as a data set that summarizes the components, materials and chemical substances or also information on repairability, spare parts or proper disposal for a product. The data originate from all phases of the product life cycle and are to be used for the optimization of design, production, use and disposal. The structuring of environmentally relevant data in a standardized, comparable format should enable all actors in the value and supply chain to work together towards a circular economy in a goal-oriented manner. At the same time, the digital product passport is intended as an important basis for more

reliable consumer information and sustainable consumption decisions in both stationary and online retailing. According to the BMU, the DPP should in principle be applicable to all products and services as well as foodstuffs, with an initial focus on particularly resource- and energy-intensive goods [18]. These would include, for example, information and communications technology (ICT) products or products from other sectors with high energy and material consumption. Another study conducted by the European Policy Centre on behalf of the BMU that sketches possible ways of designing and implementing a DPP was published in late 2020. The aim of the study was to find "better coordination and exchange of information in value chains [to] enhance transparency while creating the basis for smart circular applications". The study suggests that the EU should start developing general guidelines for "tracking and mapping [ . . . ] products, materials and substances across value chains". A DPP should build on existing databases and information requirements and take into account the experience that companies have already gained in collecting information. The authors of the study propose the Commission to focus on textiles, electronics, construction, packaging, batteries and electric vehicles [5]. DPP was published in late 2020. The aim of the study was to find "better coordination and sis for smart circular applications". The study sugg "tracking and mapping […] products, materials and substances "

Due to the uncertain development of a DPP in the future and the lack of scientific debate on the DPP, this study seeks to work out design options of the DPP and important questions to be answered in the not-too-distant future regarding the implementation of the DPP. In so doing, we first show our step-wise approach (Section 2) and, then, describe the existing regime of regulated and voluntary product information tools focusing on the role of stakeholders (Sections 3.1 and 3.2). Intermediate results presented in Section 3.3 are examined in an actor-centered analysis on potential advantages gained through the DPP factoring in the most relevant stakeholder groups in a product's value chain. Lastly, in Section 4, we discuss our results with respect to the design of the DPP, and we focus on open questions, which need to be addressed in the not-too-distant future. DPP factoring in the most relevant stakeholder groups in a product's value chain. Lastly,

#### **2. Materials and Methods**

This study seeks to identify relevant points of discussion as regards the implementation of the DPP in order to maximize the socio-economic benefits across stakeholder groups. In so doing, we carried out a two-step approach, as shown in Figure 1.

First, we looked at the current regime of product information and factored in the following questions:

• Who delivers product information?


By systematically reviewing relevant literature, we screened regulated and voluntary initiatives, which are implemented or developed from a variety of sectors in order to gain a rich overview of relevant factors to be taken into account when implementing the DPP as envisioned in the introduction of this article for the sake of providing more circular information. These findings on central characteristics of state-of-the-art information tools are then reflected in part two of the analysis: the actor-centered analysis. This part of the study will stimulate the discussion on the design of the DPP regarding the most relevant stakeholder types in a product's value chain: manufacturers, market surveillance, retailers, investors, repair shops, waste management companies.

Experts from the BMU and German Federal Environment Agency (UBA) were part of the project's expert circle and validated our findings periodically. In order to gain hands-on perspectives on the DPP, we also carried out a national expert stakeholder workshop in late 2020 as part of the project, this article is based on. More than 20 experts participated in the workshop, and the participants were selected in a way to cover a broad range of areas. This included experts from the BMU, the UBA and from the fields of standardization, digitalization, waste management, engineering and equipment manufacturing as well as academia. For the workshop, first project results were presented and discussed.

#### **3. Results**

Today, there are already a number of legal or voluntary information requirements in the area of product policy that determine information and information flows from point A to point B. At the EU level, information requirements exist for all phases of the product lifecycle, such as production, use, repair and disposal, but these requirements are mostly defined in a product-specific way. Results of the project, this article is based on Supplementary Materials.

#### *3.1. Regulated Product Information*

An illustrative example for current information flow regimes is the EU's energy labeling framework regulation, which defines a mandatory label and information obligations for selected product groups at the time of "placing on the market" (first time a product is made available on the EU market). With the status of March 2021, 15 product groups require an energy label [19]. Accordingly, product group or model-specific information must be published both on a label and on product data sheets. In the respective product group-specific implementation measures, the contents and information are further specified. For example, the label for refrigerators must include the manufacturer's name, the efficiency class, the electricity consumption per year, the volume of the refrigerator/freezer compartment and the maximum noise level for the corresponding model. The product data sheet, which must also be provided by the supplier, contains further information in addition to the information on the label, such as the exact design or duration of the manufacturer's guarantee. In addition, the Directive obliges suppliers to enter the information in the product data sheet and other data ("technical documentation") into an official digital EU database (EU Product Registration database for Energy Labelling, EPREL) via a special input page. This consists of both, a public part (for end users, among others) and a non-public part, which are only accessible to the European Commission and market surveillance authorities [20]. Apart from market surveillance, investors are a key target group of product information compiled by manufacturers. In particular, the Energy Label helps investors (including the public purse) to make conscious purchasing decisions [21], and the Label's recent revision of the scaling system is supposed to deliver higher efficiency gains through a more comprehensible labeling scheme. Retailers may also use the product information in sales talks, particularly those accompanied by the Energy Label. It should also be acknowledged that retailers do not enter or provide any new information, but they are responsible for ensuring that labels are placed on the respective products. To a very

limited extent, repair companies and waste management companies can also benefit from the (limited) information by being able to verify certain aspects of the product.

Registration with the EPREL data is already mandatory as of February 2021 for the following product groups: air conditioners, household cooking appliances, household dishwashers, space heaters and water heaters, light bulbs, individual space heaters, household refrigeration appliances, commercial refrigeration appliances, solid fuel boilers, televisions, tumble dryers, residential ventilation appliances, and household washing machines [20]. In addition, since March 2021, consumers can also use the product database for the relevant public information on energy labels and product data sheets through a QR code that is printed on the label of some first product groups. Figure 2 below schematically illustrates the general structure of the EPREL product database.

**Figure 2.** Schematic figure of the EPREL database (based on BMWi 2019 [22]).

The accessibility of the database for (potential) investors via an easy-to-use QR code is important to deliver information immediately at the point of sale, where a conventional website with cumbersome data entry would be of less help to investors.

" " In addition to the framework regulation on energy labeling, other EU regulations also contain subject-specific information and reporting obligations that differ more or less significantly depending on product and target group. For example, the EU Ecodesign Directive 2009/125/EC for energy-related products and appliances and its product group-specific implementing measures include, as does the closely linked EU framework regulation on energy labeling, information obligations at the time of "placing on the market". While aspects of circular economy and on repair options are increasingly included in the Ecodesign Directive, a central database has not been used for this purpose yet and a systematic data flow has not been prescribed. The information only has to be publicly available on a website of the manufacturer, importer or authorized representative. Another example for information requirements is the REACH Regulation EC 1907/2006 (REACH stands for Registration, Evaluation, Authorisation and Restriction of Chemicals). It includes safety data sheets for chemicals and further information on substances and mixtures and in particular on hazardous ingredients. Chemicals manufactured in the EU or imported into the internal market must be registered. The safety data sheets are primarily intended for persons who are in direct contact with the substances. This information must be provided either in electronic form or printed on paper and is intended to help protect health and the environment. In addition, the SCIP database ("database for information on Substances of Concern In articles as such or in complex objects (Products)") will be set

up for SVHC ("substances of very high concern") in 2021 [23]. Suppliers will be required to provide their information to the European Chemicals Agency (ECHA). The aim of the database established is to provide operators of waste treatment plants with information on SVHCs in order to be able to separate them if necessary and to ensure high-quality recycling. Also focusing on chemicals, the Regulation EC/1272/2008 on classification, labelling and packaging (CLP) of substances and mixtures based on the United Nations Globally Harmonized System (GHS) has defined obligations for labeling [24]. Moreover, for this purpose, the European Chemicals Agency (ECHA) maintains a database on the classification and labeling of notified and registered substances. The Waste of Electrical and Electronic Equipment (WEEE) Directive 2012/19/EU is another example for information requirements. The Directive established obligations for electrical and electronic equipment, in particular with regard to the provision of information for recycling companies and operators of treatment facilities. This can be done by means of printed manuals or in electronic form. In addition, EU member states are required to establish a WEEE producer register. The EU Packaging and Packaging Waste Directive EU/2018/852 stipulates that clearly legible markings on materials in the packaging must be attached to the product; the Fertilizer Regulation EU/2019/1009 requires manufacturers to publish information on various product properties (storage conditions, volume, ingredients, etc.) on the product or in an accompanying document. The End-of-Life Vehicles Directive 2000/53/EC specifically regulates the publication of information on the dismantling, storage and testing of reused parts in end-of-life vehicles. In the international dismantling information system IDIS ("International Dismantling Information System"), vehicle manufacturers can deposit data to support disposal companies in the environmentally friendly treatment of end-of-life vehicles [25]. Another data collection system for vehicle manufacturers is the IMDS (International Material Data System), in which all materials used in the manufacture of a vehicle are collected [26]. In this context, the use of the IMDS should make it possible to fulfill the obligations imposed on the automotive industry by national and international standards, laws and regulations [27]. In addition to the examples presented, there are also various other approaches to data collection and presentation, such as the EU-wide standardized food labeling.

#### *3.2. Voluntary Product Information Initiatives*

In addition to the regulatory requirements, there are also numerous ideas and concepts on how (parts of) a digital product passport can be implemented. Some of these are already being implemented. One example is the concept of Material Passports. In more recent discussions, MPs have been developed with special focus on the building sector. Even though this concept is not necessarily restricted to construction materials only [28], buildings appear to be the central area of application so far. As part of the EU-funded research project focusing on reversing building design, partners develop an electronic Material Passport Platform as a one-stop-shop for material information provided by manufacturers and suppliers [29]. It is considered as record or documentation of properties of materials in order to facilitate recycling and reuse [30]. Hence, Material Passports increase transparency on the circularity characteristics of building materials and information includes, amongst others, data from technical data sheets or environmental product declarations (EPD). As soon as the a building is decommissioned, information can be made available to contracted deconstruction firms [31].

Technical documentation can be regulated, as for those product groups addressed under the Energy Labelling Directive. EPDs are generally voluntary and based on a life cycle assessment providing extensive quantitative and (third-party) verified information on environmental impacts without evaluating or judging them [32]. In Germany, EPDs have so far been used in practice also in particular for the comprehensive description of the environmental performance of building products. The environmental impacts of production, use and disposal are characterized according to internationally recognized conventions, resulting in key figures such as greenhouse potential in CO<sup>2</sup> equivalents, water

consumption, waste production, ozone depletion potential or acidification potential [33]. In this way, EPDs should, for example, specifically facilitate the selection of materials in construction and form a basis for the documentation of the building materials used in the building (e.g., by means of a building passport) [34]. As regards the Material Passport Platform, the cross-referencing to other information tools shows that developers do not want to design new tools from scratch, but they also seek to build on existing information tools and embed this information for their purposes. Due to increased transparency, architects or builders can make use of materials with more circular characteristics.

