Search Results (615)

Adaptive façades, also known as climate-adaptive building shells (CABSs), could make a significant contribution towards reducing the energy consumption of buildings and their environmental impacts. There is extensive research on glazed adaptive façades, mainly due to the available technology for glass materials. The technological development of opaque adaptive façades has focused on variable-thermal-resistance envelopes, and the simulation of this type of façade is a challenging task that has not been thoroughly studied. The aim of this study was to configure and validate a simplified office model that could be used for simulating an adaptive façade with variable thermal resistance via adaptive insulation thickness in its opaque part. Software-to-software model comparison based on the results of an EnergyPlus Building Energy Simulation Test 900 (BesTest 900)-validated model was used. Cooling and heating annual energy demand (kWh), peak cooling and heating (kW), and maximum, minimum, and average annual hourly zone temperature variables were compared for both the Adaptive and non-adaptive validated model. An Adaptive EnergyPlus model based on the BesTest 900 model, which uses the EnergyPlus SurfaceControl:MovableInsulation class list, was successfully validated and could be used for studying office buildings with a variable-thermal-resistance adaptive façade wall configuration, equivalent to a heavyweight mass wall construction with an External Insulation Finishing System (EIFS). An example of the Adaptive model in the Denver location is included in this paper. Annual savings of up to 26% in total energy demand (heating + cooling) was achieved and could reach up to 54% when electro-chromic (EC) glass commanded by a rule-based algorithm was added to the glazed part of the variable-thermal-resistance adaptive façade. Full article

Biogas is a crucial renewable energy source for green hydrogen (H₂) production, reducing greenhouse gas emissions and serving as a carbon-free energy carrier with higher specific energy than traditional fuels. Currently, methane reforming dominates H₂ production to meet growing global demand, with biogas/landfill gas (LFG) reform offering a promising alternative. This study provides a comprehensive simulation-based evaluation of Steam Methane Reforming (SMR) and Dry Methane Reforming (DMR) of biogas/LFG, using Aspen Plus. Simulations were conducted under varying operating conditions, including steam-to-carbon (S/C) for SMR and steam-to-carbon monoxide (S/CO) ratios for DMR, reforming temperatures, pressures, and LFG compositions, to optimize H₂ yield and process efficiency. The comparative study showed that SMR attains higher specific H₂ yields (0.14–0.19 kgH₂/Nm³), with specific energy consumption between 0.048 and 0.075 MWh/kg of H₂, especially at increased S/C ratios. DMR produces less H₂ than SMR (0.104–0.136 kg H₂/Nm³) and requires higher energy inputs (0.072–0.079 MWh/kg H₂), making it less efficient. Both processes require an additional 1.4–2.1 Nm³ of biogas/LFG per Nm³ of feed for energy. These findings provide key insights for improving biogas-based H₂ production for sustainable energy, with future work focusing on techno–economic and environmental assessments to evaluate its feasibility, scalability, and industrial application. Full article

This research delves into the co-pyrolysis of refuse-derived fuel (RDF) and oil shale (OS), utilizing a 50% weight ratio for each component. The study employs a fixed-bed reactor, augmented by electrical kiln heating, to conduct the co-pyrolysis process. A significant aspect of this research is the use of Aspen Plus software for process simulation, with the simulated results undergoing validation through experimental data. A commendable correlation was observed between the experimental outcomes and the model predictions, underscoring the reliability of the simulation approach. The investigation reveals distinct product yields from the pyrolysis of 100% RDF and 100% OS. Specifically, the pyrolysis of pure RDF yielded 45.26% gas, 20.67% oil, and 34.07% char by weight. In contrast, the pyrolysis of pure OS resulted in 14.51% gas, 8.32% liquid, and a significant 77.61% char by weight. The co-pyrolysis of RDF and OS in a 50% blend altered the product distribution to 31.98% gas, 12.58% liquid, and 55.09% char by weight. Furthermore, the Aspen Plus simulation model aligned closely with these findings, predicting yields of 31.40% gas, 11.9% oil, and 56.6% char by weight for the RDF-OS blend. This study not only elucidates the co-pyrolysis behavior of RDF and OS but also contributes valuable insights into the potential of these materials to address the pressing issue of plastic waste management and energy resource utilization. The findings underscore the efficacy of RDF and OS co-pyrolysis as a viable strategy for enhancing the value extraction from waste and underutilized energy resources, presenting a promising avenue for environmental and energy sustainability. Full article

