**Life Cycle Assessment and Energy Balance of a Novel Polyhydroxyalkanoates Production Process with Mixed Microbial Cultures Fed on Pyrolytic Products of Wastewater Treatment Sludge**

**Luciano Vogli 1,\*, Stefano Macrelli 1, Diego Marazza 2,3, Paola Galletti 3,4, Cristian Torri 3,4, Chiara Samorì 3,4 and Serena Righi 2,3**


Received: 8 April 2020; Accepted: 25 May 2020; Published: 28 May 2020

**Abstract:** A "cradle-to-grave" life cycle assessment is performed to identify the environmental issues of polyhydroxyalkanoates (PHAs) produced through a hybrid thermochemical-biological process using anaerobically digested sewage sludge (ADSS) as feedstock. The assessment includes a measure of the energy performance of the process. The system boundary includes: (i) Sludge pyrolysis followed by volatile fatty acids (VFAs) production; (ii) PHAs-enriched biomass production using a mixed microbial culture (MMC); (iii) PHAs extraction with dimethyl carbonate; and iv) PHAs end-of-life. Three scenarios differing in the use of the syngas produced by both pyrolysis and biochar gasification, and two more scenarios differing only in the external energy sources were evaluated. Results show a trade-off between environmental impacts at global scale, such as climate change and resources depletion, and those having an effect at the local/regional scale, such as acidification, eutrophication, and toxicity. Process configurations based only on the sludge-to-PHAs route require an external energy supply, which determines the highest impacts with respect to climate change, resources depletion, and water depletion. On the contrary, process configurations also integrating the sludge-to-energy route for self-sustainment imply more onsite sludge processing and combustion; this results in the highest values of eutrophication, ecotoxicity, and human toxicity. There is not a categorical winner among the investigated configurations; however, the use of a selected mix of external renewable sources while using sludge to produce PHAs only seems the best compromise. The results are comparable to those of both other PHAs production processes found in the literature and various fossil-based and bio-based polymers, in terms of both non-biogenic GHG emissions and energy demand. Further process advancements and technology improvement in high impact stages are required to make this PHAs production process a competitive candidate for the production of biopolymers on a wide scale.

**Keywords:** LCA; energy metrics; PHAs; bio-based polymers; biodegradable plastics; pyrolysis; volatile fatty acids

#### **1. Introduction**

According to the European Commission [1], the transition to a more circular economy is an essential contribution to the efforts to develop a sustainable, low-carbon, resource-efficient, and competitive economy. In a circular economy, closed material cycles should be encouraged where possible [1]. Bio-based materials, i.e., those derived from renewable resources, such as wood, crops, or fibers, have various applications in a large variety of industries (e.g., construction, furniture, packaging, coatings, textiles, cardboard, chemicals, etc.) and energy uses (e.g., biofuels). Their characteristic of being made of organic carbon, which can be recycled and reused many times, in many ways, goes towards the principles of the waste hierarchy and, more generally, results in better overall environmental performances [1]. The bioeconomy, consequently, offers alternatives to fossil-based energy and products, and can make an important contribution to the circular economy. Moreover, bio-based materials can provide benefits connected to their renewability, biodegradability, or compostability. Nevertheless, the use of biological resources requires attention and a careful assessment through their life-cycle environmental impacts. Indeed, their use can also create competition for them and generate pressure on land use [2]. In this context, bioplastics are a very promising research area, usually indicated as a sustainable alternative to conventional fossil-based products. They have the advantage, over traditional plastics, of diminishing the use of non-renewable resources and also decreasing the environmental impact related to fossil resources' consumption [3,4].

Polyhydroxyalkanoates (PHAs) are polymers belonging to a group of polyesters that are generated by some bacteria as carbon and energy intracellular reserve granules. Currently, more than 90 bacterial species that produce PHAs and about 150 diverse monomers of PHAs have been recognized [5]. Their accumulation is usually observed when one nutrient (e.g., nitrogen, phosphorus, and oxygen) is present in the fermentation broth in a limiting concentration, while, at the same time, there is an available excess source of carbon. Most bacterial strains, such as *Cupriavidus necator*, accumulate PHAs as secondary products under nutrient-limiting conditions. However, some bacterial strains, such as recombinant *Escherichia coli* and *Alcaligenes latus*, synthesize PHAs as a primary metabolite during microbial growth [6]. Generally, PHAs are produced by pure microbial cultures grown on renewable feedstocks (i.e., sugar or oils) under sterile conditions, but recently, several authors have investigated the exploitation of residues and waste as growing substrates [7–9]. Commonly, PHAs are considered eco-friendly because they are produced from renewable natural resources instead of petrochemicals, and because they biodegrade without producing harmful or toxic by-products and leaving no worrisome waste [10]. Anyway, in order to conclude if biopolymers are environmentally advantageous over petrol-based plastics, it is necessary to analyze their entire life cycle. Moreover, to make PHAs really competitive, first of all they need to equal their petrochemical counterparts both in terms of quality and economic performances [11]. Sustainable PHAs production is multifaceted and several criticisms need to be tackled in the process in order to make the manufacture of PHAs on an industrial scale convenient both environmentally and economically. For this purpose, cheaper microbial cultures and reutilization of waste streams as growing substrates appear among the main points for large-scale industrial production [12–14].

Among the many residues and wastes tested as growth substrates [9], agro-industrial and municipal effluents and sewage sludge have received considerable attention in recent years. Combinations of food waste and sewage sludge [15,16], industry and municipal primary sludge [17], municipal secondary sludge [18], and sewage sludge after hydrothermal carbonization and acidogenic microbial fermentation [14,19] are examples of the substrates investigated in order to obtain volatile fatty acids (VFAs) suitable as a carbon source for PHAs producing mixed microbial cultures (MMCs). At the same time, agro-industrial wastewater and sludge have also attracted great interest. A plethora of agro-industrial effluents have been tested as substrate for PHAs production: Sugarcane molasses, paper mill effluent, dairy effluent [20], distillery effluents [21], rice winery wastewater [22], and yeast industry wastewater [23]. Authors often agree in concluding that the reutilization of wastewater and sludge as a carbon substrate not only abates the cost of PHAs production [20,21] but also

provides for a significant reduction of the sludge disposal cost [19], constituting a sustainable waste management [15,18] and significantly decreasing the environmental impact of PHAs [17].

The European Bioplastics association [3] supports life cycle assessment (LCA) in order to validate the eco-sustainability of bioplastics. LCA encompasses the energy and material flows within the system boundary and calculates the relevant impacts generated by each unit process. The energetic and environmental sustainability of PHAs production has usually been evaluated by means of LCA. The first LCA studies were focused on PHAs production based on carbon sources from dedicated cultures, mainly glucose [24], in particular from corn [25–28] and corn grain integrated by corn stover [29], but also soybean oil [30] and sucrose from sugar cane [31,32] as an alternative to glucose. PHAs from a genetically modified corn have also been analyzed [33]. Later, the focus shifted to evaluation of the eco-performances of PHAs produced from alternatives to dedicated carbon sources, such as fermentable sugars from lignocellulosic biomass [31], industrial wastewater [12,34], biomethane [35], municipal solid waste [36], potato [37], switchgrass [38], etc. In the last few years, the increasing attention given to wastewater and sludge as a PHAs-accumulating bacteria growth substrate has pushed many LCA studies in that direction. Heimersson and co-authors [39], Fernández-Dacosta and co-authors [40], andMorgan-Sagastume and co-authors [41] investigated the environmental performances of wastewater treatment plants (WWTPs) with integrated PHAs production. Dietrich and co-authors [42] analyzed the sustainability of PHAs production in integrated lignocellulose biorefineries. Vega and co-authors [43] examined the eco-sustainability of a biorefinery treating a mixture of cow manure and grape marc. Very recently, a review on the link between sustainability and industrial waste streams as feedstock for the production of PHAs has been published [44]; many different industrial streams have been taken into consideration, including activated sludge and industrial aqueous streams. The authors agree that the consideration of waste stream exploitation as a bacteria growth substrate is an interesting contribution towards a circular bioeconomy [44], economic competitiveness against fossil-based products [34,42], and eco-sustainability [43,45]. However, there is also a general agreement on the need for further investigations in order to improve the PHAs production process [40,44,46,47], and to deeply explore the environmental performances [41], thus avoiding possible burden shifting [43] and widening the analysis to include disregarded impacts like water use, land use, and eutrophication [39]. The reviews of Narodoslawsky and co-authors [48] and Cristóbal and co-authors [49] show a great variability among the LCA results obtained by the different authors due to technological differences but also different choices in the LCA applications. In any case, the power of LCA as tool to address eco-sustainability is reasserted.

This study aims to exploit LCA to assess the energy and environmental burdens of a hybrid thermochemical-biological process [50] that couples pyrolysis and anaerobic-aerobic fermentations to convert industrial sludge into PHAs. Specifically, the system valorizes anaerobically digested sewage sludge (ADSS) coming from wastewater treatment plants of agri-food industries for the production of PHAs via VFAs. Five different scenarios for the use of thermochemical process products were compared.

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

#### *2.1. PHAs Production at the Lab Scale*

The initial steps of the PHAs production process were tested at the laboratory scale by the Chemistry Lab of CIRI FRAME of the University of Bologna, Ravenna Campus; then, a hypothesis of process upscaling at the industrial scale was carried out.

The process for PHAs production involving MMCs consists of several phases (Figure 1) that can be summarized into three main steps: (1) Biomass feedstock pre-treatment through pyrolysis, followed by anaerobic digestion of organic carbon to produce mixtures of volatile fatty acids (VFAs); (2) PHAs-enriched microbial biomass production; and (3) PHAs extraction using organic solvents. A detailed explanation of each step is provided below.

**Figure 1.** Process for PHAs production involving mixed microbial cultures.

#### 2.1.1. Pyrolysis and Anaerobic Digestion (PyAD)

The pyrolytic pre-treatment was performed in order to improve the ADSS fermentability, which can generally be low due to the high content of recalcitrant compounds and complex poorly degradable inorganic matter [51]. Pyrolysis of biomass is a thermochemical decomposition converting biomass into energy and materials and occurring in the absence of oxygen or under an inert gas flow. Pyrolysis provides three products: A liquid fraction (bio-oil), a solid carbonaceous material (biochar), and a gaseous fraction (syngas) [52]. The bio-oil and the syngas can be used as the substrate to feed the anaerobic fermenter to produce VFAs [51]. Besides, the syngas can also be converted into energy in a combined heat and power (CHP) unit [53]. The biochar fraction can be used either as a carbon storage for long-term sequestration, or as a soil amendment bringing it back to the soil, or to produce energy through combustion [54].

Pyrolysis tests were carried out on ADSS, using a fixed bed tubular quartz reactor placed into a refractory furnace, and were performed at 500 ◦C for 30 min. The pyrolizer was connected downstream to two cold traps (ice bath) for trapping the condensable vapors (liquid fraction). The bio-oil obtained was a dark-brown biphasic liquid characterized by a low viscous aqueous phase and a tarry dark-brown one (organic phase). The two phases were separated by centrifuge. Bio-oil and biochar were collected, and the yields determined by weight difference (see Table 1).

Subsequently, the anaerobic digestion tests of pyrolysis bio-oil to produce VFAs were carried out following the procedure by Torri and co-authors [55]. Sludge generated by agro-industrial wastewater treatment itself was used as inoculum after sterilization at 120 ◦C and 2 bar for 60 min; this step should eliminate the methanogenic bacteria while preserving the sporogenic ones. This has the effect of avoiding the production of biogas or, in other terms, maximizing the yield of VFAs. Indeed, the production of VFAs is an anaerobic process involving hydrolysis and acidogenesis, known as acidogenic fermentation. During hydrolysis, the complex organic polymers present in ADSS are subdivided into simpler organic monomers due to the effect of enzymes excreted by the hydrolytic microorganisms [15]. Subsequently, acidogenic bacteria ferment these monomers into VFAs, mainly as acetic, propionic, and butyric acids. Both processes involve a wide range of obligate and optional anaerobes, such as *Bacteriocides*, *Clostridia*, *Bifidobacteria*, *Streptococci*, and *Enterobacteriaceae* [56]. A 100-mL syringe reactor was used, filled with 20 mL of bacterial inoculum and an aliquot of 200 mg of chemical oxygen demand equivalent (CODeq) of bio-oil added to feed the bacterial inoculum. The syringe was closed and stored at 42 ◦C. The reactor was analyzed daily in terms of the chemical oxygen demand (COD) and VFAs content. Biogas production was assessed on the basis of the COD content in the liquid phase, assuming that its decrease was equal to the biogas produced (namely CH4 and H2).


**Table 1.** Main parameters used for the industrial scale-up modeling of the process and related values. A reference is provided for already published data; otherwise, data are unpublished. HB: hydroxybutyrate; HV: hydroxyvalerate; HH: hydroxyhexanoate.

As for the VFAs extraction, reference was made to the experimental data obtained by Torri and co-authors [57], where the innovative methodology of pertraction making use of new liquid membranes (LMs) based on lipophilic amines and biodiesel was proposed for this purpose. The VFAs flux rate obtained with the best performing liquid membrane based on trioctylamine (TOA) at 10 wt% (TOA10-B) demonstrates the feasibility of a closed loop for a selective conversion of VFAs extracted from anaerobic fermentation systems into PHAs-enriched microbial biomass.

#### 2.1.2. PHAs-Enriched Biomass Production

VFAs were converted into PHAs using two aerobic batch reactors in sequence. The physical separation allows process optimization, as it has been shown that different conditions are required at each stage [58,59]. In the first reactor, mixed cultures are subjected to "feast and famine" conditions: High availability and shortage of the substrate are alternated in order to select microbial populations capable of incorporating the VFAs with greater efficiency. The microbial sludge retention time value was set to ensure that all carbon was consumed for cell growth and maintenance. In the latter, the selected microorganisms were fed exclusively with the VFAs produced in the acidogenic fermentation with the aim of promoting the accumulation of PHAs [14,60].

#### 2.1.3. PHAs Extraction

The applied extraction method is based on the solubilization of PHAs with dimethyl carbonate (DMC). This process can be applied either directly on concentrated microbial sludge or on dry biomass, allowing a very high polymer recovery and an excellent purity. The direct extraction from microbial sludge applied in this case study required a biomass to solvent ratio of 2.5% weight to volume ratio. This concentration was obtained by centrifuging and concentrating the microbial culture after the accumulation phase. The sludge underwent extraction with DMC for 4 h at 90 ◦C. Subsequently, the DMC phase, containing the extracted PHAs, and the biomass sludge were centrifuged and separated, and then the extracted polymer was recovered after filtering and evaporating the solvent. The polymer recovery was very high, around 96% [47].