Building information modeling (BIM) is seen as a vehicle to compile more comprehensive information on the entire building level (in contrast to the material level). BIM is a tool for networked planning, execution and management of buildings and may function also as an inventory database on the building level (in contrast to the component level). According to Honic et al. "the main results obtained from the BIM-supported MP is the total material composition of the building [...], which contrasts the share of recyclable materials with the share of waste created by the building" [35]. A challenge for MPs might be the feeding of material information continuously. For instance [36], state that steel used in buildings can, in general, be re-used without substantial testing in laboratories. However, if steel is exposed to fire, its characteristics may change, which is why the usage history of building materials can become important [36]. Such expositions but also major refurbishments, which could alter materials in buildings, would and could—ideally—be updated [31].

In addition to the MP, other concepts exist such as the cradle-to-cradle passport (C2Cpassport). For example, the Danish shipping company Maersk already makes use of a C2C-passport for part of its own fleet of ships. The C-2-C-concept is based on a proprietary approach developed by McDonough Braungart Design Chemistry (MBDC). In 2010, MBDC transferred the certification program to the non-profit Cradle to Cradle Products Innovation Institute (C2CPII), which has since acted as a third-party certification body. The objective is to recycle materials used at the end of a product's life. Maersk's passport shows, for example, which materials are used in which location of a ship and provides, for instance, information about quality differences in the steel used. For Maersk, some of the key tasks were to develop a database for material information and to encourage suppliers to make complex material information (including its composition) available and feed it into the database. Materials should then be able to be located directly in a 3D model of the ship, which is why the passport already plays an important role in the development phase [37]. For ship owners or operators, this increases transparency and allows to identify potentials for reusing existing (and already purchased) materials. In the end, this may decrease material inputs and potentially overall costs for new ships, even though costs for training staff and deconstructing ships as well as testing steel characteristics will have to be added. As regards the C2C-passport, there is a direct (financial) interest in designing ships in a transparent way, which might be a different case for actors in the construction sector.

The comprehensive digitization of industrial production is known under the terminology of *Industrie 4.0.* In this context, the concept of the "asset administration shell" (AAS) was developed to systematically record and retrieve data on manufacturing equipment [38]. The AAS represents a digital image of the real production object, which is often also referred to as a "digital twin". The AAS, thus, opens up the conceptual link between the real and digital worlds. So far, this has been used primarily in progressive industrial companies and above all to optimize internal industrial production processes and procedures. Reference Architecture Model 4.0 (RAMI 4.0) is the (underlying) conceptual basis for data collection, which is based in principle on the Smart Grid Architecture Model (SGAM) established in the energy sector. In principle, the more relevant data is stored in the AAS, the more precise the mapping of the digital twin. Data (if available) can be mapped over the complete product life cycle, from development to the end of the product's life. Industry-internal information and communication technologies and IoT-technologies (Internet of Things) systems can thereby continuously capture and store data in real time so that the AAS can correspond with the real object as best as possible at any time. Data sets can, for example,

consist of pre-configurations of production machines, material properties of intermediate products [39], limit values for use (e.g., maximum speed, highest possible operating temperature) or manuals, CAD drawings, key production figures (for example, target and actual values) or maintenance information [40,41]. However, the concepts of RAMI 4.0 and the AAS have so far been geared primarily toward use within highly networked *Industrie 4.0* areas. The AAS has therefore so far been used primarily in the production of complex production objects to create a network between appropriately equipped suppliers, integrators, machine manufacturers and other industrial users (cf. [38]). In theory, suppliers, integrators and manufacturers may benefit from increased information flows from the usage phase in order to improve product performance and for carrying out predictive maintenance.

#### *3.3. Key Takeaways from Regulated and Voluntary Product Information Initiatives*

All in all, a relatively clear picture emerges from the status quo analysis and from the different concepts and initiatives:


These intermediate findings need to be taken into account as regards the potential benefits for each stakeholder group discussed in the next Section. As a summary, an overview of the different approaches compared to the currently discussed design of the DPP is illustrated in Table 1.


#### **Table 1.** Comparison of information tools.


**Table 1.** *Cont.*

#### *3.4. Preliminary Actor-Centred Analysis of Potential Benefits Delivered by the DPP*

Moreover, for the Digital Product Passport, manufacturers will likely remain the central suppliers of product information. Hence, additional (transaction) costs incurred due to further information demands (potentially also to be requested from suppliers) need to be kept at a minimum, even though it should be acknowledged that learning effects reduce the administrative costs in the longer run (cf. [42]), and trends in digitization (IoT, blockchain, machine learning) ease information gathering. Synergies should be seen with recent legislative developments, e.g., in Germany on the country's Supply Chain Act and similar initiatives on the EU level [43]. The DPP may help to provide a more consistent and untangled overall framework for manufacturers to deliver product information, but this would require a comprehensive integration of existing regulation and could be regarded as a challenging undertaking given that several of the above-mentioned regulations are administered by different Directorate Generals of the Commission. Still, gradually, the DPP may help to switch from mixed physical and digital information to a digital-only information supply including technical and safety data sheets. However, for this, it would also need to be ensured that target groups have the equipment necessary to really gain information access. In order to increase the motivation of manufacturers to deliver more circular information, attractive circular business models would need to be incentivized as well. This can also include that IoT-equipped products deliver information for manufacturers enabling them to expand their business model (e.g., predictive maintenance) as envisaged for the AAS. The Energy Label is also a success as it offers sustainable manufacturers to showcase their products' advantages in terms of sustainability and circularity and EPREL has high security standards which exacerbate data theft. Given that the DPP is supposed to be available for a variety of products, information requirements would need to be analyzed

in a sector- or product-specific way (e.g., through a feasibility study) and manufacturers need to perceive a DPP infrastructure as a reliable and trustworthy system.

Market surveillance authorities can use product information to monitor whether manufacturers meet product standards in practice, also to protect manufacturers complying with standards against unfair competition. For such authorities, a central system, in which all information is organized, might be extremely helpful. In this respect, the EPREL database can be considered a good example as it is designed to contain selected regulated information. In our stakeholder workshop, experts argued that the digital product passport should also be seen as a part of a substance inventory, which takes stock of goods that are a" valuable secondary raw materials reservoir" and a "capital stock of the future" [44].

Retailers can use the improved information provided by a product passport to make their product range more customer-oriented and sustainable and to provide a corresponding range of information at the point of sale. Here, too, it plays a major role, which data retailers receive and to what extent this can be used in customer advice. In addition to retailers, contributors to the common good economy (second hand stores, etc.) should also be considered, as they can offer remanufactured products that are generally still usable. For them, the DPP may help if information from repair shops can be fed into the product documentation. Moreover, information on how a product has been used would also be largely beneficial as it would increase the trust of buyer in second-hand products. However, the question is what type of information can overcome barriers to purchasing second-hand products and how can the information be fed into the DPP. Amongst other, this may require the continuous multidirectional feeding of product-specific (in contrast to model-specific) information resembling the architecture of the AAS (which is largely envisioned for *Industrie 4.0*).

The key potential benefit of the DPP for product users is transparency, and private and institutional customers can make more conscious purchasing decisions. By differentiating between end-users, the role of green or sustainable public procurement should also be acknowledged as the public purse has a huge potential to transform products markets due to its buying power [45]. Products may reveal high social and ecological costs associated with production and customers are given the opportunity to buy products with a low socio-environmental footprint. Further valuable product information for customers may include the repairability and the end-of-life handling. However, it remains to be seen how information or data will be processed and made available to lay people. In order for customers to make sustainable purchasing decisions, information needs to be accessed with least possible effort. For instance, as regards the EU's Energy Label, the well-known scaling system (green to red arrows) visible to customers helps to easily differentiate between efficient and inefficient energy-related products, while disclosing only (standardized) energy consumption data (e.g., in kWh/a) would not be considered helpful by most users. An existing system for simple product identification for retail products, for example, is based on the "Global Trade Item Number" (GTIN), i.e., an identification number that can be used to uniquely identify many types of trade units. It must be mentioned here that this system has not yet been used for product-specific recording but rather for identification at the product group or model level. In any case, it is absolutely essential for a digital product passport that a product group, the model or, in perspective, even each individual product is clearly and easily identifiable. As with the EPREL database, for example, data could then be accessed directly via the individual item, e.g., via bar/QR codes or RFID tags on the product or product label (RFID stands for Radio Frequency Identification; small/tiny chips allow for wireless transfer of data). It would also make sense for consumers to be able to understand the information provided, including the language and meaning of the information, by making product features available via apps, websites or augmented reality, for example.

In contrast to product users, repair shops are dependent on precisely disaggregated information about repairs and spare parts, while information on socio-ecological effects associated with production is hardly a concern for them. Repair information is already required for some products (e.g., cars), and an extension could result in a rise of repair shops for many other products. An essential step will be that EU and national regulations require products to be manufactured in a way that factors in circularity and the right-torepair. If in parallel, consumers are aware about the repairability of their products, this may strengthen the business model of repair shops.

In addition, companies from the waste management sector may also be interested in highly disaggregated data, which usually plays a minor role for consumers, for example. In particular, materials (and combinations) included in products, dismantling information and end-of-life handling will be of relevance. Through such information, dismantling costs can be reduced, and by selling recycled materials at higher qualities, revenues can be increased. If repair companies exchange certain components in a product, compositions of new materials used may also be relevant for waste companies.

#### **4. Discussion**

The Digital Product Passport seeks to facilitate a circular economy and a low carbon transition acknowledged by the EU [4] and the German BMU [17]. It is supposed to deliver information on the origin, composition, repair and dismantling options of a product, as well as on its handling at the end of its service life [3]. However, there are several open questions regarding the DPP's final design and its implementation. For instance, a long-time grown regime of diverse information requirements already exists, in which the DPP needs to be fitted into.

Having looked at certain parts of this existing landscape form a bird's eye perspective, we found that manufacturers are the most important source of product information. This means that any future DPP information requirements should be ideally designed in a way that manufacturers and other stakeholders perceive them as an advantage and not as an additional burden, in order to create business models and intrinsic motivation. If additional information obligations are imposed, they should create as much as possible synergies with other compliance regulation (cf. [43]). Therefore, the initial DPP approach should build-up on existing systems of regulations [5] also acknowledging technology trends as well as learning effects for information supply [42].

For instance, under the Waste Framework Directive, companies supplying products containing SVHC (above certain concentrations) already must supply selected information on these articles to a database made available to waste operators and consumers [23]. Under the Energy Labelling Directive, manufacturers of refrigerators have to supply a variety of information (e.g., efficiency class, electricity consumption per year, the maximum noise level for the corresponding model). However, this information mostly focuses on the use phase of a product and have to be fed into the EPREL database. In contrast to that, the Waste of Electrical and Electronic Equipment goes beyond the Energy Labelling Directive's product scope and mandates manufacturers to deliver information on equipment disposal and handling at the end of its life, while the End-of-Life Vehicles Directives focuses on similar information types (e.g., dismantling information) but for a particular product group. For the DPP, a key question will be how to organize an optimized and synergetic data flow with the existing framework of regulatory efforts for manufacturers, which really are the core stakeholder group, at the moment. In contrast to the regulated information flows, there are also voluntary initiatives on the market or in development. In our study, we selected some information tools, which seek to contribute to a circular economy. They also differ from each other. Similar to EPREL or SVHC, they make use of a digital system to compile, feed in and retrieve data or information.

It might be helpful to investigate further on the existing information tools in order to find out what information are technically feasible to be supplied for a DPP. An option to reduce the administrative burden of manufacturers can be to, initially, develop an approach that integrates existing information requirements in a smart way, where a single point of information brings together all existing information with high security standards and provides them according to different access rights to specific stakeholder groups (cf. [7]). Thus, this single point of information will be fed by manufacturers with minimal transaction costs for changing information supply. In other words, the information requirements (mandated in various regulations and directives) remain the same, so there is not additional effort to compile new information for manufactures, only the point to enter the relevant information might differ.