The renovation of buildings impacts various factors; one of them is the economic aspect, which has a significant influence on the decision-making process in building refurbishment, especially in social housing. An often-neglected aspect of renovation is the influence of climate change. Typically, historical climate data are used to estimate the building’s future energy needs. However, due to climate change, this approach may fail to accurately represent future environmental conditions, resulting in miscalculations in energy consumption and costs. This study analyzed a building archetype obtained from the TABULA webtool with the characteristics of a social house building located in Trieste. Dynamic simulations were performed using DesignBuilder and EnergyPlus software and future climate models (the GERICS_CNRM-CM5 and GERICS_IPSL-CM5A-MR models obtained from the EURO-CORDEX database). The projected energy needs of the renovated building and its economic effects were compared with current scenarios, and due to the uncertainties in economic parameters, the outcome is expressed in terms of percentiles of the Net Present Value (NPV). The results of this study show that since temperature increases in the future, the need for energy in the heating period reduces, while the need for cooling increases, directly affecting the statistical distribution of the NPV. Full article

The paper presents the design and development of a universal simulation model named SustainSIM, intended for optimizing the carbon footprint in mechanical engineering production. The objective of this model is to enable enterprises to accurately quantify, monitor, and simulate CO₂ emissions generated during various manufacturing processes, thereby identifying and evaluating effective reduction strategies. The paper thoroughly examines methodologies for data collection and processing, determination of emission factors, and categorization of emissions (Scope 1 and Scope 2), utilizing standards such as the GHG Protocol and associated databases. Through a digital simulation environment created in Unity Engine, the model interactively visualizes the impacts of implementing green technologies—such as solar panels, electric vehicles, and heat pumps—on reducing the overall carbon footprint. The practical applicability of the model was validated using a mechanical engineering company as a case study, where simulations confirmed the model’s potential in supporting sustainable decision-making and production process optimization. The findings suggest that the implementation of such a tool can significantly contribute to environmentally responsible management and the reduction of industrial emissions. In comparison to existing methods such as SimaPro/OpenLCA (detailed LCA) and the Corporate Calculator (GHG Protocol), SustainSIM achieves the same accuracy in calculating Scopes 1/2, while reducing the analysis time to less than 15% and decreasing the requirements for expertise. Unlike simulation packages like Energy Plus, users can modify parameters without scripting, and they can see the immediate impact in CO₂e. Full article

This study proposes a sustainable multi-criteria optimization framework for the energy retrofit of collective residential buildings in Algeria, particularly those constructed between the 1970s and 1980s. Through on-site surveys, energy consumption analysis, and seasonal temperature measurements, the high energy demand of these buildings was confirmed. Using EnergyPlus simulations based on Meteoblue weather data, 16 retrofit strategies were assessed—incorporating various insulating materials applied internally or externally (via rendering or cladding). The ELECTRE III decision-making tool was employed, supported by the Simos Revised Framework (SRF) for weighting environmental, economic, and social criteria. Results demonstrate that all strategies significantly reduce energy demand—by up to 72.5%, with reductions reaching 94.4% in winter and 43.5% in summer, depending on insulation type and placement. Improvements in indoor thermal comfort were also observed, with exterior insulation beneath cladding offering the best performance during winter, while exterior rendering also proved effective in the summer. The ELECTRE III analysis identified rock wool and polyurethane with fiber cement cladding as optimal insulation solutions. The proposed approach supports national energy policies and aligns with the Sustainable Development Goals (SDGs), offering a replicable model for large-scale building retrofits in similar climatic and architectural contexts. Full article

Mobile edge computing (MEC) systems empowered by energy harvesting (EH) significantly enhance sustainable computing capabilities for mobile devices (MDs). This paper investigates a multi-user multi-server MEC network, in which energy-constrained users dynamically harvest ambient energy to flexibly allocate resources among local computation, task offloading, or intentional task discarding. We formulate a stochastic optimization problem aiming to minimize the time-averaged weighted sum of execution delay, energy consumption, and task discard penalty. To address the energy causality constraints and temporal coupling effects, we develop a Lyapunov optimization-based drift-plus-penalty framework that decomposes the long-term optimization into sequential per-time-slot subproblems. Furthermore, to overcome the curse of dimensionality in high-dimensional action, we propose hierarchical deep reinforcement learning (DRL) solutions incorporating both Q-learning with experience replay and asynchronous advantage actor–critic (A3C) architectures. Extensive simulations demonstrate that our DRL-driven approach achieves lower costs compared with conventional model predictive control methods, while maintaining robust performance under stochastic energy arrivals and channel variations. Full article