#### *2.2. PHAs Production at the Industrial Scale*

The above described lab-scale pyrolytic system was scaled-up to treat 2500 t dry matter (DM) sludge/year at 3.8 wt% DM received from an average-sized WWTP. To obtain the mass and energy balance, a mixed approach was adopted considering data from laboratory experiments, assumptions, and vendors' data sheets for the equipment. Rigorous calculations for the mass, enthalpy, and COD balances were performed at the pyrolysis stage to ensure comparability between scenarios, given the composition heterogeneity of pyrolysis (bio-oil, syngas, biochar) and gasification products (syngas, ash). Table 1 summarizes the main parameters determined by the tests carried out at CIRI FRAME and used for the industrial scale-up modeling of the process.

#### 2.2.1. Pyrolysis and Anaerobic Digestion (PyAD)

The initial process step was sewage sludge dewatering, first by centrifugation up to 25% DM then by thermal drying up to 85% DM. The dryer was assumed to recover the latent heat by condensing the evaporated water, which was then recirculated into the process water system. As explained, the pyrolytic pre-treatment of ADSS enhances the soluble, and thus bioavailable, COD of ADSS itself, facilitating the conversion yield of organic matter into VFAs during the anaerobic acidogenic fermentation. The thermal energy required for pyrolysis was assumed to be 1.28 MJ per kg of ADSS at 85% DM. VFAs were produced with different yields from the pyrolysis syngas, bio-oil and water phases, and also from syngas obtained through biochar gasification (see Table 1 and Section 2.3.1). As for this technology, a dataset representing an average indirectly heated atmospheric fixed-bed gasifier followed by a low-temperature wet gas treatment was selected. Energy self-production of the plant was modelled by a syngas-driven CHP plant dataset representing an average combustion situation without further flue gas treatment.

In the anaerobic acidogenic fermenter (AAF), the syngas and bio-oil were converted into VFAs with yields equal to 80% and 33% on the COD basis, respectively, and a hydraulic residence time of 7 days. No methane was produced as a result of the sterilization process applied on the inoculum to inhibit methanogenic bacteria.

The energy required by the AAF consists of the thermal energy necessary to heat up inlet streams and to keep the AAF at 40 ◦C, and of the electricity needed for pumping the inlet/outlet flows and for the mixing of the AAF volume. The heat necessary for increasing the temperature of the inlet flow from the environment temperature was calculated on the basis of the specific sensible heat of the water phase. The outlet stream from the AAF was separated into a biological sludge stream and a water phase by a centrifuge consuming 7 MJ per kg of sludge at 3% DM. The biological sludge was recirculated at the initial drying stage, while VFAs were concentrated by the aforementioned pertraction system, which required a centrifugal pump power of 344 J per kg of treated liquid.

#### 2.2.2. PHAs-Enriched Biomass Production

The sequencing batch reactor (SBR) is an aerobic reactor where microbial biomass is produced using VFAs as carbon source and PHAs start to accumulate in microbial cells. Biomass is subsequently transferred to another aerobic batch reactor (accumulation reactor, AR) where MMCs convert VFAs into PHAs with a 50% yield on COD basis. PHAs accumulate in microbial cells up to 60 wt%. Air sparging and liquid mixing are needed in both SBR and AR. The oxygen required for microbial biomass growth was modelled assuming a stoichiometric uptake by microorganisms. A first stage of microbial biomass dewatering was carried out by filtration, in order to reach a 13% DM content. A further dewatering stage was performed by a second thermal dryer to increase the DM to 20% before PHAs extraction. Additionally, in this dryer, the evaporated water was condensed to supply the process water system, but no heat recovery was considered in this case.

#### 2.2.3. PHAs Extraction

The model includes the extraction of PHAs from the MMC by means of the solubilization with DMC as explained in Section 2.1.3. The PHAs recovery rate was set to 96%. The extraction processes were composed by a series of equipment units: Centrifuges, batch reaction vessels, heaters and pumps.

#### 2.2.4. PHAs End-of-Life

The "end-of-life" phase was modelled based on data provided by COREPLA [61]. In Italy, in 2016, 2.2 million tons of post-consumer plastics waste ended up in the waste stream. A share of 85% was recovered, both through material recycling (43%) and energy recovery (42%), while the remaining 15% still went to landfill. Since a supply chain for the material recovery of bioplastics has not yet been implemented, neither at the Italian nor at European level, and since the amount of bioplastics recovered with the organic fraction of municipal solid waste for composting is still very small, it was assumed that all the recovered bioplastic undergoes energy recovery.

#### *2.3. Energy and LCA Analysis*

An energy analysis of the system was performed by means of the energy metrics described in Section 2.3.4.

An attributional LCA modelling was adopted following the ISO 14040:2006 and ISO 14044:2006 [62,63]. This approach was chosen because it is the most applied and best established.

#### 2.3.1. Scenarios

Three different scenarios were conceived to model the use of syngas produced by both ADSS pyrolysis and biochar gasification: (1) Pyrolysis syngas and biochar syngas to AAF for VFAs production (scenario A—AllSyn2VFA); (2) pyrolysis syngas to AAF for VFAs production, biochar syngas to energy production (scenario B—CharSyn2E); and (3) both pyrolysis syngas and biochar syngas to energy production up to complete fulfilment of the electricity and thermal energy request of the plant, the exceeding part to AAF for VFAs production (scenario C—MostSyn2E).

The electricity and thermal energy external demand were set to null by an optimization algorithm able to adjust the energy fraction produced by the plant CHP and boiler. The algorithm ensures alternatively the electric self-sustainment of the plant (scenario B) and both the electric and thermal self-sustainment of the plant (scenario C) as primary objective.

In order to assess the impact of the external energy sources choice, three scenarios were modelled on the basis of scenario A, which is the one using only external energy sources.

In the first one, named A1—ConvE, electricity is supplied by the Italian grid mix and thermal energy by the combustion of natural gas. In scenario A2—RE/Pv+Bg, electricity is provided by photovoltaic systems and thermal energy by the combustion of biogas from anaerobic digestion of energy crops, such as the so-called waxy maize. In the A3—RE/Mix+SB scenario, electricity is supplied by a mix of renewable sources and thermal energy by the combustion of solid biomass. The renewable electricity mix was determined on the basis of the 2030 outlook contained in the Integrated National Plan for Energy and Climate [64]. The plan predicts that renewable sources will provide for 55% of the electricity consumption in 2030, but in A3—RE/Mix+SB, we envisage that renewable sources will provide for 100% of the consumption mix of PHAs production plant while maintaining the same mutual relationship (Table 2).


**Table 2.** Renewable electricity mix composition for scenario A3—RE/Mix+SB.

#### 2.3.2. Functional Unit and System Boundary

The functional unit (FU) was defined as 1 kg of biopolymer ready for the product's manufacturing. The system boundary was from "cradle-to-grave": It was assumed that the bioplastic item is landfilled or incinerated. In detail, the system boundary includes (Figure 2): Sewage sludge pyrolysis, VFAs production through anaerobic digestion, PHAs-enriched biomass production using a MMC, PHAs extraction with DMC, and bioplastic items end-of-life. The analysis was carried out considering four main stages, called: "Pyrolysis and anaerobic digestion" (PyAD), "PHAs-enriched biomass production", "PHAs extraction", and "PHAs end-of-life" (PHAs EoL). The system boundary does not include product manufacturing and the additives used in polymer resins to achieve desirable material properties since these phases are the same for all scenarios; this choice is consistent with previous comparative LCA studies of biopolymers and conventional polymers [38,65–67]. Moreover, we assumed that the collection and transport of waste to various treatment plants are not relevant [68], and that even these phases are identical in all scenarios; therefore, they were not included in the system boundary. Finally, sewage sludge coming from the agro-industrial sector entering the process was considered as entering the system without any associated impact, according to the 'zero-burden-boundary' hypothesis.

The end-of-life (EoL) phase for plastic items at the European level envisages different types of treatment: Mechanical material recycling, organic carbon recycling (i.e., composting and anaerobic digestion), energy recovery, and landfilling [69,70]. Only energy recovery and landfilling for bioplastics was considered because the separated collection and recycling of bioplastic waste is scarcely implemented at the European level and in Italy, in agreement with information reported by COREPLA, the Italian national consortium for collection, recycling, and recovery of plastic packaging, as mentioned in Section 2.2.4.

Model decisions regarding the treatment of waste, and co- and by-products, are a potentially important contributor to the differences among LCA studies. System expansion, where applicable, is the baseline method for handling co- and by-products, consistent with ISO 14044 [63]. In the PHAs production model, different fates are foreseen for waste and by-products. Biological sludge from the AAF and from the PHAs extraction phase is recycled to the initial drying stage: This waste biomass was assumed to have the same composition as the inlet ADSS because the ash content concentrates in biochar; in other terms, ash exit the system and does not accumulate after every cycle. Process water is internally reused, while wastewater is sent to a WWTP and the sewage sludge produced is treated as other biosolids and used in agriculture. As regards pyrolysis co-products, bio-oil and water phases are sent to the AAF to produce VFAs, while biochar is gasified to produce biochar syngas. The latter, together with pyrolysis syngas, is alternatively sent to the AAF to produce VFAs or to energy production through CHP and a boiler in order to satisfy the energy request of the plant, depending on the scenario.

**Figure 2.** System boundary. The colored dotted lines delimit the four life cycle phases considered in the analysis, with the same color code used in the results graphs. The feedstock and the main product and co-products are indicated in the oval boxes, the processes in the rectangular boxes, and the flows in italics.

#### 2.3.3. Inventory Data

The study was based on laboratory and pilot-scale data collection in accordance with the current level of maturity of the technology: An operational commercial-scale facility has not yet been implemented. Primary data were used for foreground processes, which took place at CIRI FRAME laboratories, and LCA databases were used for background processes, while estimates based on commercial and literature data were used for other processes, such as the biochar gasification, and to scale-up the whole PHAs production system.

LCA was performed using GaBi ts 8.7 software. The LCA databases used for background data were GaBi Professional Database [71] and Ecoinvent Database version 2 [72].

In Table 3, the life cycle inventory data of the main flows referred to the FU for the scenarios are presented.



<sup>1</sup> Data for the three A-based scenarios are reported in a single column since they differ only in the external energy source used, and not in flow figures. <sup>2</sup> Various energy sources, depending on scenario.

As it is possible to observe in Table 3, data for the analyzed scenarios differ only in the pyrolysis and anaerobic digestion phase.

As regards the energy recovered through the incineration process in the EoL phase, a dataset for the combustion of bio-based polypropylene (bio-PP) with a net calorific value (NCV) of 43.5 MJ/kg in a waste incineration plant was considered [71]. It was assumed that this is also the PHAs' NCV, on the basis of the physical properties and chemical structure being similar to bio-PP. To be consistent, this value was also assumed for the calculation of energy indicators.

#### 2.3.4. Energy Balance and Energy Performance Metrics

Besides the mass balance, the energy balance was also calculated as it constitutes part of the inventory for impact assessment. Every energy figure, including primary energy (PE), is provided on an NCV basis and electricity is equally provided as NCV equivalent.

In Table 4, the energy balance for the three analyzed scenarios of the PHAs production system referring to the FU is presented. The end-of-life phase is excluded.

**Table 4.** Energy balance of the PHAs production system, referring to the functional unit (1 kg of PHAs produced).


<sup>1</sup> Data for the three A-based scenarios are reported in a single column since they differ only in the external energy source used, and not in figures. <sup>2</sup> Various energy sources, depending on the scenario.

As in Table 3, also in Table 4, data of the three A-based scenarios are reported in a single column since they differ only in the external energy source used, and not in energy figures.

Several energy indicators, such as returns and ratios, are derived from the energy balance and adopted to better investigate the effects of the main factors affecting the results: The amount of ADSS used as feedstock, the energy recovery by end-of-life, and the renewable energy ratio.

Two indicators on energy return (ER) are defined:

• ERPHAs, overall (Equation (1)) expresses the energy contained in PHAs as a rate of the total energy invested in their production; the total energy invested is defined as the sum of the energy contained in input sewage sludge and the primary energy of heat and electricity sources; and all factors are measured in terms of their NCV:

$$\text{ER}\_{\text{PHAs, overall}} = \frac{\text{energy in PHAs}}{\text{total energy invested}} = \frac{\text{Ery}\_{\text{THAs}}}{\text{PE}\_{\text{focal}} + \text{PE}\_{\text{nonveatho for 11}+\text{E}} + \text{E}\_{\text{tolag/ft}, \text{for 11}+\text{E}} + \text{E}\_{\text{sludge}, \text{for 11}, \text{for } 11 \text{Ms}}}. \tag{1}$$

• ERPHAs, EoL (Equation (2)) is a similar indicator to ERPHAs, overall but includes a term representing the energy recovered from PHAs EoL; thus, it expresses the energy return based on the net energy required for PHAs production considering also the end-of-life phase:

> ERPHAs, EoL <sup>=</sup> energy in PHAs total energy invested−energy recovered from EoL <sup>=</sup> EPHAs PE fossil+PE renewable for H+E+Esludge, for H+E+Esludge, for PHAs−EEoL . (2)

Two other indicators named renewable energy ratio (RER) are adopted to determine the rate of renewable energy used:

• RERfor H<sup>+</sup><sup>E</sup> (Equation (3)) describes the renewable quota of energy used as heat and electricity only, thus excluding the fraction of sewage sludge transformed into PHAs:

$$\begin{array}{l} \text{RER}\_{\text{for H}+\text{H}} = \frac{\text{external renewable energy} + \text{energy in sludge for heat and electricity}}{\text{total energy invested} - \text{energy in sludge for PHAs}} = \\ \frac{\text{PE}\_{\text{nomoundable for H}+\text{H}} + \text{E}\_{\text{duludge}}, \text{ for H}+\text{E}}{\text{PE}\_{\text{f},\text{f},\text{wall}} + \text{PE}\_{\text{nonveable for H}+\text{H}} + \text{E}\_{\text{duludge},\text{for H}+\text{H}}}. \end{array} \tag{3}$$

• RERoverall (Equation (4)) describes the renewable quota of energy used in the PHAs production process as heat, electricity, and material, thus including the fraction of sewage sludge transformed into PHAs; in fact, the denominator is the total energy invested:

$$\begin{array}{l} \text{RER}\_{\text{overfall}} = \frac{\text{external renewable energy} + \text{energy in sludge}}{\text{total energy invested}} = \frac{\text{PE}\_{\text{т:nrow}} \text{valid year H} + \text{E} + \text{E}^{\circ} + \text{S}^{\circ} \text{ads}}{\text{PE}\_{\text{frac}} \text{row} + \text{PE}\_{\text{π}\text{т:nrow}} \text{who for H} + \text{E} + \text{E}^{\circ} + \text{S}^{\circ} \text{ads}} \text{overnment}} \end{array} \tag{4}$$

The last energy indicator adopted is the PHAs-to-fossil-energy ratio (Equation (5)), i.e., the energy in PHAs per unit of primary energy from fossil sources invested, as an indicator of the fossil energy use efficiency:

$$\text{FER}\_{\text{PEIAs}} = \frac{\text{E}\_{\text{PEIAs}}}{\text{PE}\_{\text{fossail}}}.\tag{5}$$

#### 2.3.5. Life Cycle Impact Assessment

In the life cycle impact assessment (LCIA) phase, environmental burdens were assessed using the flows and data referred to the FU modelled in the inventory phase.