As regards the basic technical infrastructure necessary to implement the DPP, the experiences from the EPREL database as well as the Asset Administration Shell deserve some more attention. One of the basic key features of EPREL is that confidential information and non-confidential information can be fed into the database, which is relevant if information may be mandatory from the perspective of a market surveillance agency but not for other stakeholders; some information might also have to be shielded from competitors (e.g., extraction/production location of certain inputs for consumer goods). The AAS, considered largely for advancing *Industrie 4.0* and addressing respective equipment, could even provide a basis for multidirectional data exchange regarding single products. This would be interesting e.g., for the information exchange between repair shops and waste operators, especially if the concept of the AAS could be transferred and adjusted for non-industrial purposes. For instance, if particular spare parts are used differing from the original product set up, waste operators could require adjusted product information for recycling purposes. Other opportunities for the multidirectional information flow might also exist and, thus, information feedbacks between different stakeholders should be explored factoring in, e.g., advances in the field of digitization, in general, and IoT, in particular.

All in all, how to generate data during the use phase will remain extraordinarily challenging. It would give not only investors the opportunity to exchange components in advance before more serious damage occur, but is also offers equipment providers to extent business models for instance through predictive maintenance and receiving data in order to improve technology. At present, in most traditional sectors where a manufacturer "just sells" a product to an investor, there is hardly any business case for the manufacturer further down in their product's value chain. So conventional and linear business models still dominate in most sectors. However, the example of the company Maersk suggests that the company hopes to identify corporate sustainability information and new revenue streams or reduce costs at the same time through being better able to identify certain products in ships built. Likewise, the Material Passport factoring in Building Information Modelling may help to break the existing paradigm in construction works helping to generate information during the use phase.

Apart from questions around the existing (regulatory and also voluntary) information landscape and the technical infrastructure, an essential aspect is to focus also on the question how to increase general attractiveness of the DPP to users/investors. For instance, the Energy Label also enjoys broad stakeholder support as it offers manufacturers to illustrate the uniqueness and benefits of their certain product's characteristics to investors (apart from energy efficiency, also noise pollution). However, how can the DPP create similar transparency as regards the circularity of products in order to contribute to the objectives of the European Consumer Agenda [15]? In other words: How will customers know and easily understand which refrigerator belongs to the most "circular" or sustainable ones? Product information only available to stakeholders further down the value chain (repair shops, waste operators) is important for a circular economy but not necessarily to persuade investors to invest in a certain product. Hence, in order to make sustainable choices, consumers need transparent, simple information. If the DPP seeks to raise the awareness of a product's circularity characteristics, the EU needs to find out how this can be achieved (again, without increasing the administrative burden, in parallel).

With the discussion on a digital product passport gaining momentum, there is currently an ideal window of opportunity to bundle ideas at the European level and derive initial options for action as well as further research approaches [3,4]. Scientific feasibility studies should be carried out as soon as possible on how to implement a digital product passport [5]. The German Environmental Agency began to initiate such a study on textiles

and energy-related products, but further research will also have to scrutinize EU-wide conditions in various pilot projects. An analysis of the data needs of various stakeholder groups is essential but also whether these wishes can be realistically met and how taking into account different manufacturers. Since the concept of a digital product passport is still relatively new, there are currently several aspects to be clarified promptly by additional research activities for rapid and concrete implementation. These include, for example, the more precise selection of product groups to be prioritized and thus the question of which products are particularly suitable for the fastest possible introduction of a product passport system. The assessment of various experts and interest groups also still differs greatly in some cases on the question which criteria and precise data requirements should be addressed by a digital product passport. Therefore, a detailed stakeholder analysis including a differentiation, at least, regarding certain subtypes (e.g., SME vs. large companies) should be conducted and is also necessary at the beginning of further research activities in order to determine the respective information needs and acceptance factors more precisely.

In order to involve the relevant stakeholders in this process and promote acceptance, an early exchange within the framework of a scientifically accompanied consultation process is therefore recommended so that opportunities, interests, obstacles and challenges can be identified through active participation. Stating the obvious, the DPP will not be a silver bullet for achieving a circular economy alone, but its realization might make particular sense to form a key instrument in a well-orchestrated policy mix [12].

#### **5. Conclusions**

In order to identify the relevant points of discussion regarding the implementation of the Digital Product Passport, we first screened the current landscape of existing information tools. From the tools scrutinized we were able to draw some key lessons:


In a second step, these lessons were fed into our actor-centered analysis helping to carve out achievable benefits by the DPP, which depend on the overall implementation design of the instrument. In the previous chapter, we discussed that the DPP may be integrated into existing systems of information regulations but that it will be relevant to organize synergetic data flows with the existing framework. In so doing, a single point of information could bring together all existing information with high security standards and provide them according to different access rights to specific stakeholder groups. This single point of information could be fed by manufacturers with minimal transaction costs for changing information supply. Apart from that, the multidirectional information flow is highly interesting as this, e.g., would enable the information exchange between repair shops and waste operators. However, the collection of data during the use phase will remain extraordinarily challenging, though probably more relevant and feasible for some products (e.g., high value products with longer product lifetime) compared to others. Besides, the role of investors must be factored in, and the DPP should ideally help investors to better understand which products belong to the most sustainable ones in their respective product group. However, as described, all those potential design options still need further scientific investigation concerning their suitability for real-life use.

Considering all gained perspectives and results, the DPP is a very promising policy instrument that is correspondingly linked with high expectations by many stakeholders. However, being at an early stage of the discussion, several open issues need to be addressed before a Digital Product Passport can be implemented on a large scale. With this paper, we hope to initiate a broader scientific discussion and that further research take on these challenging questions to provide orientation for the DPP's design. If implemented carefully in a sense that visibly increases the benefits for different actor types and ideally also reduces costs or efforts, there is a strong potential to drive sustainable product policy in a more circular direction. Closing the material loop in the sense of a more holistic ecodesign can mean that the EU's demand for new raw materials can be reduced while increasing independence of the EU from less trustworthy suppliers at the same time (also increasing leverage in other policy fields). Information on better product usage and repair may result in innovative new circular business models in the EU extending the lifetime of products and creating also new efficiency and job opportunities. Within the EU market, the DPP in combination with complementary regulation may help innovative manufactures to stand out from competitors that hardly care about circularity. At the same time, given the EU market's strong international role in and influence on manufacturing worldwide, the DPP (in combination with other instruments, such as ecodesign) may also function as a further starting signal to transform production systems globally towards more sustainability. In this context, the DPP could be seen also as part of a complex puzzle to lower the divide between more industrialized and less industrialized countries in the sense of the SDGs.

**Supplementary Materials:** Results of the project this article is based on are available in Götz, T.; Adisorn, T.; Tholen, L. Der Digitale Produktpass als Politik-Konzept: Kurzstudie im Rahmen der Umweltpolitischen Digitalagenda des Bundesministeriums für Umwelt, Naturschutz und nukleare Sicherheit (BMU); Wuppertal Report; Wuppertal Institut für Klima, Umwelt, Energie: Wuppertal, Germany, 2021; Volume 20, p. 44, also available online at https://wupperinst.org/a/wi/a/s/ad/73 15 (accessed on 16 April 2021).

**Author Contributions:** Conceptualization, T.A. and T.G.; methodology, T.A., L.T. and T.G.; formal analysis, T.A. and L.T.; investigation, T.A., L.T. and T.G.; writing—original draft preparation, T.A., L.T. and T.G.; writing—review and editing, T.A., L.T. and T.G.; visualization, T.A.; supervision, T.G., T.A.; project administration, T.G. and T.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This article is based on the project "Environmental Policy and Digitalization", which was funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety. The responsibility for the content of this publication lies with the authors.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to thank the various experts from the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety as well as the German Federal Environment Agency and participants in the stakeholder workshop for their valuable input contributing to the results of the overall project, this article is based on.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**



#### **References**


## *Article* **Technological Solutions and Tools for Circular Bioeconomy in Low-Carbon Transition: Simulation Modeling of Rice Husks Gasification for CHP by Aspen PLUS V9 and Feasibility Study by Aspen Process Economic Analyzer**

**Diamantis Almpantis and Anastasia Zabaniotou \***

Circular Bioeconomy and Sustainability Research Group, Department of Chemical Engineering, Engineering School, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece; diamosalmpantis@gmail.com **\*** Correspondence: azampani@auth.gr; Tel.: +30-6945990604

**Abstract:** This study explored the suitability of simulation tools for accurately predicting fluidized bed gasification in various scenarios without disturbing the operational system, and dedicating time to experimentation, in the aim of benefiting the decision makers and investors of the lowcarbon waste-based bioenergy sector, in accelerating circular bioeconomy solutions. More specifically, this study aimed to offer a customized circular bioeconomy solution for a rice processing residue. The objectives were the simulation and economic assessment of an air atmospheric fluidized bed gasification system fueled with rice husk, for combined heat and power generation, by using the tools of Aspen Plus V9, and the Aspen Process Economic Analyzer. The simulation model was based on the Gibbs energy minimization concept. The technological configurations of the SMARt-CHP technology were used. A parametric study was conducted to understand the influence of process variables on product yield, while three different scenarios were compared: (1) air gasification; (2) steam gasification; and (3) oxygen-steam gasification-based scenario. Simulated results show good accuracy for the prediction of H2 in syngas from air gasification, but not for the other gas components, especially regarding CO and CH4 content. It seems that the RGIBBS and Gibbs free minimization concept is far from simulating the operation of a fluidized bed gasifier. The air gasification scenario for a capacity of 25.000 t/y rice husk was assessed for its economic viability. The economic assessment resulted in net annual earnings of EUR 5.1 million and a positive annual revenue of EUR 168/(t/y), an excellent pay out time (POT = 0.21) and return of investment (ROI = 2.8). The results are dependent on the choices and assumptions made.

**Keywords:** rice husk; gasification; CHP; Aspen Plus; simulation; economic assessment; circular economy; low-carbon energy; waste-based bioenergy

#### **1. Introduction**

The biocapacity of earth in biomass resources amounts to 172 billion t of dry matter that contains ten times more energy than the energy consumed worldwide [1] (Eurotex, 2020). This huge energy potential remains largely unexploited, as only 1/7 of the world's energy consumption is covered by biomass, mainly for traditional uses (combustion). However, 1 t of biomass is equivalent to about 0.4 t of fuel oil, only 3% of global energy needs are met by using available biomass [2].

Residues and waste from agricultural and industrial processes in Mediterranean countries, such as olive kernels or rice husks from agro-industrial plants, wine from wineries or fruit stones from fruit processing industries, are insufficiently used, resulting in a significant amount of waste left in the fields. Taking Greece as a Mediterranean case, although its total available biomass reaches approximately 7,500,000 t of crop residues (cereals, maize, cotton, tobacco, sunflower, twigs, vines), and 2,700,000 t of forest residues

**Citation:** Almpantis, D.; Zabaniotou, A. Technological Solutions and Tools for Circular Bioeconomy in Low-Carbon Transition: Simulation Modeling of Rice Husks Gasification for CHP by Aspen PLUS V9 and Feasibility Study by Aspen Process Economic Analyzer. *Energies* **2021**, *14*, 2006. https://doi.org/10.3390/ en14072006

Academic Editors: Anna Mazzi and Jingzheng Ren

Received: 7 March 2021 Accepted: 26 March 2021 Published: 5 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

(branches, bark), in addition to significant amounts of residual biomass from energy crops, the largest percentage of this biomass remains unused, often causing many hazards (fires, spreading diseases) [2].