This paper reports the latest update to and a 24 h continuous operation test of the CEU’s thermochemical iodine–sulfur cycle water-splitting system with a maximum H₂ hydrogen production capacity of 1500 L/h. To address challenges such as high energy consumption and severe corrosion in traditional processes, the system was updated and optimized by introducing a small-cycle design, simulated using Aspen Plus software, achieving a thermal efficiency of 53%. Specifically, the key equipment improvements included a three-stage H₂SO₄ decomposition reactor and an HI decomposition reactor with heat recovery, resolving issues of severe corrosion when H₂SO₄ boils and reducing heat loss. During 24 h continuous operation in January 2025, the system achieved a peak hydrogen production rate of 1536 L/h and a long-term stable rate of approximately 300 L/h, with hydrogen purity reaching up to 98.75%. This study validates the potential for the scaling up of iodine–sulfur cycle hydrogen production technology, providing engineering insights for efficient and clean hydrogen energy production. Full article

This study evaluates the impact of climate change on the energy performance of residential buildings across Portugal’s diverse climatic regions, providing a representative reference for Southern European contexts. Dynamic energy simulations using EnergyPlus were conducted for standardised residential building models in five cities: Bragança, Porto, Lisbon, Évora, and Faro. Three climate scenarios were analysed: present-day conditions (TMY2021), the current regulatory scenario (LNEG-EPW), and a projected mid-century scenario (CCW-EPW). Results indicate substantial regional variations, with significant increases in cooling demands and corresponding reductions in heating needs, exposing limitations in the regulatory climate files currently used in energy certification processes. These findings emphasise the critical need to incorporate predictive climatic scenarios into building design standards and energy policies. Adopting such an approach will enhance residential building resilience, ensure thermal comfort, reduce energy consumption, and contribute to sustainable development goals. These insights offer practical guidance for policymakers, urban planners, architects, and engineers aiming to effectively adapt residential buildings to anticipated climatic shifts, facilitating proactive and informed decision-making to address future energy challenges. Full article

Condensation assessment of a residential building in Changsha, China-located in the hot summer and cold winter climate zone-was conducted during the Plum Rain Season (PRS) using Energy Plus simulations and field measurements. Window-opening behaviour significantly influences indoor air quality and thermal comfort. This study specifically examines how window-opening patterns, including opening duration and opening degree, affect interior surface condensation risk in a rural residential building during PRS. Results indicate that window operational status (open/closed) exerts a dominant influence on condensation risk, while varying window opening degrees during identical opening duration showed negligible differential impacts. Critical temporal patterns emerged: morning window openings during PRS should be avoided, whereas afternoon (15:00–18:00) and nighttime (18:00–06:00) ventilation proves advantageous. Optimisation analysis revealed that implementing combined afternoon and nighttime ventilation windows (15:00–18:00 + 18:00–06:00) achieved the lowest condensation risk of 0.112 among evaluated scenarios. Furthermore, monthly-adjusted window operation strategies yielded eight recommended ventilation modes, maintaining condensation risks below 0.11 and providing occupant-tailored solutions for Changsha’s PRS conditions. These findings establish evidence-based guidelines for moisture control through optimised window operation in climate-responsive building management. Full article

Within the framework of the SOCIETAL CHALLENGES—Secure, Clean, and Efficient Energy objective under the European Horizon 2020 research and innovation funding program, the SO-FREE project has developed a future-ready solid oxide fuel cell (SOFC) system with high-efficiency heat recovery. The system concept prioritizes low emissions, fuel flexibility, modular power production, and efficient thermal management. A key design feature is the interchangeability of two different SOFC stack types, allowing for operation under different temperature conditions. The system was developed with a strong emphasis on simplicity, minimizing the number of components to reduce overall plant costs while maintaining high performance. This paper presents the simulation results of the proposed flexible SOFC system, conducted using Aspen Plus^® software version 11 to establish a baseline architecture for real plant development. The simulated layout consists of an autothermal reformer (ATR), a high-temperature blower, an SOFC stack, a burner, and a heat recovery system incorporating four heat exchangers. Simulations were performed for two different anodic inlet temperatures (600 °C and 700 °C) and three fuel compositions (100% CH₄, 100% H₂, and 50% H₂ + 50% CH₄), resulting in six distinct operating scenarios. The results demonstrate a system utilization factor (UF_F) exceeding 90%, electrical efficiency ranging from 60% to 77%, and an effective heat recovery rate above 60%. These findings were instrumental in the development of the Piping and Instrumentation Diagram (P&ID) required for the design and implementation of the real system. The proposed SOFC system represents a cost-effective and adaptable energy conversion solution, contributing to the advancement of high-efficiency and low-emission power generation technologies. Full article