The ILCD/PEF Recommendations v1.09 method [73,74] was used; this method considers the environmental impact under 16 impact categories, listed below: Acidification midpoint (AP); climate change midpoint, excluded biogenic carbon (GWPexc.); climate change midpoint, included biogenic carbon (GWPinc.); ecotoxicity freshwater midpoint (FE); eutrophication freshwater midpoint (EuF); eutrophication marine midpoint (EuM); eutrophication terrestrial midpoint (EuT); human toxicity midpoint, cancer effects (HTc); human toxicity midpoint, non-cancer effects (HTnc); ionizing radiation midpoint, human health (IR); land use midpoint (LU); ozone depletion midpoint (OD); particulate matter/respiratory inorganics midpoint (PM); photochemical ozone formation midpoint (POF); resource depletion water midpoint (WD); and resource depletion mineral, fossils, and renewables midpoint (RD).

The impacts due to the energy demand of the process were specifically taken into consideration through the category "primary energy from renewable and non-renewable resources" (PED, R+NR), which is not part of the aforementioned method.

Moreover, the optional phases of normalization and weighting were applied, in order to identify the most relevant impact categories in determining the overall impact of the process. The normalization factors and the weight vector used in this analysis were those proposed by PEFCR guidance [75].

The environmental performances in terms of the primary energy demand and climate change obtained for PHAs within this study were compared with those of two polymers of fossil origin, i.e., polyethylene terephthalate (PET) and polypropylene (PP), and two biobased polymers, i.e., bio-polypropylene (bio-PP) and polylactic acid (PLA). The environmental performances of these four polymers were also obtained by modeling their production and end-of-life phases through GaBi software and the related databases [71].

#### **3. Results and Discussion**

#### *3.1. Energy Performance Indicators Results*

In Table 5, the energy indicators results for the five analyzed scenarios of PHAs production system referring to the FU are presented.

**Table 5.** Energy indicators results referring to the functional unit (1 kg of PHAs produced). All energy indicators are measured in terms of net calorific value.


Considering the five scenarios from an energy perspective, several factors affect the energy balance. Such factors are (a) the amount of sewage sludge used for onsite energy production besides the PHAs production, (b) the energy recovery from PHAs EoL, and (c) the renewable energy ratio. Energy indicators are used to describe and explain the impact of factors' variation.

As previously mentioned (see Section 2.3.3), it is assumed that the energy contained in PHAs is 43.5 MJ/kg, while the energy contained in ADSS is 0.45 MJ/kg.

A key indicator is the total energy invested, this being the sum of ADSS input (energy) and PE—external supply. The latter includes the contribution of both fossil-based energy and renewable sources, such as biogas and photovoltaic, depending on the scenario. The scenario with the lowest total energy invested to produce 1 kg of PHAs is A1—ConvE, because of the lowest amount of ADSS input required by the process. As an alternative to fossil sources, scenarios A2 and A3 were considered to evaluate the use of renewable energy (heat and electricity) to fuel the process. Despite the same ADSS input value of scenario A1, both in scenarioA2 and in scenario A3, the amount of total energy invested is higher because of the higher value of PE—external supply. Such a rise depends on the renewable component of the indicator (PE—external supply, renewable), whose increase more than compensates for the simultaneous decrease of the non-renewable component (PE—external supply, fossil).

In contrast to the A-based scenarios, where the entire amount of sewage sludge is used to produce PHAs, scenarios B and C are designed to use a fraction of pyrolytic products to produce onsite the energy to fulfil either the internal demand for electricity only (scenario B) or the internal demand for both electricity and heat (scenario C). For this reason, an increasing amount of ADSS is needed to produce 1 kg of PHAs in scenarios B and C: 2.17 and 3.38 times the mass flow of scenarios A, respectively. The total energy invested per kg of PHAs ranges from 222.6 MJ (scenario A1) to 329.2 MJ

(scenario C), i.e., a growth of 48% of total energy invested is needed if the energy production is sourced onsite by the ADSS instead of the external energy supply. Such a rise depends on the ADSS input (energy) increase, and in particular on the renewable energy in sewage sludge used for H+E, whose increase more than compensates for the simultaneous decrease of the PE — external supply. This is partly explained by the difference in the efficiency between the CHP and boiler used to produce energy onsite, and the external power plants. Moreover, the sludge-to-energy process comprises several steps, each one characterized by energy costs and losses. In more detail, sewage sludge must be centrifuged and dried to be first pyrolyzed, the resulting biochar is gasified, and then biochar syngas and pyrolysis syngas are combusted together in the CHP and boiler.

To better highlight the differences among scenarios, we did not use the energy return on energy invested (EROEI), which is specifically adopted for energy systems and fuels, but a similar indicator, the energy return (ER) defined in Equations (1) and (2). This was used to evaluate the overall efficiency of the entire PHAs life cycle, including the end-of-life stage (Equation (2)).

Energy return indicators show that PHAs contain about one eighth up to a fifth of all the inputs used for its production, both energy and materials, all of them on an energy basis. If energy recovery from the EoL phase is also taken into account, such figures increase. ERPHAs, overall ranges from 12.7% to 19.5% and ERPHAs, EoL from 13.8% to 22.2%. Comparing ERPHAs, overall and ERPHAs, EoL, the latter is higher in the range between 1.2 and 2.9 percentage points due to the energy recovery by PHAs EoL. If a life-cycle perspective is assumed, EoL contributes to a reduction of the total energy invested to produce PHAs; it was calculated that 66% of the NCV contained in PHAs is recovered at the PHAs EoL stage by incineration of PHAs and the combustion of biogas from PHAs degradation in landfill. Moreover, the contribution of the EoL energy recovery can completely offset the external supply of PE, such as in scenario C, where the external PE is even lower than the energy recovery by EoL. For both these energy return indicators, it is found that the lower the ADSS amount used for energy, the greater the return (A1 > B > C).

From a process perspective, the amount of energy used per FU is an indicator of the production efficiency, but it is not enough to address the viability and impacts of the process. For this purpose, other factors, such as the renewability of energy sources, must be taken into account. The renewable energy used is expressed as the renewable energy ratio considering the sole energy used for process heat and electricity (RERfor H+E) or including every energy input (i.e., heat, electricity, sewage sludge NCV, PE) in the PHAs production (RERoverall). The onsite production of energy from ADSS enhances the renewable energy ratio (RERfor H+E) from 19% in scenario A1 up to 63% in scenario B and 93% in scenario C. Results comparable with the latter are achieved in scenarios A2 (91%) and A3 (89%), where the entire heat and electricity supply comes from external and different renewable sources. The overall renewable energy ratio (RERoverall) takes into account also the NCV of the sewage sludge transformed into PHAs. Compared to the RERfor H<sup>+</sup><sup>E</sup> results, RERoverall shows higher values, especially for scenario A1, due to the sewage sludge NCV, which almost equals the fossil PE. For all the other scenarios, RERoverall is just slightly higher than RERfor H+E. Scenario C is the one with the highest RER and with the lowest ER (excluding scenario A2) for the same reason: The use of sewage sludge to produce onsite renewable heat and electricity.

#### *3.2. LCIA Results*

The overall LCIA results for the five scenarios are reported in Table 6.

Detailed results for the five scenarios are shown in Figure 3, where the relative contribution of each LCA phase—pyrolysis and anaerobic digestion (PyAD), PHAs-enriched biomass production, PHAs extraction, polymer end-of-life (PHAs EoL)—to each midpoint impact category score are reported and compared.


**Table 6.** Life cycle impact assessment results for the five analyzed scenarios (A1, A2, A3, B, C).

#### 3.2.1. Scenarios' Performance Comparison

Table 6 shows the total impact scores of the 5 analyzed scenarios over the 17 chosen impact categories. A clear pattern emerges from the data when considering the number of impact categories in which each scenario shows better results than the others or, in other words, when all 17 impact categories are considered equivalent. From this point of view, scenario A1—ConvE performs better than the other ones but only slightly better than scenario A3—RE/Mix+SB. In more detail, scenario A1 scores best in six categories (AC, EuF, EuM, POF, PM, HTnc), and in four more categories, it is first on an equal footing (EuT, FE, HTc, and OD). Scenario A3 shows the best results in two categories (GWPinc. and IR), and in five more categories, it is first on an equal footing (EuT, FE, GWPexc., HTc, and OD), four of them with scenario A1, and is the second best in six categories (AC, EuF, EuM, POF, PM, HTnc, the latest on an equal footing). Moreover, A1 outperforms scenario A3 in nine categories, including PED, R+NR, and RD, while scenario A3 outperforms scenario A1 in four categories, including GWPexc. and GWPinc. Scenario B—CharSyn2E has medium performances, showing the best results in two categories (LU and WD); in another one, it is first on an equal footing with all the other scenarios (OD), and it never shows the worst results. Scenario A2—RE/Pv+Bg shows the worst results in six categories (AC, EuM, LU, PM, RD, PED, R+NR) and the best in three categories (FE, HTc, and OD), all of them on an equal footing with other scenarios. Scenario C—MostSyn2E shows the worst performances in seven categories (GWPinc., EuF, EuT, FE, HTc, HTnc, POF) but also the best in the other four, even if in two of them (GWPexc. and OD) on an equal footing. It is worth noting that three of the impact categories where it shows the best results are GWPexc., PED, R+NR, and RD, which is the main impacts—often the only ones—considered when comparing the environmental performances of plastics and/or bioplastics. These categories can be considered on a global scale, since the impact measured by the midpoint indicator, e.g., the global warming potential, has an effect on a global scale. On the other hand, it is possible to state that scenario C generally shows higher values than the A-based scenarios in the impact

categories AC, EuF, EuM, EuT, FE, HTc, HTnc, PM, and POF. These categories can be considered on the local and regional scale, since the impact measured by the midpoint indicator, e.g., the acidifying or eutrophicating potentials, has an effect on a local or regional scale. Therefore, the results exclude that scenarios A1 and A3 are superior to scenario 3 when considering only a small number of impact categories, or when their relative importance is also taken into account. In other words, the results cannot establish in a straightforward way whether one scenario prevails over the others, as the ranking depends on the method adopted to establish it. An insight into the reasons and origins of this trade-off is given in the following Section 3.2.2, after analyzing in detail the impacts in relevant categories.

**Figure 3.** Relative contribution of LCA phases to midpoint impact categories scores for each of the five analyzed scenarios (A1, A2, A3, B, C).

The only category equally impacted in all five scenarios is ozone depletion (2.3 <sup>×</sup> 10−<sup>8</sup> kg CFC-11 eq.). This finding can be easily explained since the impact derives almost completely from the PHAs extraction process that is the same for each scenario. In particular, the impact is attributable to the emission into the atmosphere of organic halogenates, mainly bromochlorodifluoromethane (Halon 1211), deriving from the methanol production process that is the base-chemical of DMC synthesis.

The differences among the results of the five scenarios are high for many impact categories, even one order of magnitude. Generally, differences are higher than ±50%; apart from the OD category, climate change included biogenic carbon is the one showing the lowest percent differences.

#### 3.2.2. Detailed Analysis of Relevant Impact Categories Results

As explained in Section 2.3.5, results were also calculated using the Environmental Footprint 2.0 method in order to determine relevant impact categories, i.e., those cumulatively accounting for 80% of the total impact score. Applying the normalization and weighting factors proposed by PEFCR guidance [75], the identified categories are (in decreasing order): GWP, WD, RD, EuF, POF, and respiratory inorganics.

Considering this, the analysis of the relative contribution of the processes was focused on the following impact categories: GWPexc., EuF, WD, POF, PM (as proxy for "respiratory inorganics"), and RD. The PED, R+NR impact category is also discussed. Figure 3 shows the relative contributions of PyAD, PHAs-enriched biomass production, PHAs extraction, and PHAs EoL phases.

Differences among the five scenarios for the primary energy demand from renewable and non-renewable resources are high. The energy recovered in the EoL phase is the same for every scenario, while energy required in PHAs production varies widely (see Table 4) as a function of both the energy source and the input feedstock, which increases from scenarios A to scenario B, and from scenario B to scenario C. In fact, a greater quantity of processed ADSS is progressively used, until energy independence is reached in scenario C. It must be remembered that in the LCA analysis, the input ADSS is considered as "zero-burden", i.e., without any associated environmental impact, in accordance with what is generally applied in life cycle analyses of waste. This implies that the PED, R+NR value of ADSS is zero. On the contrary, external sources supplying energy in A-based scenarios and in scenario B have positive PED, R+NR values, i.e., they have associated environmental impacts. For this reason, the PED, R+NR decreases as the processed ADSS increases. The results for A-based scenarios clearly show that the PED, R+NR values for external renewable sources are not lower than those of fossil sources; quite obviously, their high value mainly derives from the renewable part of the indicator, while that of the fossil sources mainly derives from the non-renewable part. This is in accordance also with the findings from the analysis of energy performance indicators (see Section 3.1).

In scenarios A1, A2, A3, and B, the life cycle phase causing the greatest impacts in terms of PED, R+NR is PyAD, and in particular the pertraction system, due to its high energy consumption. In scenario C, the phase responsible for the greatest impacts is PHAs extraction, due to the background process of DMC production, an energy-intensive process. Thanks to the energy recovery from the bioplastic products, the EoL phase leads to environmental credits. These credits, together with the energy self-sufficiency of the process, lead to the negative PED, R+NR value for scenario C.

Differences among the five scenarios for the climate change, excluded biogenic carbon category, are high, in some cases even more than one order of magnitude. The contributions to climate change scores are mainly due to PyAD and PHAs-enriched biomass production in scenario A1; to PyAD and PHAs EoL in scenarios A2, B, and C; and to PHAs extraction and PHAs EoL in scenario A3. Relevant flows analysis indicates that the carbon dioxide emissions due to thermal energy and electricity production processes are key contributors in every scenario. Generally, the lower the non-renewable energy use, the better the results for GWPexc. In scenarios A3 and C, i.e., the two best performing scenarios, carbon dioxide savings are due to credits for energy production in the PHAs EoL phase. These credits are subtracted from the emissions due to the other life cycle phases, resulting in an overall environmental benefit for this impact category.

Differences among the five scenarios for eutrophication of freshwater are less than one order of magnitude. EuF is mainly related to PyAD in every scenario. In particular, it is related to the phosphorous emissions to freshwater deriving from WWTP and related sludge agricultural spreading, and to phosphate emissions to freshwater from biochar gasification ash disposal. Coherently, the impact increases as the amount of input ADSS to be processed increases.

As regards water depletion, scenarios are grouped into three clusters: The highest impact values are observed for A1 and A2, and the lowest ones for B and C, while A3 shows intermediate values. The relevant processes related to water depletion are different for the various scenarios. In scenario A1, this impact is roughly equally distributed among the different processes and PHAs EoL leads to a

water saving of about 30%. In scenario A2, the contribution of PHAs extraction and PHAs-enriched biomass production decreases, while that of PyAD increases, as well as the percentage saving due to EoL. In scenario A3, the contribution of PyAD and PHAs-enriched biomass production decreases substantially, while that of PHAs extraction increases slightly; however, credits obtained from PHAs EoL lead to an overall environmental benefit. In scenarios B and C, WD is an "avoided impact" too, and for more than 50%, thanks to PHAs EoL. These savings are due to credits for energy production through plastics incineration, while the impacts in the other life cycle stages in every scenario are always due to electricity consumption from the grid mix.