The European Union (EU) aims to increase biomass uses towards helping to achieve goals of renewable energy and greenhouse gas (GHG) emissions reduction. Facilities that use locally renewable energy sources, designed to supply local energy communities through micro- and small-scale units, are at the forefront of the EU energy strategy, while combined heat and power (CHP) production from agricultural waste and residue could be a viable way for the development of renewable, reliable, and affordable electricity, while improving waste management, contributing to sustainable agriculture, and implementing circular economy innovations [3].

The end-users of bioenergy can vary in scale, from households, school, public buildings and tourist complexes to district heating, and heat and steam production in agroindustrial facilities. Thus far, CHP biomass systems have been proven to be viable only at large scales that are supported by tariffs or green certificates. However, it is important to mention that large scale bioenergy demand for the scale-up of biomass availability may have some consequences on the environmental impacts that the bioenergy sector can create, due to the direct relationship between the biomass demand scale and the GHG profile of its production.

Sustainable small-scale biomass plants, which produce CHP, appear to be among the most promising techniques for decentralized energy production if they can operate sustainably. The small-scale units benefit from a flexible integrated technology system, with the possibility of the successful penetration into the electricity market, in the market, and the promotion of regional development and the strengthening of the agricultural sector. However, investments or long repayment times create obstacles to their implementation [3].

Gasification is the thermochemical conversion of biomass into gas-fuel through heat with a gasifier agent such as air, oxygen, or steam. Air is the most used gasification agent because it is cheap and readily available [4]. The syngas produced can be stabilized in quality, so it is easier to be used and has multiple uses compared to the original biomass from which it derives, in gas engines and gas turbines, or even as a power supplier for liquid fuel production [5]. Installed gasification units operating in different parts of the world are differentiated by the type of gasifier. Gasifiers fueled with organic materials and residual biomass may need to be specially designed for higher efficiencies, better economies, and a safe environment [5]. Although the smallest size of biomass particles is favorable, it is essential to consider that energy consumption to reduce particle size should reduce overall energy efficiency, therefore different types of gasifiers should be designed to handle different sizes of biomass particles [4].

#### *Scope and Objectives of the Study*

This study aimed to present a customized circular waste-based bioeconomy solution for a rice processing industrial sector, which is of great technological and commercial interest in many countries, and to support the use of simulation tools for the planning phase of bioenergy solutions within a circular bioeconomy. These tools are the Aspen PLUS V9 and Aspen Process Economic Analyzer for process simulation and economic assessment, respectively, which were used in this study for the air gasification-based CHP system fueled by rice residue.

The scientific objectives were: (1) the simulation of an atmospheric fluidized bed gasification (FBG) system fueled with rice husk (RH) for CHP generation with an Aspen Plus V9 simulation modeling (SM) tool; (2) the simulation of steam and (steam + oxygen) FBG scenarios to compare with the air FBG main scenario by using suitable indicators; and (3) the feasibility study of an air gasification-based unit with a capacity of 25,000 t/y RH using the Aspen Process Economic Analyzer.

This study does not intend to bring technical innovation beyond the state of the art on gasification and CHP technology. It is based on the SMARt-CHP innovative technology, a prototype of an FBG-based system designed and developed at Aristotle University, Greece, and funded by a European Commission LIFE+ project some years ago. The experimental proof of concept of RH gasification results are published elsewhere [6]. After collecting the experimental results and designing an FBG system, it was considered a useful move for the bioenergy sector to introduce simulation modeling (SM) to allow developers and users to examine the system operation, using different possible scenarios and conditions, and using less time-consuming tools for planning at higher technological readiness levels (TRLs).

#### **2. Methodology**

The simulation study was based on experimental data obtained at our laboratory by previous researchers [6]. The technology used was the SMARt CHP technology developed by our team and described in a previously published work [7].

The modeled flow diagram of the bioenergy system was developed by using the Aspen Plus software, which proposes appropriate devices for the process simulation at the proposed operating conditions.

A sensitivity analysis was performed to explore the relation of syngas product using indicators such as the equivalence ratio (ER), low heating value (LHV), cold efficiency (CCE) and cold gas efficiency (CGE) and the steam-to-biomass ratio (SBR) by selecting the gasification agent as the design variable. The comparison of the gasification efficiency in relation to the use of other gasifying agents (air, steam, and combination of oxygen-steam) was also performed.

An economic assessment was performed by using the Aspen Process Economic Analyzer software, estimating the economic indicators of fixed investment, total investment, annual operating costs and the net profit of the unit, as well as the return of investment (ROI) and pay out time (POT) indicators, in order to assess economic viability.

Finally, conclusions of the study were drawn, while assumptions and approaches considered in the calculations were commented on.

#### **3. Materials and Methods**

RH is of a huge reserve and availability at a low price in Greece. It is the by-product of the industrial processing of rice. It accounts for approximately 20 wt.% of bulk grain weight and is very often used as an alternative source of silica in ceramics [8]. It contains 70–80% organic substances such as cellulose, lignin, and 20–30% components such as silica, alkalis, and trace elements [9]. Due to its high calorific value, it can be used as fuel for energy production by gasification [10].

RH has a low inherent moisture content (<10 wt.%) and a C/N ratio >150, thus it is an appropriate fuel for thermochemical processing, such as gasification. Gasification generates the rice husk ash (RHA), which accounts for about 25% of the initial husk weight and causes environmental disposal problems [11].

#### *3.1. Choice of Materials*

RH used in this study was provided by Greek company "Agrino", which is the largest rice producer in Greece (5 t/h) (http://www.agroenergy.gr/content, accessed on 30 May 2020). This production accounts for RH production of approximately 20 wt.% of the total paddy weight (whole grain). Therefore, 5 t/h of paddy grain produces about 1 t/h (20%) of RH, and when it is gasified to generate energy, it generates also about 250 kg/h (25 wt.%) of ash, a volume containing around 45 kg (85–95%) of amorphous silica [12]. The ultimate, proximate, and chemical analysis of "Agrino" RH is presented in Table 1.


**Table 1.** Ultimate and proximate and chemical analysis of rice husk (RH) [6].

#### *3.2. Choice of the Technology*

The technological system used was the SMARt-CHP system that produces renewable CHP from waste-biomass and is used for waste management [7]. The electricity generated is either used on-site or it is supplied to the grid. The heat generated by the process is used to heat the industry's buildings.

SMARt-CHP is a technological output that is suitable for circular economy applications. It consists of a pilot fluidized bed gasifier coupled with an internal combustion engine (ICE). It was designed and developed in our laboratory, funded by an EU LIFE+ project (www.smartchp.eng.auth.gr, accessed on 30 May 2020). The unit includes the following parts:


▪


The max capacity and efficiencies of the SMARt-CHP bioenergy generation technology are:


#### ▪ ▪ *3.3. Choice of Experimental Data*

The experimental data on which the simulation was based were derived from experiments performed at our laboratory in the temperature range of 700–900 ◦C, with under-stoichiometric conditions of oxygen supply. A sub-stoichiometric ratio of 10/90

*v*/*v* % O2/N<sup>2</sup> was used. The conversion yield reached 24% wt./wt. The heat produced was on average 10.6 MJ/Nm<sup>3</sup> . The syngas composition mainly consisted of carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), hydrogen (H2) and traces of ethylene (C2H4) and ethane (C2H6) with average values of 30, 40, 10, 16, 0.75, and 1.15% in *v*/*v*, respectively. Char, the solid gasification by-product, yielded at 33.5 wt.% [6].

#### *3.4. Choice of Simulation Modeling*

Although models can mimic many natural phenomena, they require very detailed information (geometry, materials, and boundary conditions) and high computational resources. Models are classified as stoichiometric and non-stoichiometric depending on whether they are based on equilibrium constants or minimizing Gibbs free energy. Non-stoichiometric equilibrium models are the most common approach to describing the performance of a Fluidized Bed Gasifier (FBG). Aspen Plus is used to model biomass gasification processes [13].

ASPEN Plus is the chemical industry's leading process simulation software that allows the user to build a process model and then simulate it using complex calculations (models, equations, math calculations, regressions, etc.), while it enables lifecycle modeling from design through operations combining accuracy and time-saving. It is being used by many researchers to simulate the gasification process of biomass and wastes [4,14].

Ultimately, the choice of the model largely depends on the targets and experimentally available information. We knew that Aspen Plus modeling involving an FBG could be difficult due to the complexity of the hydrodynamic liquefaction and the complex nature of the natural and chemical phenomena that occur within the FBG.

#### Hypotheses and Model Assumptions

The accuracy of simulation results strongly depends on the decisions and assumptions that have been made. Table 2 presents the assumptions made in this study for simulating the process of FBG.

#### **Table 2.** Model's hypotheses and assumptions.


#### *3.5. Choice of Processes*

The processes of this simulation concern pyrolysis, combustion, and gasification as well as the cleaning of the gaseous product. The first stage involves pyrolysis, which simulates the thermal decomposition of biomass before oxidation (i.e., the gasification zone of the gasifier, where the biomass is broken). The pyrolysis process is achieved at high temperatures around 500 ◦C and its goal is the conversion of biomass from nonconventional to simple components (H2, CO, CO2, CH<sup>4</sup> and H2O). The second stage concerns combustion and gasification (i.e., the combustion zone and gasification zone of

the gasifier, where the conventional components react with the gasification agent to further oxidation–reduction reactions after preliminary gasification).

For the evolution of temperature, we used information provided by the experimental study. First, the fuel was loaded into the reactor from the top at room temperature, while the gasification agent was introduced from the bottom of the reactor at ambient temperature if it was air and at above 100 ◦C if it was steam. As biomass moves downwards, it is subjected to cracking, carried out at a temperature up to 500 ◦C. Then, the gasification stage takes place, in a temperature range of 550–900 ◦C. The combustion products introduced into the reactor in the oxidation zone can rise the temperature up to 1100 ◦C for the need of breaking down the heavier hydrocarbons and tar of the syngas. As these products move downwards, they enter the reduction zone where a production gas is formed by the action of carbon dioxide and water vapor. Hot and dirty gas passes through a system of refrigerators, cleaners, and filters before being sent to engines, as it is the standard way [15].

In the present simulation in the first-round calculations, air was used as the gasification agent, the oxygen of which, in combination with the high temperature, leads to combustion. At the same time, the remaining conventional components and combustion products were led to the gasification stage where the achieved temperature was above 700 ◦C. In this process, reactions such as the methane reforming reaction (MSR) and the water–gas displacement reaction (WGS) play an important role in the production of the high-value gas product based on the Gibbs free energy minimization principle.

The final stage involves wet cleaning through cooling water and the separation of clean gas and unwanted liquid products.

#### *3.6. Choice of Reactions System*

Biomass contains carbon, hydrogen, and oxygen as the main chemical components. Therefore, it can be represented by the molecular formula CxHyOz which can be quantified by the final analysis, where x, y and z represent the elemental fractions C, H, and O, respectively. RH molecular form is described as CHαhOβ<sup>h</sup> (SiO2)δh, where ah, bh, dh and a, b, d were calculated by the analysis of RH from Table 1.

We also assumed that the RH char has the chemical formula CHαOβ(SiO2)δ.