Resources depletion shows a very similar pattern to that of water depletion for scenario A1, where the non-renewable elements and energy resource consumption for energy production have a high impact. The impact grows further in scenarios A3 and A2, in particular for the PyAD and PHAs-enriched biomass production phases, even if in these scenarios are due to the consumption of renewable energy sources. PyAD in scenario B and both PyAD and PHAs-enriched biomass production in scenario C show negative scores. This is due to credits obtained for the use in agriculture of WWTP sludge deriving from process water treatment, which implies an avoided consumption of phosphate ore for chemical fertilizer production. In scenarios B and C, the negative score is also due to the avoided consumption of non-renewable elements and energy resources for energy production. There are other impacts coming from non-renewable elements and resource consumption for methanol production in the PHAs extraction phase in all scenarios.

As mentioned above, two other relevant impact categories are POF and respiratory inorganics. Differences among the five scenarios for photochemical ozone formation are less than one order of magnitude and the results show a pattern similar to the eutrophication of freshwater. Impacts are mainly related to PyAD, in particular to nitrogen oxide emissions from syngas CHP and the boiler used for electricity and thermal energy production in scenarios B and C, and from biogas combustion in scenario A2. Coherently, the impact increases as the amount of syngas and biogas used to produce energy increases. As a proxy for respiratory inorganics, not present among the ILCD-recommended impact categories, it is possible to analyze the impact category of particulate matter. Additionally, for this category, the differences among the five scenarios are less than one order of magnitude, and the impact score is primarily due to PyAD, in particular to PM2.5, i.e. PM with a diameter of 2.5 μm or less, and nitrogen oxides emissions from syngas CHP and the boiler used for electricity and thermal energy production in scenarios B and C, and from thermal energy production through biogas combustion in scenario A2. In this case, biogas combustion causes a greater impact than syngas combustion.

Summarizing the contributions analysis, it is possible to claim that PyAD is generally the most relevant phase for each scenario and each relevant impact category, and that when there are exceptions, they are related to the avoided impacts obtained by PHAs EoL. The environmental impacts of PyAD in all scenarios are essentially due to the energy production processes, both from fossil and renewable sources, and also from the syngas CHP and boiler when present. Among the processes with the highest energy needs is the pertraction system used to transfer VFAs from dilute streams of anaerobic digestion into a VFAs-rich stream for PHAs production. The temperature difference between mesophilic anaerobic digestion with sporogenic bacteria and PHAs production with MMC results in a high thermal energy demand in the pertraction system because of the cooling of large volumes of liquid from AAF, which must be heated again to a mesophilic temperature before being recirculated back to AAF. As demonstrated by comparing the different scenarios, and in particular the A-based ones, the replacement of fossil energy sources with renewable energy sources does not allow ipso facto for a reduction of the environmental impacts due to this process in all the analyzed impact categories, but there is a trade-off between global and local impacts.

Generally, renewable energy sources cause lower fossil carbon emissions and a lower use of fossil primary energy and fossil resources, that is, lower impact values in global-scale categories; however, their total primary energy and their resource use is higher when renewable energies and resources are taken into account, but this is not the case when organic waste, such as ADSS, which is

considered "impact free", i.e., without any associated environmental impact, is used as an energy source. On the other hand, renewable energy production and particularly the production and combustion of biomass shows higher scores in regional- and local-scale impact categories; this is mainly due to the fact that biomass production requires vast agricultural areas and intensive cultivation methods, including the use of chemical fertilizers, and that biomass combustion is often less efficient than fossil fuel combustion, generating emissions and waste with high acidifying and eutrophicating potential, and with toxic effects.

However, it is possible to note that the A3—RE/Mix+SB scenario generally has good performances both on global impacts, such as climate change, thanks to the use of renewable resources, and on local ones, such as the water depletion, particulate matter emission, and eutrophication categories. The latter is thanks to the fact that the combustion processes of solid biomass (consisting mainly of by-products) have lower impacts compared to the combustion of biogas produced from ad hoc grown biomass (A2—RE/Pv+Bg), and that the renewable electricity mix has lower impacts than the electricity mix used in the "conventional energy" scenario (A1—ConvE).

The modelled end-of-life phase leads to environmental credits being obtained. In fact, electricity and thermal energy is produced from both PHAs' direct combustion in waste-to-energy plants and the combustion of recovered biogas generated by PHAs' degradation in landfills. Thanks to this energy production, an equivalent amount of energy production from non-renewable resources, with greater environmental impact, is avoided.

An undeniable advantage of PHAs is their biodegradability, which at the moment is not fully considered and evaluated within the LCA methodology. While methodological research in this field shall go on, in the near future, a different end-of-life for these polymers is foreseeable as their diffusion increases, that is, their collection together with organic waste, followed by the recovery of the carbon content through composting or anaerobic digestion. At that stage, it will be appropriate and interesting to assess which end-of-life of the polymer will bring the greatest benefits.

#### 3.2.3. Comparison with PHAs Literature and Other Polymers' Results

In terms of environmental performance, in general, bio-based products tend to compare poorly against their respective conventional counterparts on impact categories, such as eutrophication, an impact typically caused by fertilizers during biomass cultivation [76,77]. Mixed results have been reported for other categories, including acidification and tropospheric ozone formation. Claims of environmental benefits for bioproducts often rely on reduced greenhouse gas (GHG) emissions and non-renewable energy use. In any case, the wide ranges of objectives, scopes, methodological choices, and results of LCA studies on bio-based products makes it difficult not only to infer a general trend on their environmental impacts but also to suggest a widely applicable way to reduce their impacts [76]. However, as regards PHAs production specifically, and as it is shown in the following comparison with the literature, the choice of feedstock and the use of residues and co-products for energy production can reduce the environmental impacts of this process [12,31,33,34,36,40,41].

In Figure 4, the values of non-biogenic GHGs emissions per kilogram of PHAs obtained by various authors are compared; data are in chronological order and the whole range of results found in each paper, including this study, is represented when available. Figure 4 shows also the values for four other polymers with which the PHAs are compared with, two of them are fossil-based (PET and PP) and two bio-based (bio-PP and PLA).

**Figure 4.** Greenhouse gases emissions reported in previous PHAs production studies compared to those of PHAs production investigated in this study and those calculated for other polymers. The symbol "\*" before reference number indicates that the system boundaries are "cradle-to-grave", otherwise they are "cradle-to-gate". The whole range of results of each study are represented when available.

Generally, values range from 0.5 to 5 kg of CO2-equivalent per kilogram of PHAs. The results of the present study fall within this range. However, great variability exists among the studies. A potential impact up to about 25 kg CO2-equivalent per kg of PHAs was assessed by Pietrini and coauthors [32] and an avoided impact of up to about 6 kg CO2-equivalent per kg of PHAs was evaluated by Rostkowski and coauthors [35]. Higher values were reported by Pietrini and coauthors [32], Kendall [36], and Posen and coauthors [38] and they correspond to sugar cane- and corn-derived polyhydroxybutyrate (PHB). Lower values (i.e., below 0.5 kg CO2-equivalent emitted per kilogram of biopolymer) were reported by studies that account for carbon uptake during biomass growth, thereby considering carbon storage in the bio-based polymer [26–28,30] or when the only carbon source is a waste [12,41]. Negative impacts were reported when the energy source [33] or also when waste streams from PHAs production are used for energy recovery [35].

In Figure 5, the fossil energy requirement for PHAs production reported by various authors is compared; data are in chronological order and the whole range of results found in each paper, including this study, are represented when available. Figure 5 shows also the values for four other polymers with which the PHAs are compared; two of them are fossil-based (PET and PP) and two bio-based (bio-PP and PLA).

Data range from 320 to −12 MJ per kilogram of PHAs, with median values spanning between 20 to 80 MJ per kilogram of biopolymer. The present work shows values ranging from about 80 to −10 MJ per kg of PHAs, depending on the considered scenarios. The highest values were reported by Pietrini and coauthors [32] and Sakamoto [37], where carbon sources are from dedicated crops and the whole life cycle of PHB-based end products is considered. The lowest values were reported in studies where the use of organic residues as fuel to generate electricity and steam is assumed [26,29].

**Figure 5.** Non-renewable energy demand reported in previous PHAs production studies compared to that of PHAs production investigated in this study and that calculated for other polymers. The symbol "\*" before reference number indicates that the system boundaries are "cradle-to-grave", otherwise they are "cradle-to-gate". The whole range of results of each study are represented when available.

As regards the comparison of PHAs from ADSS with other polymers, first of all, it can be noted that the bio-based polymers tend to have both lower non-biogenic GHGs emissions and lower fossil energy demand values than the fossil-based polymers, as expected. For both indicators, the PHAs' range of values overlaps the range of other polymers' values, being wider. PHAs' average value is only slightly higher than that of bio-PP and PLA, and falls within the range of fossil-based PET and PP.

In summary, the performances of PHAs produced from sewage sludge of the agro-industry analyzed in this study are of the same order of magnitude as those of other PHAs production processes found in the literature, in terms of both non-biotic GHG emissions and energy demand. Moreover, their performances also overlap with those of other fossil-based and bio-based polymers. This is a good omen, considering that there is still room for improvement for this process and that no primary data is available at the industrial scale yet.

#### **4. Conclusions**

From the analysis of the energy indicators, it was possible to observe that the use of ADSS for onsite energy production (scenario C—MostSyn2E) results in an increase of the overall renewable energy ratio (95%), while decreasing the PE (−85%), the fossil energy used (−84%), and the dependency from the external supply, despite a lower energy return and a higher total energy invested. On the other hand, the use of a mix of external sources of renewable energy, such as in scenario A3—RE/Mix+SB, can allow for a higher energy return and a lower total renewable energy invested, while keeping both the renewable energy ratio and fossil energy efficiency use at high levels. PHAs EoL can further enhance the energy return by allowing for a recovery of up to 66% of PHAs energy.

From the LCA analysis, it was possible to observe that differences among the five scenarios for PED, R+NR, GWPexc., WD, and RD are high. Smaller differences, less than one order of magnitude, were observed for EuF, PM, and POF. The only category equally impacted in all five scenarios was OD. It is possible to conclude that scenario C—MostSyn2E is the best in climate change, primary energy demand from renewable and non-renewable resources, and resources depletion categories, while scenario A1—ConvE prevails on acidification, eutrophication, and generally in categories related to local and regional impacts. The results highlight a trade-off between local/regional

impacts and global ones. It cannot be established in a straightforward way whether one scenario prevails over the others, as the ranking depends on the method adopted to establish it. However, also from the LCA perspective, the use of a mix of renewable sources can help find a balance between opposite scenarios, and contribute to significantly lowering the global impacts while keeping local ones at low levels.

PyAD is generally the life cycle phase showing the highest impacts, essentially due to the high energy demand and the related combustion processes; on the other hand, PHAs EoL usually generates avoided impacts. The innovative pertraction system has high thermal energy needs, due to the unavoidable small temperature gap, but multiplied by very large volumes, between VFAs-producing anaerobic acidogenic fermentation and PHAs-producing aerobic reactors with MMC.

Anyway, the PHAs produced from sewage sludge analyzed in this study already show environmental performances comparable to those of both fossil-based and bio-based polymers, in terms of both non-biotic GHG emissions and energy demand. New data at the industrial scale and the improvement of technology, besides the use of renewable energy sources, will make this process a competitive candidate for the production of biopolymers on a wide scale, also considering the vast availability of low-value sustainable feedstock.

**Author Contributions:** Conceptualization, D.M., P.G. and S.R.; Data curation, L.V., S.M., C.T. and C.S.; Formal analysis, L.V., S.M. and C.T.; Funding acquisition, D.M., P.G. and S.R.; Investigation, L.V., S.M., C.T. and C.S.; Methodology, L.V., S.M., D.M. and S.R.; Project administration, P.G. and S.R.; Resources, L.V., P.G., C.T., C.S. and S.R.; Supervision, S.R.; Validation, D.M., P.G. and S.R.; Visualization, L.V.; Writing—original draft, L.V., S.M. and S.R.; Writing—review & editing, L.V., S.M., D.M., P.G., C.T., C.S. and S.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by both Regione Emilia-Romagna through European Regional Development Fund, grant number PG/2015/735284 ("VALSOVIT" project), and EIT Climate-KIC through both Pathfinder program ("BioGrapPa" project) and Accelerator program ("B-PLAS" project).

**Acknowledgments:** The authors wish to thank Alberto Novi of thinkstep Ltd. and Filippo Baioli of CIRSA—University of Bologna for their support and their advice in modelling with GaBi software, and also the company Caviro Extra Ltd. for supplying the sludge samples for the experiments.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Abbreviations**



#### **References**


© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Environmental Performance of Innovative Ground-Source Heat Pumps with PCM Energy Storage**

#### **Emanuele Bonamente 1,2,\* and Andrea Aquino <sup>3</sup>**


Received: 19 November 2019; Accepted: 23 December 2019; Published: 25 December 2019

**Abstract:** Space conditioning is responsible for the majority of carbon dioxide emission and fossil fuel consumption during a building's life cycle. The exploitation of renewable energy sources, together with efficiency enhancement, is the most promising solution. An innovative layout for ground-source heat pumps, featuring upstream thermal energy storage (uTES), was already proposed and proved to be as effective as conventional systems while requiring lower impact geothermal installations thanks to its ability to decouple ground and heat-pump energy fluxes. This work presents further improvements to the layout, obtained using more compact and efficient thermal energy storage containing phase-change materials (PCMs). The switch from sensible- to latent-heat storage has the twofold benefit of dramatically reducing the volume of storage (by a factor of approximately 10) and increasing the coefficient of performance of the heat pump. During the daily cycle, the PCMs are continuously melted/solidified, however, the average storage temperature remains approximately constant, allowing the heat pump to operate closer to its maximum efficiency. A life cycle assessment (LCA) was performed to study the environmental benefits of introducing PCM-uTES during the entire life cycle of the system in a comparative approach.