The homogeneous and heterogeneous chemical reactions that we considered to occur in the gasification process are shown in Table 3. The number next to the reactions indicates the order in which they are performed. Hydrogen and carbon in combustion reactions (R-3, R-2), as well as water–gas in displacement and methanization reactions (R-7, R-4) are all exothermic and ideally provide the system with the required energy. On the other hand, steam reforming, Boudouard and water–gas shift reactions (R-6, R-8, R-5) are endothermic and their effect on gasification products becomes more apparent at high temperature.


**Table 3.** Reactions used in simulation with Aspen Plus.

This simulation involves 3 stages:


❖ In fact, there is another preliminary stage before cracking, which is the drying phase to reduce the raw material moisture content below 10 wt.%., depending on the moisture content of the raw material. We neglected the drying stage in this study because RH' moisture content is 9.5 wt.% < 10 wt.% [6].

We also considered the process of gasification to take place at the atmospheric pressure that is the most common [16].

)

#### CHahOβh(SiO<sup>2</sup> ) *3.7. Choice of Reactor Blocks*

δh → CHaO<sup>β</sup> (SiO<sup>2</sup> δ + volatiles H<sup>ଶ</sup> + 0.5O<sup>ଶ</sup> ↔ HଶO CH<sup>ସ</sup> + HଶO ↔ CO + 3H<sup>ଶ</sup> CO + HଶO ↔ CO<sup>ଶ</sup> + H<sup>ଶ</sup> CHahOβh(SiO<sup>2</sup> ) δh → CHaO<sup>β</sup> (SiO<sup>2</sup> ) δ + volatiles H<sup>ଶ</sup> + 0.5O<sup>ଶ</sup> ↔ HଶO CHahOβh(SiO<sup>2</sup> ) δh → CHaO<sup>β</sup> (SiO<sup>2</sup> ) δ + volatiles The simulation of the gasification reactor was performed in Aspen Plus software with the array of 2 reactors, each of which had a separate use which at the same time led to the result. For a multi-phase or multi-action system such as RH gasification, which involves multiple decompositions, combination, and adverse reactions, it is recommended to use the type of Gibbs reactor (R-Gibbs) created in Aspen Plus required to solve all of them to predict equilibrium compositions. This type of reactor is based on minimizing the total Gibbs energy of the mixture products and allows control and transport.

CHୟOஒ(SiOଶ)<sup>ஔ</sup> <sup>+</sup> γΟଶ ↔ ቂ2 − 2γ − β + <sup>α</sup> 2 ቃ CO + ቂ2γ + β − <sup>α</sup> 2 ቃ CO<sup>ଶ</sup> + ቀa 2 ቁ HଶΟ + ash CHୟOஒ(SiOଶ)ஔ + [(ସିୟାଶୠ) ଶ ]H<sup>ଶ</sup> ↔ CH<sup>ସ</sup> + βHଶO + ash CHୟOஒ(SiOଶ)ஔ + (1-β) HଶΟ ↔ CO + ቂ1 − β + ቀ ଶ ቁቃ Η<sup>ଶ</sup> + ash CHୟOஒ(SiOଶ)<sup>ஔ</sup> + CO<sup>ଶ</sup> <sup>↔</sup> 2CO + βHଶΟ + ቂቀ ଶ ቁ − βቃ Η<sup>ଶ</sup> + ash CH<sup>ସ</sup> + HଶO ↔ CO + 3H<sup>ଶ</sup> CO + HଶO ↔ CO<sup>ଶ</sup> + H<sup>ଶ</sup> CHୟOஒ(SiOଶ)<sup>ஔ</sup> <sup>+</sup> γΟଶ ↔ ቂ2 − 2γ − β + <sup>α</sup> 2 ቃ CO + ቂ2γ + β − <sup>α</sup> 2 ቃ CO<sup>ଶ</sup> + ቀa 2 ቁ HଶΟ + ash CHୟOஒ(SiOଶ)ஔ + [(ସିୟାଶୠ) ଶ ]H<sup>ଶ</sup> ↔ CH<sup>ସ</sup> + βHଶO + ash CHୟOஒ(SiOଶ)ஔ + (1-β) HଶΟ ↔ CO + ቂ1 − β + ቀ ቁቃ Η<sup>ଶ</sup> + ash H<sup>ଶ</sup> + 0.5O<sup>ଶ</sup> ↔ HଶO CH<sup>ସ</sup> + HଶO ↔ CO + 3H<sup>ଶ</sup> CO + HଶO ↔ CO<sup>ଶ</sup> + H<sup>ଶ</sup> CHୟOஒ(SiOଶ)<sup>ஔ</sup> <sup>+</sup> γΟଶ ↔ ቂ2 − 2γ − β + <sup>α</sup> 2 ቃ CO + ቂ2γ + β − <sup>α</sup> 2 ቃ CO<sup>ଶ</sup> + ቀa 2 ቁ HଶΟ + ash Since R-Gibbs cannot handle non-conventional components such as RH, in the case that some electricity or heat is needed, this can be inserted into the R-Yield block. In this block, RH is converted into a system of equivalent environmental components at the same levels of enthalpy. This current, generated after R-Yield, in combination with the air required for partial combustion and gasification, is directed to the R-Gibbs block to produce the products of the gasification reactions. The R-Gibbs subunit calculates adiabatic reactivity temperatures, such as the equilibrium component (estimated using Gibbs free energy minimization). The R-Gibbs calculation subunit can also be used when one or more reagents are not fully involved in equilibrium conditions. This is achieved by specializing in the extent of equilibrium for the ingredients.

ଶ CHୟOஒ(SiOଶ)<sup>ஔ</sup> + CO<sup>ଶ</sup> <sup>↔</sup> 2CO + βHଶΟ + ቂቀ ଶ ቁ − βቃ Η<sup>ଶ</sup> + ash CHୟOஒ(SiOଶ)ஔ + [(ସିୟାଶୠ) ଶ ]H<sup>ଶ</sup> ↔ CH<sup>ସ</sup> + βHଶO + ash CHୟOஒ(SiOଶ)ஔ + (1-β) HଶΟ ↔ CO + ቂ1 − β + ቀ ଶ ቁቃ Η<sup>ଶ</sup> + ash <sup>↔</sup> 2CO + βHଶΟ + ቂቀ In the case of the gasification of RH where there are adiabatic conditions, the equilibrium of the composition of the product provided by R-Gibbs depends on the flow rates, composition, and temperature of the surface materials (rice husk and air) supplied to the gasifier. The reactor blocks are presented in Table 4.

ቁ − βቃ Η<sup>ଶ</sup> + ash


ଶ **Table 4.** Reactor models used in this simulation in Aspen Plus.

#### *3.8. Flow Sheet of Air Gasification*

CHୟOஒ(SiOଶ)<sup>ஔ</sup> + CO<sup>ଶ</sup>

In the software, the biomass supplied to the gasifier is characterized by the ultimate and proximate analysis and not by its chemical formula, as it is classified as non-

conventional. The HCOALGEN and DCOALIG tool was used, for the the final analysis and sulfur analysis, to calculate the lowest heating value (LHV), the enthalpy calculation (HCOALGEN) and the density (DCOALIG) of the biomass (non-conventional component). The Peng Robinson equation was used to estimate all the physical properties of conventional components produced by the gasification process.

The R-Gibbs block calculates the equilibrium of the chemical equilibrium and the phase by minimizing the Gibbs free energy of the system. Before feeding the biomass to the R-Gibbs block, it must be decomposed into conventional elements using the R-Yields reactor. Thus, the R-Gibbs block was used to precisely simulate oxidants and reduction zones in the gas reactor. A mixer block was used to mix the products of the R-Yield reactor (Decomp) with the flow of air, in a sub-stoichiometric quantity, before entering the R-Gibbs block.

Figure 1 presents a comprehensive Aspen Plus flow sheet for the fluidized-bed gasification process, while Table 5 descripts the Aspen Plus reactor blocks considered in the model.

**Figure 1.** Comprehensive Aspen Plus flow sheet for the fluidized-bed air gasification.



The performance of the individual products of the R-Yield block can be estimated using a "block calculator", i.e., a subroutine written in Fortran language defined by the user to estimate the performance of volatility products based on the final and immediate analysis of the biomass, or with approximate models of the reactions that take place during the firing stage and are processed in Excel software.

#### *3.9. Energy Balances*

We considered that the mixing ratio is high due to the fluidized bed gasifier, and that removal, combustion, and all gasification processes occur at a high rate, at the operating temperature. Therefore, the mass balance can be given by the Equation (1), considering the Gibbs free energy minimization concept.

$$\begin{aligned} \left. \mathrm{Q}\_{\mathrm{C}}(\mathrm{T},\mathrm{P}) \right|\_{\mathrm{combustion}} &= -\mathrm{Q}\_{\mathrm{C}}(\mathrm{T},\mathrm{P})|\_{\mathrm{heat loss}} + \left. \mathrm{Q}\_{\mathrm{H}}(\mathrm{T},\mathrm{P}) \right|\_{\mathrm{rise \\_hths}} + \left. \mathrm{Q}\_{\mathrm{O}}(\mathrm{T},\mathrm{P}) \right|\_{\mathrm{oxidant}} + \left. \mathrm{Q}\_{\mathrm{d}}(\mathrm{T},\mathrm{P}) \right|\_{\mathrm{dry}} \\ &+ \left. \mathrm{Q}\_{\mathrm{dv}}(\mathrm{T},\mathrm{P}) \right|\_{\mathrm{devolatility}} + \left. \mathrm{Q}\_{\mathrm{g}}(\mathrm{T},\mathrm{P}) \right|\_{\mathrm{gasification}} - \left. \mathrm{Q}\_{\mathrm{c}}(\mathrm{T},\mathrm{P}) \right|\_{\mathrm{deulration}} - \left. \mathrm{Q}\_{\mathrm{p}}(\mathrm{T},\mathrm{P}) \right|\_{\mathrm{quadult\\_gas}} \end{aligned} \tag{1}$$

where:

$$\left(d\mathbb{G}\_{system}\right)\_{T,P} = 0\tag{2}$$

$$nG = \Sigma \left( n\_i \Delta G\_{fi}^0 \right) + \left( \Sigma n\_i \right) RTlnP + RT\Sigma \left( n\_i \ln\_{ji} \right) + RT\Sigma \left( n\_i \ln\_{\Phi i} \right) \tag{3}$$

or

$$\min \frac{G}{RT} = \sum\_{i=1}^{N} n\_i \frac{\Delta G\_{f,i}^0}{RT} + \ln n\_i \sum n\_i \tag{4}$$

The term "elutriation" in Equation (1) refers to the separation of fine particles from smaller ones. It is important to include the contribution of the elutriation of fine particles from fluidized beds because it affects the residence time, conversion, and is used for reaction, drying and in energy balance.

The above Equation (4) is the total Gibbs equation (*nG*) that must be minimized depending on the composition of the individual compounds at the operating temperature and pressure. This depends on the constraints imposed by individual balances written on closed systems [4].

#### *3.10. Air Gasification Syngas Composition Estimation*

We based the estimation of syngas composition on the main reactions that took place during pyrolysis. Essentially, we followed the procedure described elsewhere [17] but with a consideration of carbon efficiency close to 100% (YC = 0).