**Keywords:** ground-source heat pumps; space conditioning; environmental sustainability; life cycle assessment (LCA); phase-change material (PCM)

#### **1. Introduction**

Buildings dramatically contribute to worldwide energy use and greenhouse gas (GHG) emissions. According to last official European Union reports [1,2], in 2010 the building sector accounted for about 32% of worldwide final energy consumption and 34% of global GHG emissions. More recently, the Intergovernmental Panel on Climate Change (IPCC) updates confirmed the energy share (31%, [3]) and reduced the estimates of greenhouse gas emissions (23%, [4]). However, both values are expected to increase, resulting in further energy consumption (+2 billion tons of oil equivalent [5]) for the construction of new living spaces worldwide (+230 billion square meters), causing an estimated +30% increase in GHG emissions in the next 40 years. In this context, government and international organizations are pursuing new and more severe policies specifically aimed at reducing the energy consumption of the building sector and mitigating associated emissions. As an example, the European Union aims to reduce by 90% the equivalent carbon dioxide (CO2e) emissions of buildings by the year 2050, estimating that about the 17% of the primary energy saving is achievable by retrofitting existing buildings. The potential energy savings of an existing building are strictly related to its heating and

cooling systems. During the entire building's life cycle (from construction to demolition), a major share of the required energy (80–90%) is associated with operating energy [6], and up to 60% of the total energy consumption is due to air-conditioning [7]. The enhancement of air-conditioning energy efficiency, coupled with an integration of renewable-energy technologies, is considered an effective solution to mitigate the environmental impacts of buildings [8–12].

Among the renewable energy systems for space conditioning [13], ground-source heat pumps (GSHPs) have a wide diffusion rate because of their low operative temperature. Low-temperature heat sources (soil or underground water) between 5 and 30 ◦C are found easily worldwide as they are available at reasonable depths [14,15]. GSHPs exchange heat with the first layer of the ground via properly designed borehole heat exchangers (BHEs) and, due to the low operative temperatures of the working cycle, their feasibility does not depend on the nature of the geothermal resources available at the building site [16]. Like conventional heat pump (HP) systems, GSHPs need electricity as the driving energy in order to transfer heat from a cold source to a hot source. During the heating season, the heat is extracted from the ground and given to the indoor space. Meanwhile, during the cooling season the cycle is reversed. The coefficient of performance (COP), defined as the ratio of total output energy (i.e., heat transfer to/from the building) to the total power input, measures the overall system efficiency. This is the only indicator recognized by technical regulations [17] to evaluate the environmental performance of an HP system, which is classified as a renewable energy system only if the measured COP exceeds a minimum value (3.2 to 3.5 for typical geothermal applications [18]). However, the exploitation of geothermal resources is affected by several environmental implications and a comprehensive environmental impact assessment of a GSHP cannot be limited to its energy performance. Additional negative environmental impacts should be considered, such as the risks of aquifer contamination or the contribution to soil compaction and habitat loss or disturbance. An important source of such impacts is the excavation of BHEs, which can also affect initial costs by up to 50% of the total investment [19].

Thermal energy storage (TES) is a key component of the optimization of the performance of a building energy system and the reduction of its environmental impact [20,21]. Thermal energy can be stored in three main forms: sensible heat, latent heat, and thermo-chemical energy. Sensible storage devices commonly use water as a storage material because of its well-known properties, availability, and negligible cost. However, these systems require important storage volumes with relatively high costs. Latent heat TES makes use of the enthalpy change of a material during its phase transition in order to provide higher energy density than sensible systems. As a consequence, latent heat systems need smaller storage volumes than sensible ones. Conventional materials (e.g., water, diathermic oil, etc.) change phase at temperatures that are not optimal for air-conditioning applications. On the other hand, phase change materials (PCMs) make the latent heat storage available at a large range of operative temperatures, including those favorable for indoor air-conditioning. These materials are easily suited to several heating and cooling applications due to the wide market offer in terms of thermal properties and packaging: PCMs are commonly available both in packaged volumes, as spheres, panels, etc. (i.e., encapsulated PCMs), or without packaging (i.e., free-form PCMs) [22,23].

Previous studies on GSHP systems [24,25] have shown that final COP depends on system configuration and local site conditions (e.g., the BHEs setup, the climate, the ground properties, and the HP nominal power), as well as the transient operative conditions of the system's duty-cycle: the ground's undisturbed temperature, the temperature difference between the surface and the underground, and the temperature of the water leaving the heat pump and the BHEs. These temperature fluctuations lower the system COP. The performance of GSHP gradually decays with operating time because of the continuous injection of heat into the ground (cooling cycle), which increases its undisturbed temperature [26]. The heating cycle produces a similar effect that presents as an inverted temperature trend [27]. A general explanation of this issue is that gradual COP reduction is due to an unbalanced (over the yearly season) heat exchange between the system and the ground [28]. The effects of this thermal imbalance by a two-year on-site measurement campaign were a decrease in system COP by 42% and 26% in the winter and summer season, respectively [29].

The role of a TES is essentially to decouple the thermal energy exchange of the ground from the building demand by accumulating thermal energy (heat or cold) when it is more readily available and using it upon request. This mismatch could attenuate the effects derived from temperature fluctuations in system efficiency and, therefore, several works aim to include a TES system in the standard GSHP layout. The two approaches can be identified as:


The first approach is illustrated in [30]. A laboratory-scale BHE with PCM included in its backfill was tested. The system successfully delayed the ground temperature variation and reduced the thermal interference radius (i.e., less area was required for installation). Furthermore, the constant temperature of phase change improved the BHE extraction rate. Another work ([31]) presented the exergy analysis of a hybrid GSHP system, coupled with solar panels, and indoor air conditioning was possible because of radiant panels with encapsulated PCM. The results showed that PCM was beneficial in term of first and second law efficiency; it increased with density and melting temperature.

The work presented in this paper follows the second approach. We propose the inclusion of an upstream (i.e., between the BHE and the HP) thermal energy storage in the standard GSHP layout. A similar approach is given in [32], where five different control methods for an air-to-air HP were implemented and coupled to a PCM-based TES to optimize its charge/discharge step with electricity tariffs and user demand. Solar-assisted GSHP were analyzed in [33], where the PCMs within the hot water puffer increased the amount of stored solar energy and generally improved the global COP. The importance of thermal storage in these hybrid systems was also confirmed by the experimental campaign in [34] and the numerical analysis in [35].

Finally, the works specifically focused on geothermal systems are [36,37], where PCM-based and sensible, respectively, downstream thermal storage (i.e., between the HP and the air distribution system) were coupled to a GSHP. On the one hand, in a comparison of the annual thermal energy use, at variable partial load ratio, of a boiler-based air conditioning system vs. conventional GSHP vs. TES-upgraded GSHP, the latter showed the best performance in terms of energy-savings. On the other hand, a similar configuration was seen for the cooling of an office building using different cooling storage ratios of PCM storage (i.e., the cooling capacity of PCM on the total building cooling capacity). The optimal cooling capacity was estimated to be 40%, which produced a decrease of 34% of the annual energy cost if compared to a GSHP system without TES.

In this work two different prototypal layouts were assessed and compared: a sensible-heat TES (SH-TES), where water was used as thermal storage material, and a latent-heat TES based on PCMs (PCM-TES). A first energy analysis using computational fluid dynamics of both scenarios was available in a previous work [38]. Based on these results, the work presented here is an in-depth evaluation of the respective energy performance and environmental impacts, measured and compared throughout whole life cycles, with the final aim of defining the most efficient and sustainable system configuration.

Section 2 gives a short description of the methodology. The case study is presented in Section 3. The results are shown in Section 4. Conclusions are discussed in Section 5. Extended results are given in the Supplementary Material.

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

All the potential environmental impacts of the proposed energy systems were quantified by the life cycle assessment (LCA) approach. This technique can be successfully used as a comparative analysis of different products or technologies providing the same service, in order to recognize the most efficient and environmentally sustainable option [39,40]. A life cycle assessment is a standardized and widespread approach allowing the evaluation of environmental impacts associated with any process, product, or service throughout its entire life, usually starting from raw material acquisition until the end-of-life stage. Thanks to this approach, the life cycle of the studied product could be split into several parts corresponding to relevant components and/or phases (i.e., steps of the production and usage). The relative environmental weight of each part was explicitly assessed in order to spot and, possibly, optimize, the most impactful ones.

Our study, in accordance with standard LCA guidelines [41,42], was undertaken using the following steps:


The aim of this work was to measure the environmental impact of two innovative GSHP layouts. The comparative approach was as follows: Starting from a reference layout used as a benchmark (i.e., baseline scenario), the enhancement of its energy performance and environmental impacts gained by the two proposed TES systems was measured in terms of mid- and end-point indicators. Different operative conditions were also evaluated for each system. All results were validated against experimental data measured using an existent prototype with a sensible-heat thermal storage and a small-scale laboratory unit with a latent-heat thermal storage.

The life cycle was modeled using the SimaPro v9.0.0 software [45] and the ecoinvent v3.5 database [46]. The impacts were computed according to the product category rule (PCR) document relative to electricity, steam and hot/cold water generation and distribution version 3.1 [47]. In this study, the following environmental indicators were considered

	- global warming in equivalent kgCO2;
	- photochemical oxidation in equivalent kgC2H4;
	- acidification in equivalent kgSO2e;
	- eutrophication in equivalent kgPO4 3−.
	- human health;
	- ecosystems;
	- resources.

#### **3. The Case-Study**

The system under investigation is a conventional ground-source heat pump system, used air-condition commercial buildings, characterized by a peak power of 17 kW, a yearly demand of 19,965 kWht of heating energy, and 4918 kWht of cooling energy, for a total of 24,883 kWht/year provided by the heat pump [48]. The baseline configuration (BAS) of this system consists of:

• a 17-kW reversible ground-source heat pump;

•

•

• and a total of 3 double-loop boreholes, each one 120 m deep with an average extraction capacity of 46.5 W/m measured by the ground response test.

This setup was upgraded by including upstream thermal storage (uTES) between the borehole heat exchangers and the heat pump [49]. The uTES was sized based on the maximum daily energy need instead of the maximum power. This new layout was designed to exchange heat with the ground even when it was not needed by the building, and using the thermal storage as a peak-shaving component. While average power exchanged with the ground on the one side and with the heat pump on the other side was the same, the maximum power required by the ground was less than the maximum power required by the heat pump as the former works with a higher duty cycle. On-site monitoring of a real-building prototype [50] confirmed that this layout was able to successfully decouple the energy exchange between the ground and the HP, resulting in a substantial reduction of peak geothermal power and, therefore, reduced borehole size (i.e., shorter/fewer boreholes to be drilled). Figure 1 shows the upgraded layouts with respect to the BAS scenario. They both use the same 17 kW heat pump while using just 1 borehole in combination with an uTES properly configured for each setup as follows:


**Figure 1.** Layouts of the baseline setup (**left**) and the upgraded setup using an upstream thermal storage (uTES) (**right**).

As shown in a previous LCA study [48], the sensible-heat uTES was already able to reduce the environmental impact of the conventional layout. However, this preliminary analysis was limited to the evaluation of the environmental impact of the SH-uTES scenario, proposed first as an upgraded conventional BAS system. The inclusion of a latent-heat TES was already mentioned as a potential future development to reduce the storage volume, without any reference to its environmental issues. Similarly, the preliminary CFD energy analysis [38] detailed in the following paragraph exclusively calculated in terms of global COP, the performance of the PCM-uTES scenario. A more detailed assessment of the environmental impact of this system is presented in the current LCA analysis, where the environmental impacts of the PCM-TES system were measured during the life cycle of the plant (20 years), considering the energy consumption of a reference year. The obtained results were compared with a baseline scenario (BAS) and a sensible-heat thermal storage (SH-TES) scenario. For all three scenarios, two hypotheses were proposed: (a) electric energy is taken from the grid, and (b) electric energy is produced entirely from photovoltaic panels.

#### *3.1. PCM Modeling and Validation*

The energy performance of the PCM-TES scenario was already calculated by a comparative CFD analysis in [38], between the sensible and the latent uTES. Both systems abide by the same daily energy demand schedule, implemented on the basis of yearly energy consumption (see Section 3) and building characteristics [50]. The PCM-TES scenario simulated by CFD analysis provided two closed loops connected to the heat pump and the borehole, respectively. The heat transfer fluid within each loop entered a serpentine tube that crossed two different PCM stacks. The heat transfer between the borehole and PCM during the winter season was modeled via the following steps:


The results of this preliminary analysis clearly showed the twofold benefits of the PCM-TES scenario. On the one hand, the daily needs were fully covered by 10 times less thermal storage and, on the other hand, the reduction of storage temperature oscillations, as an effect of phase transition, increased the overall COP from 3.4, for both heating and cooling, to 4.13 and 5.89 for the heating and cooling modes, respectively.

An experimental campaign for the CFD model validation using data from a 1-kWh scale prototype is currently ongoing [51]. Figure 2 shows the experimental apparatus. It consists of two open loops connected to a boiler and a chiller, respectively. Each loop has its circulation pump. The PCM-storage connects both loops and collects the flows; these get mixed within the storage and exchange heat with the PCMs. (They present the same properties used in the CFD analysis.) The heating cycle took place in the following way:


Data were measured by two calorie flow meters placed at the outlet sections of the storage. The PCM phase transition was modeled as function of their temperature (TPCM) and the thermo-physical properties (Table 1). When TPCM achieved the melting/solidification point, the phase change started and the heat was stored in latent form along the transition region (±0.25 ◦C with respect to the nominal transition temperature). Preliminary experimental data collected from the laboratory unit are shown in Figure 3. Good agreement between the simulated and measured outlet temperature (Tout) was observed using nominal PCM properties before any model calibration, strengthening the above estimates on performance improvement. CFD simulations were performed using a constant power equal to the average power measured during the experimental campaign. More detailed analyses will be presented in future works.



**Figure 2.** (**left**) Overview of the experimental layout. 1: phase-change-material (PCM) thermal storage, 2: circulating pumps, 3: chiller, 4: heater, 5: temperature/flow meters. (**right**) Close view of the PCM storage and top view of its geometry model.

**Figure 3.** RT-6 PCM transition region: simulated vs. experimental data.

#### *3.2. The Life Cycle Inventory*

The three scenarios differ by borehole number, storage type, and electric energy consumption for heat production (the incidence of raw materials is reported in Table 2); the final electric energy consumption of each scenario is given by the respective COP values (Table 3). These values can be considered a conservative estimate as no contribution from cooling (heating) material is considered for the size of the heating (cooling) material (i.e., the effect of sensible heat of the cooling-optimized PCM is not considered on heating and vice versa).


**Table 2.** Material inventory of each scenario.

**Table 3.** Coefficient of performance (COP) and yearly electricity consumption of each scenario.


#### *3.3. Functional Unit and System Boundaries*

The functional unit in this study was 1 kWh of thermal energy (kWht) provided by the heat pump to the thermal energy distribution system. The impacts associated with the functional unit were obtained considering the energy inputs during the reference year and allocating the results based on the total production of heating and cooling energy. The system boundaries included the production of input materials for the implementation of the system from the boreholes to the heat pump, excavation activities for the boreholes and the sensible-heat storage, electric energy consumption, and end-of-life of the thermal storage and heat pump.