Therefore, based on the proportions resulting from the above reactions, the simplified yields of the conventional products follow the relationships:

$$\mathbf{Y\_{CO2} = 2 \ \* \ Y\_{CO}} \tag{5}$$

$$\mathbf{Y}\_{\text{CH4}} = \mathbf{0}.\mathbf{3} \,\,\mathbf{7} \,\,\mathbf{Y}\_{\text{H2}} \,\,\tag{6}$$

$$
\Upsilon\_{\text{char}} = 0.35\tag{7}
$$

$$\mathbf{Y}\_{\text{CxHy}} = \mathbf{0.03} \tag{8}$$

Combining the above and the using the *n* ∑ 1 Y = 1 subroutine, we calculated the yields (v.v%) of CO, CO2, CH4, H2, C2H4, C2H6, and H2O.

The flows of the material were as follows: The releases of biomass gas components such as CO, H2, CO2, CH4, N2, H2O, and O<sup>2</sup> are defined as routine components. Biomass is the non-conventional ingredient. The decomposition unit is very similar to the R-Yield performance reactor. In this section, the biomass is decomposed into some conventional solid elements, i.e., the gasification product in a simpler form of each element such as O2, H2, C, N2, and ash. The energy flow in this process is as follows: Some of the heat generated by the combustion of carbon is the heat loss of the whole system, and some flows to the pyrolysis reactor. The rest of the heat is provided by the gasification reaction to create gas. In the cracking unit, the carbon conversion ratio is 99%, i.e., it approaches 100% in the gasifier.

In the R-Gibbs reactor, chemical equilibrium reactions have been tested to represent the gasification process, namely the methane reforming reaction and the water–gas displacement reaction:

Methane reforming reaction (R-6): CH<sup>4</sup> + H2O <-> CO + 3 H2, ∆H = 206 kJ/mol (9)

$$\text{Water-gas reaction (R-7): CO} + \text{H}\_2\text{O} < \text{>CO}\_2 + \text{H}\_2, \Delta\text{H} = -40 \text{ kJ/mol} \tag{10}$$

The methane reforming reaction is a chemical reaction that converts methane into carbon monoxide and/or hydrogen. WGS converts CO and H2O to extra H<sup>2</sup> and carbon dioxide, as the reaction does not change linear sets and therefore the effect of the pressure on the reaction is minimal.

Assessing tar and char yields is a difficult task through a thermodynamic equilibrium model because tar is usually a non-equilibrium product. Since the predictions of mathematical models are substantially improved when tar formation is included, in this study, tar and

carbon yields were considered as input parameters and were determined independently of the gasifier operating conditions, according to other bibliographic models. Thus, they were placed as inert ingredients in the R-Gibbs reactor. At high temperatures, such as those examined, the tar content is very moderate, while the gas efficiency is very high.

Tar was described as "C6H6" with the same thermochemical properties of benzene, while char was defined as carbon with the thermochemical properties of graphite.

#### **4. Indicators for Monitoring and Assessing of FBG System**

In order to be able to derive reliable and comparable results of the three scenarios, certain indicators must first be defined in addition to the gasification temperature, characterized as active evaluation indicators. Parameters such as temperature (T), equivalence ratio (ER), and biomass vapor ratio (SBR) are suitable for the synthesis of syngas, as well as the lowest heating value (LHV). The indicators used for the sensitivity analysis in this study are:


#### *4.1. Air–Biomass Equivalence ratio (ER)*

In this simulation, the gasification process was investigated by changing the air flow and consequently oxygen, which affects the ER equivalence ratio that is the main operating parameter. ER is defined as the air-to-biomass weight, relative to the stoichiometric airto-biomass weight required for complete combustion. The ratio of air used in the system to the stoichiometric required air (ratio of air equivalence ratio) is an important factor to consider. Its wise choice discourages the stimulation of oxidation reactions. The reason for combustion equivalence is the ratio between the available oxidizer and the stoichiometric quantity required for the complete reaction. It will have a value of 1 for full combustion and 0 for pyrolysis, while the appropriate values fall in the range 0.19–0.43. Oxygen availability, both as a free molecule and as a percentage in the water molecule, is a key factor in gasification. ER is defined by the following equations [14]:

$$\text{ER}(\text{O}\_2) = \frac{\frac{\text{air used} \text{[kg]}}{\text{fòrmas used} \text{[kg]}}}{\frac{\text{sútheonentially demanded} \text{ } \text{air} \text{[kg]}}{\text{fòrmas used} \text{[kg]}}} = \frac{\text{actual air to biomass ratio}}{\text{sútheinometric air to biomass ratio}} = \frac{\text{feed O}\_2 \left| \frac{\text{kg}}{\text{s}} \right|}{\text{flow of O}\_2 \text{ for complete combustion} \left| \frac{\text{kg}}{\text{s}} \right|} \tag{11}$$

#### *4.2. Steam to Biomass Ratio (SBR)*

In a steam gasification scenario, steam is used as the oxidizing agent instead of air. In this case, the steam-to-biomass ratio (SBR) is used as the ratio between the flow rate of the incoming steam to the flow rate of the biomass fed, as can be seen the following equation [18]:

$$\text{SBR} = \frac{\text{steam mass flow} \,\left(\text{kg}\right)}{\text{biomass feed rate} \left(\text{kg}\right)} \tag{12}$$

The biomass feed rate is maintained as constant while the steam flow is varied. Therefore, it is clear to foresee that above the SBR optimum range, the gas yield, LHV, and carbon conversion efficiency will tend to decrease because high amounts of unreacted H2O will appear in the syngas, causing thermal efficiency to decline significantly. The optimum range of SBR is 0.2–0.4, based on the bibliography [14].

#### *4.3. Lower Heating Value (LHV)*

The lower heating value (LHV) is defined as the net calorific value and is determined by subtracting the heat of vaporization of water vapor. The main research goal is to produce gas enriched in CO, H2, and CH<sup>4</sup> because the presence of these fuels leads to gas of high heating value, suitable for further exploitation in internal combustion engines and turbines for power generation. The lowest heating value (LHV) of the produced gas is calculated using the following equation [14]:

$$\text{LHV}\_{\text{syn}} = \frac{\left(30 \ast \text{X}\_{\text{CO}} + 25.7 \ast \text{X}\_{\text{H}\_2} + 85.4 \ast \text{X}\_{\text{CH}\_4}\right) \ast 4.2}{1000}, \text{ MJ/Nm}^3 \tag{13}$$

where XCO, XH2, and XCH4 are the linear fractions of the gaseous products in syngas.

#### *4.4. Cold Gas Efficiency (CGE)*

CGE is the key index that measures the global performance of the gasification process. It is defined based on the first law of thermodynamics as the ratio between the chemical energy of raw syngas (calculated as the product of syngas mass flow and its lower heating value) and the chemical energy of RH feedstock. Therefore, CGE is the energy output over the potential energy input (chemical energy contained in the product gas with respect to the energy contained in the initial solid fuel) based on the LHV of both the solid fuel and the product gas. The CGE indicates the percentage of energy content of RH inherited from the syngas and can be calculated from the following equation [18]:

$$\text{CGE} = \frac{\text{LHV}\_{\text{gas}} \ast \text{V}\_{\text{gas}}}{\text{LHV}\_{\text{b}} \ast \text{F}\_{\text{b}}} \tag{14}$$

where LHVgas is the producer gas's lower heating value, Vgas is the volume of produced gas and LHV<sup>b</sup> is the lowest heating value of rice husks which is equal to 7.13 MJ/Nm<sup>3</sup> . F<sup>b</sup> is the RH feed.

#### *4.5. Carbon Conversion Efficiency (CCE)*

Carbon conversion efficiency (CCE) expresses how much of the natural carbon from biomass waste is transported to the gas produced. The equation used to calculate CCE is shown below [18]:

$$\text{CCE} = \frac{\text{total carbon outlet syngs} \ast 100}{\text{carbon in biomass feed}} = \frac{[12 \ast \text{V}\_{\text{gas}}(\text{CO}\% + \text{CO}\_2\% + \text{CH}\_4\% + 2 \ast \text{C}\_2\text{H}\_X\%) \ast 100\%}{\text{C}\_6\% \ast 22,4} \tag{15}$$

where Vgas is the volume of produced gas, CO *v*/*v*% the volume percentage of gaseous species in the producer gas and C<sup>b</sup> is the wt.% of carbon in the RH feed.

#### **5. Results: Model Validation with Experimental Results (ERes)**

The simulation results (SRes) for the FBG gasifier model were validated through comparisons with experimental data from one previous study [6]. In order to be able to compare data obtained from the simulation of Aspen Plus, the gasification conditions of the experiments must first be provided. In each gasification cycle in the experimental study, 5 g of biomass of rice husk biomass were fed to the gasifier, so a 0.005 t/cycle. Additionally, as a gasification agent, the air under stoichiometry of 10/90 *v*/*v*% (O2/N2) with a flow of 200 mL/min was used and the residence time in the gasifier was 32 min on average [14]. Therefore, with simple calculations for each gasification cycle, 0.11 L/cycle of the gasification agent was estimated.

Table 6 shows the simulated results (SRes) of the syngas composition for three air gasification temperatures (T = 700, 800, 900 ◦C) and for various experimental results (Eres).


**Table 6.** Simulated results (SRes) of the syngas composition with temperature and experimental results (ERes).

The ER value is directly related to the oxygen/air content in the gasifier, and if it is high, it can turn the gasification process towards combustion as Table 6 shows. Higher ER values lead to a decrease in syngas heating value and in the higher conversion of H–Cs to CO and CO2, a decrease in tar yield and CH<sup>4</sup> content in the syngas. Increasing temperature increases H<sup>2</sup> production in product gas due to the gasification of char and methane reforming reactions (Figure 2). Figure 2a–d compare the SRes with ERes of syngas composition in CO, CO2, CH<sup>4</sup> and H<sup>2</sup> in function of the air gasification temperature.

As it can be noticed in Figure 2, SRes show good accuracy in the prediction of H<sup>2</sup> but not for CO, CO<sup>2</sup> and CH4, content. This indicates that the model needs modification to improve the accuracy of prediction.

This can be attributed to the fact that the RGIBBS reaction simulates better an entrained flow gasifier and not so well a fluidized bed gasifier, for which a semi-empirical model might fit better than the RGIBBS reaction. For a more detailed calculation of the difference between SRes and ERes, Table 7 presents the calculated deviations by using the following equation: % deviation = [(SRes) − (ERes)/(ERes)] × 100.


**Table 7.** Deviation between the simulated (SRes) with experimental results (ERes) at 900 ◦C with ER = 0.3 (air gasification)

**Figure 2.** Comparison of simulated and experimental data: effect of air gasification temperature on syngas composition: (**a**) CO; (**b**) CO<sup>2</sup> ; (**c**) CH<sup>4</sup> ; and (**d**) H<sup>2</sup> .

As it can be noticed in Table 7, deviations vary from −60.0 to +76.2. These are the lower and upper deviations that mainly occur in the case of CH<sup>4</sup> and consequently to the cold gas efficiency (CGE). While experimentally it appears that in the syngas there is a certain amount of methane, this is not the case in the simulated results. This devaluation of CH<sup>4</sup> is due to the minimization of Gibbs energy and the ideal chemical equilibrium reactor that were hypothesized in the simulation, which do not occur in real commercial gasification systems.

Additionally, the SMARt-CHP technology that was considered in this study for the experimental results produces tar and hydrocarbons (mainly methane), components that were neglected in the equilibrium-based predicted model.

Similarly, some small differences in the composition of the gaseous products are due to the consideration of the R-Yield reactor to simulate gasification in Aspen Plus. The RGIBBS reaction is rather closed to the entrained flow gasifier and not to the fluidized bed gasifier, for which a semi-empirical model might fit better than the RGIBBS reaction.

#### **6. Sensitivity Analysis for Monitoring and Assessment by Using Indicators**

A sensitivity analysis was conducted to monitor and assess the studied system by using the indicators described in the previous chapter.

#### *6.1. Effect of Equivalence Ratio (ER) and Gasification Temperature on Syngas Composition*

During gasification, emphasis is placed on the maximizing gas efficiency to produce a gas with an HHV to be efficient and used to generate electricity. Two parameters are the main ones that affect the efficiency and composition of the gas:


If a high ER is used, the syngas content on CO, H<sup>2</sup> and CH<sup>4</sup> decreases with a higher ER (Figure 2); and the gas LHV decreases. At the same time, increasing the ER allows to

increase the temperature of the reactor, promoting a higher flow through the reactor, and reducing the tar content in the syngas. According to the above figure, the concentration of CH<sup>4</sup> in syngas decreases dramatically with increasing ER. The CO and H<sup>2</sup> content decreases with very high ER. For the above reasons, an accurate choice of the two parameters is necessary to optimize the process.

At the Aspen Plus simulation of air gasification, the ER ratio was set at 0.3 to achieve a syngas with a high heating value. Figure 3 depicts the effect of ER on the composition of the syngas simulated results (SRes).

**Figure 3.** Effect of ER on the composition of the syngas simulated by Aspen Plus.

Based on the results from the above ER comparison chart, it is observed that for ER > 0.3, carbon dioxide (CO2) increases sharply, which is not desirable. Therefore, in the case of rice husk air gasification, an ideal ER ratio is proved to be 0.3 (quite close to the bibliographic one which is 0.27). It is obvious that for a given temperature, the increase in air (higher ER) leads to a decrease in the final efficiency of syngas.

Further increase in ER leads to reductions in CO and H<sup>2</sup> concentrations, which is probably due to the favorable combustion reaction. The CO<sup>2</sup> concentration increases sharply with the increase in ER due to complete combustion and reaches a value in the range of 20–30% at ER = 1. The change in the concentration of CH<sup>4</sup> with an increase in ER is considered negligible. Further increases in ER were found to lead to reductions in CO and H<sup>2</sup> volume fractions due to combustion reactions. LHV increases with increasing ER to the value in the range of 0.35 and then begins to decrease dramatically. In conclusion, ER had the opposite effect on LHV from temperature, i.e., higher ER reduced LHVgas due to the oxidation of part of the gaseous gases present in the syngas.

Moisture content (MC) of biomass affects the efficiency of the gasification process. It is known that the high content of MC is responsible for reducing H<sup>2</sup> and CO in gas production and increasing CO2. As a result, the heating value of syngas decreases while the MC increases. For this reason, in this simulation, the biomass of rice husks with moisture content below 10% was used as a raw material.

The gasification unit was simulated in Aspen Plus software in the temperature range of 700–900 ◦C, with an air gasification agent and with stoichiometry (10/90 *v*/*v*% O2/N2). This temperature range was implied by the experimental data because the process was studied in the temperature range of 550–900 ◦C to optimize the syngas quality. The ER was set to 0.3, a value set in the experimental study. The effect of temperature on the quality and the energy efficiency of the syngas was studied in Aspen Plus software and is shown in Figure 4.

**Figure 4.** Effect of temperature on the syngas composition simulated results of air gasification of rice husks in Aspen Plus (free of H2O and N<sup>2</sup> ).

The temperature of the gasifier affects the overall composition of the final product as shown in the diagram above. This is because some of the related chemical reactions that take place inside the gasifier are endothermic. Higher temperatures favor endothermic reaction products according to Le Châtelier's principle. Temperature promotes the formation of a gas produced with higher H<sup>2</sup> and CO contents and therefore higher LHV. On the other hand, the content of CH<sup>4</sup> and CO<sup>2</sup> follows an opposite trend. CH<sup>4</sup> decreases with temperature because the methane reaction formation is exothermic.

According to the above Figure 4, CO<sup>2</sup> follows a downward trend until it is eliminated as the gasification temperature increases, in contrast to CO, which while initially having a lower composition than CO2, follows an upward trend reaching very high percentages. H<sup>2</sup> shows a relatively small increase and stabilizes at 20 *v*/*v*% from 750 ◦C onwards. CH<sup>4</sup> shows a very downward trend and stabilizes at 6 *v*/*v*% from 750 ◦C onwards. Finally, it should be noted that the remaining hydrocarbons (C2H<sup>4</sup> and C2H6) in the whole range of temperatures have a composition below 1 *v*/*v*%.

The reduction in CO<sup>2</sup> concentration could be attributed to the Boudouard reaction which takes place at a higher temperature range compared to the water–gas shift reaction. Therefore, CO production and CO<sup>2</sup> consumption are preferred. In addition, methane reforming reactions affect the CH<sup>4</sup> concentration which is reduced to a higher gasification temperature. The bottom line is that the produced gas from the simulation of the Aspen Plus gasification unit is rich in CO and H2, but poor in CH<sup>4</sup> and CO2.

The molecular weight of the produced gas is 22.

#### *6.2. Effect of Gasification Temperature on Syngas Low Heating Value (LHV)*

It was observed that this LHV of the syngas stabilized at 13 MJ/Nm<sup>3</sup> from 850 ◦C onwards. It is considered that at 850 ◦C, the gasifier reaches the highest fuel conversion. During these calculations, the LHV values of the syngas at 700, 800, and 900 ◦C were taken to be around 14.5, 13.0 and 13.0%, respectively.

#### *6.3. Effect of Gasification Temperature on Cold Gas Efficiency (CGE)*

CGE indicates the percentage of energy content of RH transferred in the gas product. CGE for all raw materials is directly proportional to the gasifier temperature according to the definition and Equation (15). However, gas LHV decreases with temperature, and CGE is higher at a higher gasification temperature due to the increased volume of gas product. The CGE from the rice husks is maximized at 850 ◦C where the gasifier reaches the highest fuel conversion. During these calculations, the CGE values of the FBG gasifier at 700, 800,

and 900 ◦C were taken to be 85.0, 92.0, and 90.0%, respectively. The high CGE suggests that the coke is cracked.

#### *6.4. Effect of Gasification Temperature on Carbon Conversion Efficiency (CGE)*

Carbon conversion efficiency (CCE) expresses how much of the natural carbon from rice husk waste is transported to the produced gas. The maximum efficiency of carbon conversion (CCE) is at 900 ◦C, where the gasifier reaches the highest fuel conversion, and it is equal to 22%, while at 850 ◦C it reaches the 21% conversion. During these calculations, the CCE values of the FBG gasifier at 700, 800, and 900 ◦C were taken to be 16.5, 20.0, and 22.0%, respectively.

#### *6.5. Study of Alternative Gasification Scenarios*

The oxidizing agent has a significant effect on the heating value syngas produced. However, the main scenario studied was that of air gasification, and simulations of other two scenarios were attempted by using all the same hypotheses and conditions whilst only changing the gasification agent. Thus, the second scenario simulated was the steam gasification in the R-Gibbs reactor and the third scenario was the (air + oxygen) gasification.

The flow sheet of the steam gasification scenario is presented in Figure 5. The flow sheet of the (steam + oxygen) gasification is depicted in Figure 6.

**Figure 5.** Aspen Plus flow sheet for steam FBG scenario.

**Figure 6.** Aspen Plus flow sheet of (oxygen + steam) FBG scenario.

The results of the second alternative scenario calculated by the Aspen Plus worksheet are shown in Table 8 with respect to SBR or ER, LHV and CCE indicators.


**Table 8.** Effect of steam-to-biomass ratio (SBR) and gasification temperature on the syngas composition simulated results (SRes) by Aspen Plus.

As can be noticed in the case of steam gasification, there is a larger initial amount of methane (CH4) and less carbon monoxide (CO) compared with the results of air gasification as presented in Table 6. Finally, based on Table 8, there is an improvement in the composition of the gaseous product to the SBR = 0.5 and then as the SBR increases, the carbon dioxide increases, which is not desirable. Therefore, in the case of this study, the ideal value for the SBR was calculated as 0.4 (quite close to that of the literature which is 0.35).

The use of steam as a gasifier increases the partial pressure of H2O in the gas reactor that favors water–gas, water–gas displacement and vapor reactions, leading to an increase in H<sup>2</sup> and CO<sup>2</sup> and a decrease in CO production as SBR increases. The heating value and hydrogen content of syngas are generally higher when the gasification of RH occurs with steam than when it occurs with air. However, based on Table 8, the results are almost similar to those presented in gasification with air factor (Table 6).

Both gasification agents (air and steam) are efficient with the only difference in the case of air, however, it is a cheap agent as opposed to the steam and steam gasification needs more energy to turn water into steam to be used in the process, although in the case of steam gasification, syngas has a higher H<sup>2</sup> composition, resulting in higher LHV value.

Regarding the third alternative scenario of gasification with (steam + oxygen), it can be noticed that the best results are derived when ER = 0.3 and SBR = 0.4.

The comparison of the scenarios based on the syngas composition is depicted in Table 9.

**Table 9.** Composition for syngas of (steam + oxygen) gasification for ER = 0.3 and SBR = 0.5 at 900 ◦C.


Based on Table 9, for the gasification of RH with (oxygen + steam), the results regarding the composition of syngas were slightly better than those of gasification with air (Table 6), but worse than those of steam gasification (Table 8). This can be explained by the fact that the use of the agent (oxygen + steam) reduces the residence time of the air inside the reactor, preventing the continuous reactions in the gasifier from achieving the chemical equilibrium of a substance undertaken by the model.

However, (steam + oxygen) gasification needs an external energy source to maintain the reaction temperature, while oxygen and air are used in direct gasification because the oxidation reactions provide the energy required to sustain the temperature of the reaction. Nonetheless, oxygen is the best gasifying agent, though using oxygen is more costly and there is a risk that the gasification process may shift to combustion.

Therefore, the feasibility study that was conducted and is presented in the next chapter is the scenario of the assessment of its economic viability.

#### **7. Feasibility Study**

The Aspen Process Economic Analyzer was used for economic assessment. The SMARt-CHP characteristic values were used for the economic assessment. The cost of transportation and the price of RH was considered to be zero because it is hypothesized that the CHP unit will serve as a waste management solution for the rice processing company.

The Greek rice type "Agrino" is produced by the homonymous company which is the largest rice producer in Greece (5 t/hr). This production accounts for an RH production of approximately 20 wt.% of the total paddy weight (whole grain).

#### *7.1. Fixed Investment Calculation (IF)*

The first step in calculating the fixed investment is to calculate the cost of mechanical equipment. Based on the calculation by the Aspen Process Economic Analyzer, the cost of equipment amounts to USD 2,279,490 = EUR 2,101,735 for the scenario of air gasification and for a capacity of 25,000 t/y rice husks (USD/EUR = 1.084).

Based on the cost of purchased equipment, the amount of fixed investment of the facility was obtained. Using the estimation method based on the cost of procurement of mechanical equipment, the amount of the fixed investment was calculated. All individual costs are expressed as a percentage of the value of the mechanical equipment and represent average values for standard chemical installations [19]. Table 10 shows fixed investment analysis using the Aspen Process Economic Analyzer. In Table 10, the percentage of the land purchasing was intentionally omitted (6%) because the gasification unit was installed in the yard of the rice processing agro-industry.