#### **4. Results**

#### *4.1. Mid-Point Indicators*

Results for mid-point indicators are shown in Tables 4 and 5, considering electric energy from the grid and from photovoltaic panels, respectively. The percentage variation was relative to the corresponding baseline scenario. We observed that, in terms of overall GHG emissions, the PCM-TES scenario was the most systematically competitive thanks to its higher overall COP that reduced primary energy consumption. This particularly impacted the case where electric energy was taken from the grid (−16% and −17% with respect to BAS and SH-TES scenarios, respectively). It can be noted that the SH-TES scenario had slightly higher impact as a direct consequence of COP deterioration (see Table 3). If electric energy was produced entirely by photovoltaic panels, the average reduction in impact indicators for the PCM-TES scenario would be approximately 69% (76% for CO2e). Even in the PV hypothesis, the PCM-TES scenario performed best (−11% and −3.5% with respect to BAS and SH-TES scenarios, respectively). In all cases, the most impactful process was the consumption of electric energy. For the baseline scenarios, it accounted for an average of 85% using the grid hypothesis (Table 6) and 64% using the PV hypothesis (Table 7). Such values dropped to 64% and 41% for the PCM-TES scenarios, respectively. This result was produced by the fact that emission factors for PV production (multi-Si, 3 kWp) were, on average, 75% lower than emission factors for grid electricity (Italy, low voltage).


**Table 4.** Mid-point indicators for the three scenarios (electricity from grid).

**Table 5.** Mid-point indicators for the three scenarios (electricity from photovoltaic sources).


**Table 6.** Incidence of highest impact processes (electricity from grid).



Tables 6 and 7 clearly show that processes related to the PCM-uTES introduced non-negligible impacts. From a life cycle perspective, it is, therefore, worth investigating if possible optimization of such processes might be viable and rewarding. Paraffins and steel, with similar shares, account for approximately 9% using the grid hypothesis, and 31% using the PV hypothesis. Considering these values, an optimal scenario might be foreseen (as discussed in Section 4.2) including the selection of bio-based PCMs and the minimization of steel in the storage structure with respect to the non-optimized prototypal layout.

#### *4.2. Global Warming*

The system life cycle was divided into six parts according to physical and usage boundaries. In addition, a detailed analysis on the global warming indicator was performed using this partition. The tree view of the PCM-uTES scenario considering electricity from the grid is given in Figure 4. The LCA indicators were:


**Figure 4.** Typical tree view of the system life cycle (shown as global warming for the PCM-uTES scenario with energy from the grid).

The relative incidence of each part of the total GHG emissions is shown in Table 8. The phase-change material thermal energy storage was characterized by an impact higher than the water storage, mainly because of the steel and PCMs. However, a large part of its impact (44%) was compensated by the end-of-life indicator, during which steel can be recycled.


**Table 8.** Incidence of different life-cycle assessment (LCA) parts on global warming.

Details of the PCM-uTES are given in Table 9. PCMs and steel accounted for the highest impacts (42% and 40%, respectively). The amount of steel was computed using a direct proportion based on the 1-kWh lab prototype of storage. It was, therefore, straightforward to hypothesize lower incidence of steel for an optimized production-ready component. An optimal scenario was finally foreseen using bio-based PCMs and a reduced amount of steel. In the case of bio-based alternatives, the emission factor for PCMs could be lowered from 0.747 kgCO2e/kg (ecoinvent, generic paraffin) down to 0.278 kgCO2e/kg [52]. Considering the optimization of the PCM storage design, a reduction of 0.5 mm in the wall and tube thickness could be hypothesized for the production step without compromising the structure strength, resulting in a savings of approximately 40% of raw materials with respect to the non-optimized prototypal design.

**Table 9.** Details of the PCM-uTES.


The optimal scenario (PCM-TESopt, PV), realized using the above criteria and tested using the PV hypothesis, resulted in a 51% decrease in the global warming impact associated with the PCM-uTES, and an additional reduction of 10% with respect to the PCM-TES, PV scenario. The overall emission factor for the optimal scenario would be 0.028 kgCO2e/kWht, −78% and −21% with respect to BAS, grid and BAS, PV scenarios, respectively.

#### *4.3. End-Point Indicators*

The results of the end-point indicators are shown in Figure 5. Impacts on human health are responsible for 93% of average overall impacts. As part of the electricity-from-grid hypothesis, the PCM-TES scenario showed a decrease of 10% with respect to the baseline. On the other hand, an increase of 1.3% was observed using the photovoltaic hypothesis. In this case the PCM-uTES introduced a positive impact (+25.6%) that was slightly higher than the overall reduction due to shorter boreholes (−13.5%) and lower energy demand for heating (−7.4%) and cooling (−3.3%).

**Figure 5.** End-point indicator results.

The optimal scenario would likely lower the impacts of storage, resulting in the overall best performing layout, also in terms of end-point indicators (−64% and −13% with respect to BAS, grid and BAS, PV, respectively).

#### **5. Conclusions**

The LCA analysis was applied to a customized ground-source heat pump system. The innovative layout featured upstream thermal energy storage that was able to decouple the thermal fluxes from/to the ground and from/to the heat pump. Following the results of the experimental campaign, an upgrade TES component was evaluated using phase change materials instead of water. We showed that PCMs could reduce the volume of storage by a factor of 10 and enhance the system COP because of temperature stabilization during the charge/discharge cycles. The environmental performance of the system was evaluated in terms of mid- and end-point indicators per unit of thermal energy provided to the building considering the following two hypotheses: (1) energy is taken from the grid, and (2) energy is supplied by photovoltaic panels.

We found that the exploitation of phase-change materials allowed for sensible reduction of electric energy consumption (−18%) thanks to an improved COP both in terms of heating (from 3.50 to 4.13) and cooling mode (from 4.00 to 5.89). This resulted in an overall variation of mid-point indicators of −16% and −11% with respect to hypotheses (1) and (2), respectively. End-point indicator variation was −10% and +1.3%, respectively. The increase in the end-point indicators with respect to the PV hypothesis was due to the high relative impact of the PCM storage itself. Considering further, realistic, optimization of the prototypal storage design using the PV hypothesis, mid- and end-point impacts could be as low as −78% and −64%, respectively, with respect to the baseline scenario (grid hypothesis), making it the most sustainable option.

In terms of global warming, the baseline scenario was characterized by 1.30 kgCO2e/kWht (grid) and 0.0356 kgCO2e/kWht (PV). The grid-hypothesis result was consistent with a previous study (0.156 kgCO2e/kWht [50]), the difference being the higher value of the emission factor of electricity in the database used (ecoinvent v 3.2 [53]). The proposed PCM-TES layout could lower impacts by as much as 0.108 kgCO2e/kWht and 0.0312 kgCO2e/kWht for the grid and PV hypothesis, respectively. This would make such a solution very attractive to sensibly reduce both energy consumption (−21%) and the environmental impacts associated with space conditioning.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1996-1073/13/1/117/s1, Table S1: Mid-point indicators (CML-IA baseline V3.05) for the three scenarios (electricity from grid), Table S2: Mid-point indicators (CML-IA baseline V3.05) for the three scenarios (electricity from PV), Table S3: Incidence of most-impacting processes (BAS, grid), Table S4: Incidence of most-impacting processes (SH-TES, grid), Table S5: Incidence of most-impacting processes (PCM-TES, grid), Table S6: Incidence of most-impacting processes (BAS, PV), Table S7: Incidence of most-impacting processes (SH-TES, PV), Table S8: Incidence of most-impacting processes (PCM-TES, PV).

**Author Contributions:** Conceptualization, E.B. and A.A.; methodology, E.B.; software, E.B.; data curation, E.B. and A.A.; writing—original draft preparation, E.B.; writing—review and editing, E.B. and A.A.; supervision, E.B.; project administration, E.B.; funding acquisition, E.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Fondazione Cassa di Risparmio di Perugia, grant number 2017.0237.021.

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

#### **References**


© 2019 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 (http://creativecommons.org/licenses/by/4.0/).

*Article*

## **The Environmental Potential of Phase Change Materials in Building Applications. A Multiple Case Investigation Based on Life Cycle Assessment and Building Simulation**

**Roberta Di Bari 1,\*, Rafael Horn 1, Björn Nienborg 2, Felix Klinker 3, Esther Kieseritzky <sup>4</sup> and Felix Pawelz <sup>4</sup>**


Received: 15 April 2020; Accepted: 10 June 2020; Published: 12 June 2020

**Abstract:** New materials and technologies have become the main drivers for reducing energy demand in the building sector in recent years. Energy efficiency can be reached by utilization of materials with thermal storage potential; among them, phase change materials (PCMs) seem to be promising. If they are used in combination with solar collectors in heating applications or with water chillers or in chilled ceilings in cooling applications, PCMs can provide ecological benefits through energy savings during the building's operational phase. However, their environmental value should be analyzed by taking into account their whole lifecycle. The purpose of this paper is the assessment of PCMs at the material level as well as at higher levels, namely the component and building levels. Life cycle assessment analyses are based on information from PCM manufacturers and building energy simulations. With the newly developed software "Storage LCA Tool" (Version 1.0, University of Stuttgart, IABP, Stuttgart, Germany), PCM storage systems can be compared with traditional systems that do not entail energy storage. Their benefits can be evaluated in order to support decision-making on energy concepts for buildings. The collection of several case studies shows that PCM energy concepts are not always advantageous. However, with conclusive concepts, suitable storage dimensioning and ecologically favorable PCMs, systems can be realized that have a lower environmental impact over the entire life cycle compared to traditional systems.

**Keywords:** phase change materials; PCM; thermal energy storage; life cycle assessment (LCA); Storage LCA Tool; Speicher LCA

#### **1. Introduction**

In a context calling for more affordable, sustainable and modern energy [1,2], the building sector is under particular attention as one of the main drivers of energy consumption. While most of the primary energy supply is delivered for energy production [3], heating and hot water in households alone account for 79% of the total final energy use. In addition, despite efforts made to improve energy efficiency, the final energy use for space conditioning grew from 118 million TJ in 2010 to around 128 million TJ in 2018 [4].

A strategy for energy saving is the combination of a source of renewable energy with thermal energy storage, which can be realized for heating and cooling systems not only through sensible heat storage materials but also through phase change materials (PCMs) and thermochemical materials (TCMs) [5,6]. Processes that enable thermal storage are reversible adsorption–desorption reactions, exothermic in adsorption and endothermic in regeneration [7]. Typically, water vapor is used in combination with thermally stable and inexpensive nanoporous materials belonging, for example, to the class of zeolites (Zeolite 13X) or composite sorbents [8,9]. Composite materials, such as multiwalled carbon nanotubes/lithium chloride (MWCN-LiCl) especially, have proven to be advantageous due to their heat storage density with both water and methanol as working fluid [10]. For PCMs, latent heat storage can be achieved through state of matter changes (solid → liquid and liquid → solid). While PCMs also allow for sensible heat storage (i.e., they store heat by raising the temperature of a liquid or solid and they release it with the decrease of temperature if required), they can absorb large amounts of heat at their melting point with constant temperature until all the material is melted [11]. Solidification of PCM occurs when the ambient temperature around the liquid material falls below the crystallization temperature, which leads to the release of the stored latent heat [12]. PCMs, which are mainly hydrated salts or paraffins, are available in any required typology; they can be organic or inorganic and suit any temperature in a range from −50 to 100 ◦C [13,14].

The advantages of innovative storage systems that incorporate PCMs in the building sector have already been investigated: in comparison with a traditional storage concept, containment volumes are reduced, allowing more storage capacity. The energy input/output occurs longer with almost constant temperatures. As a consequence, insulation of latent storage systems may be less sophisticated and expensive [15]. Together with energy storage systems, they can be directly attached to building components, e.g., walls or chilled ceilings, or included into cooling devices. In such cases, a reduction of direct greenhouse gas emissions during a building's operational stage can be achieved through energy savings [12,16,17]. However, these savings may be overcompensated by additional impacts caused by storage material production or other activities not necessary in conventional systems. As a consequence, innovative storage systems can contribute to tackling climate change if the overall environmental impact during the life cycle can be reduced. This requirement is also indirectly requested by 7th sustainable development goals (SDG7), which calls a reduction in environmental impacts that drive climate change [18].

The environmental impacts can be evaluated through life cycle assessment (LCA), a technique comprised of a set of procedures for compiling and examining the inputs and outputs of materials and energy and the associated environmental impacts directly attributable to the functioning of a product or service system throughout its life cycle. International Standard ISO 14040 establishes LCA principles and framework [19], while ISO 14044 defines requirements and guidelines [20].

With regard to PCMs, only a few LCA studies and investigations are available so far. However, existing studies are often very limited in detail or focus only on a specific application [21]. Existing studies consider only cost aspects or energetic evaluation in operation [22] without a link to the environmental impact evaluation over the entire life cycle of the storage material. When available, the environmental evaluation is oftentimes only carried out at the laboratory scale (1 m × 1 m × 1 m cube) without reference to real application scenarios. Other works carry out analyses on a large scale and neglect a life cycle perspective [23].

In an effort to bridge the gap between the environmental promise of innovative storage materials and their actual environmental impact while considering system complexity and modularity, the "Storage LCA" (German "Speicher LCA") project was conducted, with funding from the German Federal Ministry of Economics and Technology (BMWi) and in collaboration with Fraunhofer ISE, ZAE Bayern and University Stuttgart. Some of the project results are presented in this paper [24]. The research focuses on a wide range of organic paraffins and salt hydrates with melting points between 10–15 ◦C for cooling systems and 58–62 ◦C for heating systems. Central heating systems combine the advantage of renewable energy supply through solar collectors with PCM thermal storage. With respect

to centralized cooling systems, cooling devices are accompanied by PCM cold storage that allows for a cooling load shift from day to night, increasing the efficiency of cold production. Furthermore, room-integrated chilled PCM ceilings or PCM ventilation systems are considered. The consideration of sensible, latent and thermochemical storage concepts, as well as their energetic performances evaluated through energy simulation, enables comprehensive environmental assessments of the materials and systems used for thermal energy storage in buildings. Furthermore, the data collection for LCA is based on up-to-date information coming from manufacturers and material developers [13,14]. Previous works related to the project provided results at the material and component levels and already demonstrated the benefits of PCM configurations with high storage density in comparison with, for example, water storage [15].

In the present study, the created "Storage LCA Tool" is presented [25]. Based on the obtained LCA and energy simulation data platform, both practitioners and experts in energy and storage can carry out analyses on different levels. With the help of graphs and numerical results, users are able to decide whether innovative storage systems are advantageous in comparison with conventional systems. This paper analyses the overall environmental assessment of PCMs applied in buildings and provides insight into the general effectiveness and environmental benefits of PCM storage systems.

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

For the analysis, a three-level approach was established. Terms used in this work and their respective definitions are given below [6].


Environmental database and energy simulation results were combined in a common data platform, on which the final building LCA was provided consistently with the selected analyzed case study. The environmental database contained results coming from LCA analyses carried out in Gabi ts software (Version 8.0, Thinkstep, a sphera company, Stuttgart, Germany) [26] (updated in 2019), according to the ISO 14040 and 14044 standards [19,20] and following specifications and suggestions for buildings [27–32]. Building energy simulations were carried out in TRNSYS (Version 18, Thermal Energy System Specialists LLC, Madison, WI, USA) [33].

#### *2.1. LCA Specifications*

In this work, LCAs were carried out at the material, component and energy concept levels (see Table 1). For the cradle-to-grave analyses, the considered life cycle modules were production stage (A1–A3, including raw material supply, transport and manufacturing) and end-of life (C3, C4 and D modules, including waste processing, disposal and eventual benefits due to recycling) [27]. For storage concept analyses, the operational building energy use was included (B6 module according to EN 15804 [27]). At the building level, a 20-year lifespan was considered, with reference to a nominal service life of the installation system without component replacement (module B4 according to EN 15804 [27]). Functional units are related to each level and specified in the following subsections.


**Table 1.** Life cycle assessment (LCA) specification according to [24,25,28].

The investigated impact categories were selected in the project due to the goal and scope defined therein, including expectations of energy storage experts in regard to the tool content. For the evaluation of overall energy savings, the primary energy demand total (PEtot) indicator was chosen; this can be divided into primary energy renewable total (PERT) and primary energy nonrenewable total (PENRT). The global warming potential (GWP) expresses the emission of greenhouse gases in kg CO2 equivalent. The life cycle impact assessment (LCIA) characterization method suggested for buildings in EN15084-Annex 2 [27] was used.

#### *2.2. Analysis at the Material Level*

For the PCM analysis, a wide range of commercially available materials were investigated with the support of Rubitherm Technologies GmbH, a PCM producing company. The investigated materials were mainly organic paraffins (RTXX) [13] and inorganic salt hydrates (SPXX) [14] with different melting points in both encapsulated and non-encapsulated variants (Table 2).

**Table 2.** Analyzed phase change materials (PCMs), listed as organic materials and salt hydrates.


For storage material analyses, impacts were given by the functional unit (impact/kWh material storage capacity). These were obtained through the unit transformation as shown by Equations (1) and (2):

$$\text{GWP}\left(\frac{\text{kg CO}\_2\text{ eq.}}{\text{kWh}}\right) = \text{GWP}\left(\frac{\text{kg CO}\_2\text{ eq.}}{\text{kg}}\right) \cdot \frac{1}{\text{Ed}\left(\frac{\text{kWh}}{\text{kg}}\right)}\tag{1}$$

$$\text{PEtot} \left( \frac{\text{MJ}}{\text{kWh}} \right) = \text{PEtot} \left( \frac{\text{MJ}}{\text{kg}} \right) \cdot \frac{1}{\text{E}\_{\text{d}} \left( \frac{\text{kWh}}{\text{kg}} \right)} \tag{2}$$

where Ed is the PCM energy density, calculated by taking into account the PCM's physical properties and the working temperatures of the distribution system. The material properties were based on our own experimental testing supported by technical datasheets of PCM producers as well as the collection of information about manufacturing.

The material analysis was supported by the theoretical cycle-based payback time, namely the ratio between impacts due to PCM lifecycle and conventional reference systems (Equation (3)).

$$\text{Payback}\_{\text{GWP}} \left( \frac{\text{kg CO}\_2 \text{ eq.}}{\text{kWh}} \right) = \text{GWP}\_{\text{PCM}} \left( \frac{\text{kg CO}\_2 \text{ eq.}}{\text{kWh}} \right) \cdot \frac{1}{\text{GWP}\_{\text{ref}} \left( \frac{\text{kg CO}\_2 \text{ eq.}}{\text{kWh}} \right)'} \tag{3}$$

The energetic payback-cycles indicate the theoretical minimum number of full charge and discharge cycles the material must endure to have environmental benefits compared to conventional systems (without storage). At the material level, this number does not consider any capacity losses or other use-phase-related impacts. It thus provides a theoretical minimum value that systems have to achieve from an environmental perspective to provide an advantage compared to the chosen reference. The selected reference systems were the following:


#### *2.3. Analysis at the Component Level*

At the component level, functional units were established singularly. By considering, for instance, a storage containment, the storage volume (m3) could be recommended as functional unit. In order to expand the data basis for LCA, several materials were selected at the component level based on implemented systems or recommendations from experts and users. Together, the components formed the storage system (see Table 3), which, in addition to the actual storage, also included other building installations such as the piping. For most components, an LCA on constructive aspects was sufficient, and production (A1–A3) and end-of-life (C + D) were considered. Since the focus of this work is on PCM storage systems, it does not show water-based energy storage systems and their respective components.


Storage containment HDPE 1, steel 4, stainless steel 1,4 Insulation storage Mineral foam, EPDM <sup>2</sup> foam 4; XPS <sup>3</sup> PCM containment/capsules Aluminum, steel, HDPE <sup>1</sup>

**Table 3.** Storage component list with their respective available materials.

<sup>1</sup> High-density polyethylene. <sup>2</sup> Ethylene propylene diene monomer rubber. <sup>3</sup> Extruded polystyrene. <sup>4</sup> Only for sensible water storage.

#### *2.4. Analysis at the System Level*

Analysis at the whole system level considered the building components and the operational stage. Environmental impacts were calculated by multiplying material impacts by their quantities. These were scaled by the established functional unit, namely the yearly impact per net surface unit (kg CO2 eq./m2 net surface year).

In order to determine the energy demand of all investigated systems, they were examined in several building types in a building simulation using TRNSYS 17 or TRNSYS 18 (Table 4) [32]. Each building type was considered in different climate zones (Athens, Strasbourg and Helsinki) and with different insulation levels. The insulation levels refer to insulation standards in the three different climate zones around the year 1990, where "no insulation", "little" and "moderate" refer the insulation standards of Greece, Central Europe and Northern Europe, respectively. Buildings of this age are particularly interesting for system-level analyses, as the existing heating and cooling systems of these buildings are at the end of their life cycle and therefore need to be replaced. Simulations of energy-efficient buildings

are included as well. Together with the system provided with thermal storage, the reference system without thermal storage is simulated for comparison (Table 4).


**Table 4.** Energy concept systems list.

#### *2.5. Storage LCA Tool*

"Storage LCA Tool" is available online for free [22]. The tool supports storage experts and practitioners by providing scientific support in the selection of environmentally suitable thermal storage materials and concepts for building applications. Comprehensive environmental assessments based on the LCA software Gabi ts [26] allow environmental assessments and comparisons at the material, component and system levels. In addition to the environmental database, simulation results have been included under the previously mentioned different boundary conditions.

All relevant components have been identified through expert surveys among the participating institutions and participants of the IEA Task 58—"Materials & components for thermal energy storage of the solar Heating and cooling" program of the International Energy Agency [33]. Based on the surveys, standard component and system setups have been derived and their parts have been modeled for LCA [15,22]. The models were based on ÖKOBAUDAT datasets [34] and our own models and were complemented by a material database to create and assess specific components that were not natively considered.

The tool is a useful instrument for all expert and nonexpert users. For this reason, two different tool usage modes are available (Figure 1).

**Figure 1.** Storage LCA Tool functionality: advanced and basic modes.

The Basic Mode enables the user to perform comparisons of storage materials at predefined working temperatures. Minimum and maximum temperatures are defined according to the melting temperature range of the selected PCM and operating temperature of the selected distribution system. At the system level, a simplified analysis of innovative storage system layouts at the building level can be conducted and compared with a reference system. User enters information about (1) building type, (2) location, (3) insulation level, (4) energy storage and supply concept. Depending on such specifications, a drop-down list of available storage system layouts is generated and the chosen one analyzed. The final analysis is based on default storage components/combinations as well as default energetic TRNSYS simulations. Analogously, reference systems layouts are automatically generated and analyzed based on LCIA and energy simulation results. Results can be visualized in numerical or graphical simplified form easy to understand.

Within the Advanced Mode, default storage components and systems can be customized individually and analyzed. Moreover, individual energy demand simulations may be integrated and a more detailed analysis performed. To ensure consistent results, advanced mode analyses are suggested for expert users and only if comprehensive information about storage system and building installations are available. Results visualization can be personalized as well through Pivot tables and diagrams according to the scope of the analysis.

#### **3. Results**

In this section, with the support of "Storage LCA Tool", significant innovative storage materials as well as storage system concepts are selected and compared to their respective reference systems as demonstrative examples.

The chosen examples of storage system concepts for heating and cooling using innovative centralized PCM storage all use salt hydrates as storage materials. In the selected innovative cooling system, the relevance of the location for the selection of suitable and effective storage systems was emphasized. A heating concept has been selected in order to prove the great advantages of coupling PCM storage systems and solar collectors.

Generalized results for all energy concepts are presented in Section 4.

#### *3.1. Material Level*

To provide a broad spectrum of materials, the LCA makes use of a generic modeling approach, assuming similar production processes and routes wherever possible. This not only allows for comparison of materials with different technology readiness levels (TRLs), but also focuses on the impacts of the underlying raw materials. Detailed information at the material level is provided in the tool documentation [25]. As an example, a comparison between paraffin RT10HC, the salt hydrate SP15 and water is presented (Tables 5 and 6). Physical properties are derived from producers' technical documentation (Table 5), while the environmental assessment is carried out by Storage LCA Tool (Table 6), which is able to compare up to three materials [25]. Both PCMs are assumed to be operated in the range of 10–16 ◦C. As already noticed by previous works, paraffins show higher GWP impacts and PENRT [6,15]. The required raw materials strongly influence the material's global warming potential. For instance, the paraffin model only considers the petroleum-based route as a refinery by-product. In comparison with salt hydrates, paraffins consequently have higher environmental impacts (GWP of RT10HC is almost 50% higher in comparison with SP15, and the PENRT is 5 times higher) [35]. The selected system conditions result in long payback times for RT10HC (Figure 2). Therefore, from the environmental perspective, the salt hydrate is preferable in this case. Results analysis for all PCMs are available in Appendix A.


**Table 5.** Material properties of RT10HC (paraffin) and SP15 (salt hydrate) in comparison with water. Reproduced from [13,14], Rubitherm: 2019.

**Table 6.** LCA analyses for RT10HC (paraffin) and SP15 (salt hydrate) in comparison with water. Reproduced from [25], IABP: 2019.

**Figure 2.** Environmental assessment through "Storage LCA Tool" of RT10HC and SP15: (**a**) global warming potential (GWP); (**b**) primary energy nonrenewable total (PENRT); (**c**) PENRT payback cycles. Reproduced from [25], IABP: 2019.

#### *3.2. Component and System Levels*

#### 3.2.1. Helsinki—North European Insulation Standard

A cooling system with PCM storage in an office block located in Helsinki with moderate insulation level (North European insulation level) serves as an example. Together with the storage components (see Table 4), further elements constitute the overall storage energy concept, as shown in Figure 3a. The PCM storage is connected through valves and pipework to the building where the cold is distributed via chilled ceilings. In Figure 3b, the associated reference system for the comparison is represented. Unlike the innovative one, it does not entail any thermal storage. The water chiller and the distribution

system have the same features [24]. For the selected location, with a mean temperature of 6.05 ◦C, the annual cooling demand is 4.08 kWh/m2 a (see Appendix B).

**Figure 3.** System layouts of (**a**) PCM storage for a cooling system and (**b**) a reference system. Reproduced from [24], Fraunhofer ISE 2019.

Table 7 reports comprehensive system information concerning materials and quantities. On this basis, LCIA is carried out, with the results reported in Table 8. Here, impacts are grouped according to their respective system (storage, cooling, distribution) and shown in both absolute and relative form.


**Table 7.** System information, including storage components. Reproduced from [25], IABP: 2019.


**Table 8.** Environmental assessment of system, including storage components Reproduced from [25], IABP: 2019.

LCIA results (Table 8) show that the storage system is the main factor responsible for the evaluated impacts. Among storage components, the PCM (SP15) and its insulation (XPS) present the highest global warming potential (GWP) and primary energy nonrenewable demand (PENRT), respectively.

Finally, the system is compared with its respective reference system. As demonstrated by the results (see Figure 4), compared with the reference system, the inclusion of a PCM storage system entails greater impact due to production. Energy savings during the operational stage due to the increased efficiency in cold production enabled by the inclusion of thermal energy storage do not compensate for this initial impact over a 20-year analysis. As a result, the total GWP and PEtot are slightly higher for the innovative system in the considered setup and climate zone.

**Figure 4.** PCM storage for a cooling system in comparison with the reference system. Reproduced from [25], IABP: 2019.

The high environmental impact may be due to the selected boundary conditions. The energy simulation results included in the tool show an electricity demand of 616 kWh per year (kWh/a) for cold production and distribution. The reference system in turn consumes 669 kWh/a. The electricity savings for the innovative system amount to only 8%. Hence, in this case, the investigated innovative cooling system in a building with standard insulation level may not be the most effective solution due to the selected rather cool Helsinki climate zone.

#### 3.2.2. Athens—North European Insulation Standard

A further analysis can be carried out for the same cooling system, water chiller + SP15 storage, in an office building with moderate insulation located in Athens. The new selected location has a mean temperature of 16.54 ◦C, and the annual cooling demand is 48.01 kWh/m2a (see Appendix B). The total primary energy demand of the innovative system is reduced compared to the reference, and there are slight reductions of the total GWP. According to the simulation results included in the tool, this system has an electrical energy demand of 7445 kWh/a due to water chiller and cooling distribution. The reference system in turn has a total demand of 9039 kWh/a. On one hand, the cooling demand is higher due to the selected location, but on the other hand, an innovative system can provide greater benefits, with an 18% reduction in electricity demand for the cooling system. As a result, despite the high GWP due to SP15 production, the impacts can be more than compensated for. For the whole lifecycle, the selected innovative system records a GWP reduction of almost 10% (Figure 5).

**Figure 5.** PCM storage for a cooling system in comparison with the reference system. Reproduced from [25], IABP: 2019.

#### 3.2.3. Centralized Heating System with PCM Storage: Office Block in Helsinki

In contrast to the previous case study, the same building with an equal energy standard is now provided with a centralized heating system (see Figure 6a). A storage system using the salt hydrate SP58 is considered. The PCM storage with a volume of 16.49 m<sup>3</sup> is combined with solar collectors (with an area of 149.40 m2). The reference system consists of a gas boiler with domestic hot water storage and underfloor heating (see Figure 6b) [24]. For the selected location with a mean temperature of 6.05 ◦C, the annual heating demand is 137.39 kWh/m2a (see Appendix B).

**Figure 6.** System layouts of (**a**) PCM storage for a heating system and (**b**) a reference system. Reproduced from [24], Fraunhofer ISE 2019.

The results of the analysis demonstrate the effectiveness of this choice. In terms of both GWP and PEtot, high savings are recorded. This is in the first place due to the solar collectors, which increase the total energy savings. The simulation results included in the tool show an electricity demand related to pumps and the collector circuit of 881.8 kWh/a and an energy demand of 52.74 kWh/a for heating provided by the gas boiler. Cooling demand is not included. For the reference system, the lack of solar collectors reduces electricity demand to 71.4 kWh but considerably increases the gas demand, which reaches 79.38 kWh/a (+44% in comparison with the innovative system). The gas demand strongly affects the final environmental assessment. The innovative system shows environmental advantages with a 46% reduction of the total GWP (Figure 7).

**Figure 7.** PCM storage for a heating system in comparison with the reference system. Reproduced from [25], IABP: 2019.