**Table 10.** Fixed investment (IF) analysis based on Aspen Process Economic Analyzer.

**Table 10.** *Cont.*


*7.2. Operating Costs Estimation*

Summing up all the individual expenses together with some additional ones, the total annual operating costs of the unit were obtained, as shown in the following Table 11.

**Table 11.** Total annual operating cost analysis (C).


\* Utilities: (a) 106,488 l/y air for the gasification; (b) 12,530,304 Whel/ y electricity for the operation of the gasification; and (c) 25,229 t/y water for the gasification products cooling.

The labor cost was calculated by using the Wessel Equation (19):

$$\frac{\text{Manhours}}{\text{days} \times \text{stages}} = \alpha \ast \left(\frac{\text{t product}}{\text{d}}\right)^{0.24} \tag{16}$$

where α is a coefficient depending on the type of unit.

The following hypotheses were made:


#### ❖ *7.3. Annual Sales Profits*

❖ The unit makes a profit on the one hand from the sale of electricity and heat, whilst on the other hand from the char. According to technology chosen, for an FBG unit for CHP and capacities of 100 kg/h, the energy produced is equal to 1.1–1.2 kWh for every 1 kg/h of power, regardless of the type of biomass. Thus, in our case of RH, the energy produced is set at 1.1 kWh for every 1 kg/h of RH gasified.

 CHahOβh(SiO<sup>2</sup> ) δh → CHaO<sup>β</sup> (SiO<sup>2</sup> ) δ + volatiles CHahOβh(SiO<sup>2</sup> ) δh → CHaO<sup>β</sup> (SiO<sup>2</sup> ) + volatiles In the simulation performed, the capacity was 25,000 t/y RH, so by simple calculations, the generated energy was equal to around 99,000,000 kWh. From the produced energy, 28% was electricity and 72% thermal energy. Therefore, finally, 27,720,000 kWhel and 71,280,000 kWth will be produced by the gasification simulation unit and be sold as commodities.

H<sup>ଶ</sup> + 0.5O<sup>ଶ</sup> ↔ HଶO CH<sup>ସ</sup> + HଶO ↔ CO + 3H<sup>ଶ</sup> δ H<sup>ଶ</sup> + 0.5O<sup>ଶ</sup> ↔ HଶO CHahOβh(SiO<sup>2</sup> ) δh → CHaO<sup>β</sup> (SiO<sup>2</sup> ) δ + volatiles CHahOβh(SiO<sup>2</sup> ) δh → CHaO<sup>β</sup> (SiO<sup>2</sup> ) δ + volatiles The conversion of RH to char is equal to 35 wt.%. In the positive scenario of 25,000 t/y capacity, 8750 t/y char will be produced (0.35 \* 25,000 = 8,750 t/y) which can be sold or used as biochar. Table 12 shows cash inflows (S) generated by the unit from the sales of the commodities.


CO + HଶO ↔ CO<sup>ଶ</sup> + H<sup>ଶ</sup> H<sup>ଶ</sup> + 0.5O<sup>ଶ</sup> **Table 12.** Cash inflows (S).

> CHୟOஒ(SiOଶ)<sup>ஔ</sup> + CO<sup>ଶ</sup> <sup>↔</sup> 2CO + βHଶΟ + ቂቀ ቁ − βቃ Η<sup>ଶ</sup> + ash CHୟOஒ(SiOଶ)ஔ + (1-β) HଶΟ ↔ CO + ቂ1 − β + ቀ ଶ ቁቃ Η<sup>ଶ</sup> + ash CHୟOஒ(SiOଶ)ஔ + [(ସିୟାଶୠ) ଶ ]H<sup>ଶ</sup> ↔ CH<sup>ସ</sup> + βHଶO + ash + ቂ2γ + β − <sup>α</sup> 2 ቃ CO<sup>ଶ</sup> + ቀa 2 ቁ HଶΟ + ash CHୟOஒ(SiOଶ)ஔ + [(ସିୟାଶୠ) The gross income of the unit is calculated by using the equation:

]H<sup>ଶ</sup>

CH<sup>ସ</sup> + HଶO ↔ CO + 3H<sup>ଶ</sup>

↔ HଶO

↔ HଶO

H<sup>ଶ</sup> + 0.5O<sup>ଶ</sup>

$$\mathbf{R} = \mathbf{S} - \mathbf{C} \tag{17}$$

CHୟOஒ(SiOଶ)<sup>ஔ</sup> + CO<sup>ଶ</sup> <sup>↔</sup> 2CO + βHଶΟ + ቂቀ ଶ ቁ − βቃ Η<sup>ଶ</sup> + ash ଶ <sup>↔</sup> 2CO + βHଶΟ + ቂቀ The assumptions made to calculate the total net revenues (NRs), are the following:

ଶ

ଶ

ଶ

ቁቃ Η<sup>ଶ</sup> + ash

ቁ − βቃ Η<sup>ଶ</sup> + ash


▪ ▪

▪ ▪ Flat tax rate is t = 0.4.

▪

CHୟOஒ(SiOଶ)<sup>ஔ</sup> + CO<sup>ଶ</sup>

▪ ▪ ▪▪ Depreciation coefficient for tax purposes is d = 1/N = 0.1.

ଶ

▪ ▪ Depreciation coefficient for fixed capital is e = d.

▪ ▪ The total net revenue was calculated by using Equation (18):

$$\mathbf{P} = (\mathbf{R} - \mathbf{d} \text{ \* } \mathbf{I}\_F) \text{ \* } (1 - \mathbf{t}) = 2.483\text{.}398\text{ }\mathbf{6}\text{/}\text{y}\tag{18}$$

#### *7.4. Estimation of ROI and POT Indexes*

The ROI index expresses the performance in relation to the amount initially invested and is calculated by using the equation:

$$\text{ROI} = \frac{\text{P}}{\text{(I}\_{\text{F}} + \text{I}\_{\text{W}}\text{)}} = \text{0.21} \tag{19}$$

The POT economic index expresses the time required to equate finance with fixed investment capital and is calculated by using the equation:

$$\text{POT} = \frac{\text{I}\_{\text{F}}}{(\text{P} + \text{e} \ast \text{I}\_{\text{F}})} = 2.88 \tag{20}$$

The economic indicators are very positive.

#### *7.5. Range of Viable Capacity Estimation*

Based on the economic data and by using the ROI and POT indices, we can calculate the range in which the capacity of RH that is economically viable based on sensitivity analysis by Aspen Plus software and using as parameters the capacity, the fixed investment, occupational costs, utilities, and gross profit.

The only assumption we need to keep in mind is that the ROI must exceed 0.2 and the POT must never be lower than or exceed 3.63. For this reason, the Aspen Plus software performed a sensitivity analysis on the unit's bandwidth (if the gasification unit operates for 7000 h/y). Figure 7 depicts the evolution of economic indicators with the capacity.

In conclusion, the gasification system is viable at any capacity between 25,000 and 75,000 t/y. Comparing the economic simulation results of the three gasification scenarios based on different gasification agents, we found that although oxygen-steam gasification is the most favorable option for rich syngas production, the operating costs due to oxygen and high steam requirements, render the oxygen-steam gasification the less attractive economically scenario compared to the air-gasification.

**Figure 7.** *Cont*.

**Figure 7.** Evolution of the economic indicators (**a**) return of investment (ROI) and (**b**) pay out time (POT) with capacity.

#### **8. Discussion on Environmental Issues**

There are some concerns associated with RH gasification concerning the solid and gaseous by-products—mainly ash and carbon dioxide—derived from RH gasification. To overcome these challenges, we propose the following:

	- (1) Ash can be used as an insulating material due to its low thermal conductivity.
	- (2) Ash can be used as an adsorbent to extract various contaminants from water and air.

agglomeration problems, especially when silica interacts with silica sand beds (SiO2) usually used as catalytic material for tar cracking in the reactor [14].

	- ✔ To use tar cracking catalytic methods during or after the gasification process (in situ and off site);
	- ✔ To use a hot syngas cleaning method.
	- ✔ To use lower gasification temperatures to reduce the production of tar.

#### **9. Conclusions**

Modern agri-food industries face high energy bills and produce large quantities of residues, which could be utilized to provide added value at all levels (material, energy, environmental, economic). Gasification offers an attractive solution allowing the utilization of the waste' energy content to produce energy and fuels to be used on-site or sold to the grid.

A rice husk fluidized bed gasification for a combined heat and power production system of 25,000 t/y capacity enables decentralized energy production from agro-industrial wastes, offering to the agro-industrial sector a circular utilization of resources, and reduction in their environmental footprint. In this study, the assumptions used to simulate the air FBG of rice husks by Aspen Plus software played an important role in the extraction of the results. We assumed that all reactors operate at a constant temperature and the pressure profile and at chemical equilibrium conditions which is not theoretically possible in real reaction conditions. However, in real conditions, the heat loss is higher than the simulated one affecting the whole process energy balance. In addition, for simplification, tar was not considered in the model.

Simulated results show good accuracy in the prediction for H<sup>2</sup> but not for CO, CO<sup>2</sup> and CH<sup>4</sup> content. This indicates that the model needs modification to improve the accuracy of prediction. The results of air gasification showed a deviation from the experimental results varying from −25% to +33%. In general, the deviations in the quantities of the gas components and in the values of LHV, CGE and CCE indicators are not prohibitive. The largest deviations concern the yield of CH<sup>4</sup> and the CGE. The limitations of our model were in assessing tar and char yields, which is a difficult task through a thermodynamic equilibrium model because tar is usually a non-equilibrium product. Since the predictions of mathematical models are substantially improved when tar formation is included, in this study, tar and carbon yields were considered as input parameters and were determined independently of the gasifier operating conditions, according to other bibliographic models. Thus, they have been placed, as inert ingredients, in the R-Gibbs reactor. At high temperatures, such as those examined, the tar content is very moderate, while the gas efficiency is very high. Another reason for the fact that simulated data do not fit very well with the experimental results might be attributed to the fact that the RGIBBS reaction is rather closed to the entrained flow gasifier and not to the fluidized bed gasifier, for which a semi-empirical model might fit better than RGIBBS reaction.

Among the three scenarios examined, the scenario of gasification with steam and oxygen gives a syngas with higher H<sup>2</sup> content resulting in a higher LHV value. However, although, this is a more favorable result, the high thermal requirements of the steam increase the operating cost.

The Aspen Process Economic Analyzer was used for the economic assessment. The ROI and POT were very positive (0.21 and 2.8, respectively), for the case of air gasification with a capacity of 25,000 t/y.

Simulation modeling (SM) and the economic assessment of the planning phase of a gasification system had an increasingly important role in the design of and optimization of the processes and to give an idea of the economic viability of the industrial application, guiding the investors to decide. The main advantages of using SM as a customizable tool to help decision-making is that it makes it possible to analyze how the key process indicators affect the viability of a bioenergy system, without the need to spend more money and time on experimental demonstration.

**Author Contributions:** Conceptualization, A.Z.; methodology, A.Z.; software, D.A.; validation, D.A.; formal analysis, D.A.; investigation, D.A.; resources, A.Z.; data curation, D.A.; writing—original draft preparation, D.A.; writing—review and editing, A.Z.; visualization, D.A.; supervision, A.Z.; project administration, A.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available in https://doi.org/10.3 390/su11226433.

**Conflicts of Interest:** The authors declare no conflict of interest.

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*Article*