As the previous example, the storage material (SP15) is the main factor responsible for evaluated impacts. Storage thermal insulation (XPS) has only a minor influence on the overall evaluation (Table 9).


**Table 9.** Environmental assessment of the system, including storage components. Reproduced from [25], IABP: 2019.

#### **4. Discussion**

Since results typically vary from case to case, evaluations of the utility of a PCM application cannot be based on the results of a single case, such as those considered in the previous section. In order to derive generalizable conclusions and to identify eventual common characteristics from the combined energetic–environmental investigations, all results coming from "Storage LCA Tool" have been assessed in a meta-analysis, i.e., data from multiple case studies were combined.

Environmental impacts have been gathered and sorted by storage system, PCM storage material, building type, insulation level and location. For each case, impacts (GWPinn sys and PENRTinn sys) of

the innovative system have been divided by the impacts of the associated reference system (GWPref sys and PENRTref sys), according to Equations (4) and (5). Two ratios are thus calculated: (1) a GWP ratio over the whole lifecycle, which describes the environmental performance, and (2) a primary nonrenewable energy demand ratio over the building use phase (B6 module), which is used to analyze the efficiency of the storage system.

$$\text{GWP ratio} = \frac{\text{GWP}\_{\text{inn sys}} \text{(kg CO}\_2 \text{ eq.)}}{\text{GWP}\_{\text{ref sys}} \text{(kg CO}\_2 \text{ eq.)}},\tag{4}$$

$$\text{PENRT ratio}\_{\text{B6}} = \frac{\text{PENRT}\_{\text{inn sy,B6}}(\text{MJ})}{\text{PENRT}\_{\text{ref sy,B6}}(\text{MJ})} \tag{5}$$

Results are visualized in an x–y diagram, where the GWP ratio is plotted on the y-axis and energy efficiency ratio is plotted on the x-axis (see Figure 8). If both ratios are less than 1, the system is deemed advantageous from both environmental and energy perspectives. If the PENRT ratio is less than 1, savings due to energy storage are recorded and the storage system can be deemed efficient. In the worst-case scenario, in which both ratios are greater than 1, the storage system is found to have very energy-intensive production processes and high nonrenewable energy demand. In such cases, storage systems are not advantageous.

**Figure 8.** Interpretation of results for following figures (scheme).

#### *4.1. Centralized Heating Systems*

In Figure 9a centralized heating systems with PCM storage are considered and compared to the corresponding reference systems and are differentiated by PCM type. In the Figure 9b–d, results are filtered by location. The evaluated innovative systems use two different storage materials, namely RT62HC (organic) and SP58 (salt hydrate), and two different distribution systems, namely radiators and underfloor heating. The following parameter variations were implemented:


**Figure 9.** Environmental assessment through "Storage LCA Tool" of centralized heating systems, showing (**a**) comparison with reference systems and (**b–d**) results filtered by location: (**b**) Athens; (**c**) Strasbourg; (**d**) Helsinki.

The combination of PCM storage tanks with solar collectors seems to be largely advantageous and enables energy savings and emission reductions in most cases, as indicated by the accumulation of points in the lower left quadrant in Figure 9a. Different distribution systems seem to be not relevant to the whole lifecycle. There are also results in the upper left quadrant (red circle in Figure 9a) which show high GWP ratios. These cases mainly belong to systems with SP58 and seasonal storage, which enable energy savings but lead to high GWP ratios above 1.3 due to the high amount of PCM and its infrequent use. They mostly occur in Athens (Figure 9b) and decrease in North European locations such as Helsinki (Figure 9d).

The combination of solar collector + RT62HC (with both distribution systems) presents less variation in terms of environmental potential and energy storage. The best savings are recorded by solar collector + SP58 storage + radiators for a single-family house located in Athens with little insulation (GWP ratio = 0.44; energy ratio = 0.32). The worst performance (GWP ratio 2.13; energy ratio = 0.77) is recorded for a solar collector + SP58 storage + underfloor heating system in an energy-efficient office block in Strasbourg with high storage volume (121.82 m3) and small collector surface (5.65 m2). A more detailed visualization of results can be found in Appendix C (Figure A1).

#### *4.2. Centralized Cooling Systems*

The following parameter variations were implemented:


As already mentioned in Section 3.2.3, the achievable energy savings in central cooling systems are lower in comparison with those recorded for heating systems (Figure 10a). As in the example above, the results in Figure 10b–d are filtered by location. Compared with the reference systems, only a few innovative PCM storage concepts achieve positive environmental balances. In these cases, the energy savings due to the efficiency improvements in cold generation, which are achieved by shifting the cooling load into the night by integrating a PCM cold storage, more than compensate for the higher environmental impact due to the additional storage components.

**Figure 10.** Environmental assessment through "Storage LCA Tool" of centralized cooling systems, showing (**a**) comparison with reference systems and (**b–d**) results filtered by location: (**b**) Athens; (**c**) Strasbourg; (**d**) Helsinki.

Unlike heating systems, better performances are reached in Mediterranean area (Athens, see Figure 10b), while PCM systems in Helsinki lack good environmental performances overall (Figure 10d).

Water chiller + RT10HC + fan coil systems show high performance variability, especially in Athens, while the SP15 storage + cooling surface combination offers a better performance in more cases with lower variability (green circle in Figure 10a). An office block located in Athens (no insulation) provided with RT10HC storage + fan coil and a storage volume of 7.11 m3 has the best performance (GWP ratio = 0.89; energy ratio = 0.75). The worst performance (GWP ratio = 1.73; energy ratio = 1.84) is recorded by the same energy concept used in a single-family house in Athens, with low storage volume (0.34 m3) and little insulation [22]. More results details are available in Appendix C (Figure A2).

#### *4.3. Decenteralized Systems*

Finally, decentralized PCM ventilation systems were analyzed. The two different systems are a water chiller with a chilled PCM ceiling (Figure 11a) and a water chiller with a PCM ventilation system (Figure 11b).

**Figure 11.** PCM storage for decentralized systems. System layouts of (**a**) water chiller + PCM cooling surface and (**b**) water chiller + PCM ventilation systems. Reproduced from [24], Fraunhofer ISE 2019.

The following parameter variations were implemented:


Results are shown in Figure 12a. Here, a linear relationship between environmental impacts and energy demand is found.

Among the simulated examples (only office buildings), most cases located in Strasbourg (Figure 12c) offer advantageous energy performances, and all are environmentally advantageous. In comparison, the applications located in Helsinki offer even greater environmental savings while showing higher variability in energetic performance (Figure 12d). The systems simulated for Athens did not provide relevant energetic or environmental advantages (Figure 12c).

**Figure 12.** Environmental assessment through "Storage LCA Tool" of decentralized systems, showing (**a**) comparison with reference systems and (**b–d**) results filtered by location: (**b**) Athens; (**c**) Strasbourg; (**d**) Helsinki.

Water chiller + SP21EK surface cooling systems show a wide range of performance variability depending on location (Figure 12b–d). The best performance (GWP ratio = 0.42; energy ratio = 0.82) is recorded by this energy concept, located in a highly insulated office block in Helsinki with a PCM mass distribution for surface cooling of 22.4 kg/m2. The worst result (GWP ratio = 1.02; energy ratio = 1.26) is recorded in an energy-efficient office block located in Athens (PCM mass distribution for surface cooling of 22.4 kg/m2) [22]. A more detailed visualization is available in Appendix C (Figure A3).

#### **5. Conclusions**

Within this work, further LCA data were generated and provided by "Storage LCA Tool" for the support of decision-making in field of innovative storage materials, which are available or being researched for application in building services engineering. Through "Storage LCA Tool", analyses were carried out at different levels.

At the pure PCM level, the integration of updated information coming from PCM producers proved to be relevant for the assessment of the environmental potential of storage systems. A wide range of capacity-specific environmental effects depending on materials and their applications in heating, cooling and ventilation systems was demonstrated. Outcomes of analyses at the material level demonstrate the advantages of paraffins in terms of thermal storage but also indicate their higher environmental impacts. Conversely, the consideration of these environmental impacts during material production (e.g., when determining the synthesis route) offers the potential to minimize environmental impacts throughout the life cycle. This calls for more research on organic paraffins, which is actually ongoing, in order to minimize the primary energy (nonrenewable) demand for PCM production by replacing the raw materials by renewable sources and furthermore increase their recycling potential [6]. The environmental optimization of salt hydrate raw materials offers optimization potential as well.

At a higher level, two significant cooling and heating systems have been analyzed. Together with the amount of PCM in the thermal storage, the composition of the required auxiliary components (storage insulation, containment, heat exchanger, etc.) shows a great influence on the overall LCA. For this reason, it is necessary to evaluate each quantity carefully, in order to avoid excessively adverse environmental impacts and, at the same time, guarantee enough storage capacity.

By coupling such results with building energy simulation results, innovative PCM storage concepts seemed to be advantageous, especially if associated with a source of renewable energy such as solar collectors. In this case, PCM storage systems can represent an advantageous alternative to a traditional gas boiler in a heating system. Other factors that affect the benefits of PCM thermal energy storage are boundary conditions, i.e., building type, location and insulation level. These factors influence the yearly energy demand and the efficiency of the considered storage systems. Not surprisingly, by ensuring enough thermal insulation on the building envelope, advantageous PCM applications for cooling systems are recorded in warmer locations.

PCM thermal storage systems are, in some cases, advantageous alternatives to traditional water storage. To prove that, analog analyses have been carried out by assuming hot water tank storage (HWT) within heating systems and cold water tank storage (CWT) for cooling systems. In Figure 13, each water storage system is compared with all of the above-reported PCM storage systems. Systems have been distinguished as heating or cooling systems and divided by distribution system. With regard to heating systems, hot water storage (HWT) systems show greater advantages.

It is well known that solar thermal heating systems can save significant amounts of energy compared to conventional gas heating if they are correctly dimensioned. This is confirmed by this study. The impact is quantified and lies in the PENRT ratio range of 0.05 to 0.98, depending on the load and system size. In terms of GWP, the results are more ambiguous. Large PCM storage systems for long-term storage have a high impact due to production. Due to the low cycle number they are not used effectively, so the savings during use phase cannot always compensate for the footprint of production. Water storage systems, on the other hand, have a much lower impact during production and therefore yield better results here. The rather wide temperature range available for heat storage additionally results in a comparable volumetric energy density of water- and paraffin-based thermal storage. All case studies are located in the diagram area belonging to efficient systems with environmental potential.

**Figure 13.** Environmental assessment through "Storage LCA Tool" of water storage systems for (**a**) centralized heating with underfloor heating; (**b**) centralized heating with radiators; (**c**) cooling systems with surface cooling; and (**d**) cooling systems with fan coil.

In contrast, PCM cooling systems are more likely to provide more efficient energy storage and have lower environmental impact compared to a cold water storage reference system. In cooling applications, the temperature interval usable for cold storage is significantly smaller than in heating applications. A PCM storage unit can fully exploit its advantages here due to its high heat storage capacity in a small temperature range. A PCM storage unit allows a significantly smaller storage volume and higher storage temperatures than a comparable water storage unit. On the one hand, this reduces the environmental impact caused by the production of the storage insulation material and the storage cylinder (especially since the PCM storage unit can be made of plastic, whereas the water storage unit is made of steel or stainless steel); on the other hand, the cold can be generated more efficiently due to a higher storage temperature, so that the environmental impact during the utilization phase is reduced.

This strongly affects the final environmental assessment of the innovative cooling systems (Figure 13c,d). Hence, in cooling systems, a PCM storage may be preferable to water tanks.

Despite the large number of energy concepts simulated, the data platform still needs to be improved and enriched. In this study, no degradation of the PCM's thermal properties over thousands of cycles is considered, which would affect the LCA results. At the storage system concept level, in this work, PCM applications for decentralized energy systems showed benefits, although the results are restricted to only one building type. In general, further innovative PCM storage materials; energy concepts; or boundary conditions, including locations, building types and insulation standards, can be included and analyzed through the "Storage LCA Tool". So far, this tool has been assessed (including validation of results) through beta tests, both internal (with help of project partners) and external, with feedback coming from LCA experts and potential tool users. The tool is freely available on the website https://www.iabp.uni-stuttgart.de/new\_downloadgallery/GaBi\_Downloads/SpeicherLCA.zip for further testing.

**Author Contributions:** Conceptualization, R.D.B. and R.H.; methodology, R.H., B.N. and F.K.; software, R.D.B.; validation, B.N., F.K. and R.H.; investigation, R.D.B.; data curation, R.H., B.N., F.K., E.K. and F.P.; writing—original draft preparation, R.D.B.; writing—review and editing, R.H., B.N., F.K. and E.K.; visualization, R.D.B.; supervision, R.H.; project administration, B.N. All authors have read and agreed to the published version of the manuscript.

**Funding:** Research and APC were funded by the German Federal Ministry for Economic Affairs and Energy for the Project "Speicher-LCA" (Grant number: 03ET1333) by a decision of the German Bundestag.

**Acknowledgments:** The authors thank the company Rubitherm GmbH for providing specific data on the production and encapsulation of various PCMs.

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

#### **Appendix A**

In Table A1, material properties of the analyzed PCMs are reported according to [13,14]. In Table A2, results of LCA analysis of PCMs are presented. All PCMs are applied for use in a cooling surface with operating temperature range of 10–16 ◦C.


<sup>1</sup> Macroencapsulated.

**Table A2.** LCA analyses of PCMs and water applied on a cooling surface with operating temperatures 16–20 ◦C. Reproduced from [25], IABP: 2019.


<sup>1</sup> kWh material storage capacity.

#### **Appendix B**

In this Appendix, relevant information for building energy simulations is reported. Table A3 presents mean, maximal and minimal temperatures of each location. Table A4 gives an overview of the established insulation level. Lastly, in Table A5, specific heating and cooling demands are listed for simulation of the chosen office building [24].

**Table A3.** Overview of the climate data used to calculate the soil surface temperature. Reproduced from [24], Fraunhofer ISE 2019.


**Table A4.** U-values were used for energy simulation of office buildings at the different locations. The building efficiency is examined at all locations. Reproduced from [24], Fraunhofer ISE 2019.


**Table A5.** Specific heating and cooling energy demands for simulations of office buildings. Reproduced from [24], Fraunhofer ISE 2019.


#### **Appendix C**

In this Appendix, the overall environmental performances are reported for each single energy concept for better understanding. In Figure A1, results for centralized heating systems are presented.

**Figure A1.** Environmental assessment through "Storage LCA Tool" of PCM storage systems applied for use in centralized heating systems, divided by location and energy concept.

For a better understanding, results for centralized heating systems are presented in Figure A2.

**Figure A2.** Environmental assessment through "Storage LCA Tool" of PCM storage systems applied for use in centralized cooling systems, divided by location and energy concept.

In Figure A3, results for decentralized systems are presented.

**Figure A3.** Environmental assessment through "Storage LCA Tool" of PCM storage systems applied for use in decentralized systems, divided by location and energy concept.

#### **References**


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