Cell-free massive multiple-input multiple-output (MIMO) networks eliminate cell boundaries and enhance uniform quality of service by enabling cooperative transmission among access points (APs). In conventional cellular networks, user equipment located at the cell edge experiences severe interference and unbalanced resource allocation. However, in cell-free massive MIMO networks, multiple access points cooperatively serve user equipment (UEs), effectively mitigating these issues. Beamforming and cooperative transmission among APs are essential in massive MIMO environments, making efficient power allocation a critical factor in determining overall network performance. In particular, considering power allocation from the central processing unit (CPU) to the APs enables optimal power utilization across the entire network. Traditional power allocation methods such as equal power allocation and max–min power allocation fail to fully exploit the cooperative characteristics of APs, leading to suboptimal network performance. To address this limitation, in this study we propose a convolutional neural network (CNN)-based power allocation model that optimizes both CPU-to-AP power allocation and AP-to-UE power distribution. The proposed model learns the optimal power allocation strategy by utilizing the channel state information, AP-UE distance, interference levels, and signal-to-interference-plus-noise ratio as input features. Simulation results demonstrate that the proposed CNN-based power allocation method significantly improves spectral efficiency compared to conventional power allocation techniques while also enhancing energy efficiency. This confirms that deep learning-based power allocation can effectively enhance network performance in cell-free massive MIMO environments. Full article

To face the global energy crisis, the requirement of energy transition and sustainable development has emphasized the importance of controlling building energy management systems. Reinforcement learning (RL) has shown notable energy-saving potential in the optimal control of heating, ventilation, and air-conditioning (HVAC) systems. However, the coupling of the algorithms and environments limits the cross-scenario application. This paper develops chiller plant models in OpenAI Gym environments to evaluate different RL algorithms for optimizing condenser water loop control. A shopping mall in Changsha, China, was selected as the case study building. First, an energy simulation model in EnergyPlus was generated using AutoBPS. Then, the OpenAI Gym chiller plant system model was developed and validated by comparing it with the EnergyPlus simulation results. Moreover, two RL algorithms, Deep-Q-Network (DQN) and Double Deep-Q-Network (DDQN), were deployed to control the condenser water flow rate and approach temperature of cooling towers in the RL environment. Finally, the optimization performance of DQN across three climate zones was evaluated using the AutoBPS-Gym toolkit. The findings indicated that during the cooling season in a shopping mall in Changsha, the DQN control method resulted in energy savings of 14.16% for the cooling water system, whereas the DDQN method achieved savings of 14.01%. Using the average control values from DQN, the EnergyPlus simulation recorded an energy-saving rate of 10.42% compared to the baseline. Furthermore, implementing the DQN algorithm across three different climatic zones led to an average energy savings of 4.0%, highlighting the toolkit’s ability to effectively utilize RL for optimal control in various environmental contexts. Full article

Hydrogen (H₂) is a key energy vector in the global transition toward clean and sustainable energy systems. Among the various production methods, water electrolysis presents a promising pathway for zero-emission hydrogen generation when powered by renewables. This review provides a comprehensive evaluation of water electrolysis technologies, including alkaline (AWE), proton exchange membrane (PEMWE), solid oxide (SOEC), anion exchange membrane (AEMWE), and microbial electrolysis cells (MEC). It critically examines their material systems, catalytic strategies, operational characteristics, and recent performance advances. In addition to reviewing experimental progress, the study presents a finite element modeling (FEM) case study that evaluates thermal and mechanical responses in PEM and AWE configurations—illustrating how FEM supports design optimization and performance prediction. To broaden methodological insight, other simulation frameworks such as computational fluid dynamics (CFD), response surface methodology (RSM), and system-level modeling (e.g., Aspen Plus^®) are also discussed based on their use in recent literature. These are reviewed to guide future integration of multi-scale and multi-physics approaches in electrolyzer research. By bridging practical design, numerical simulation, and material science perspectives, this work provides a resource for researchers and engineers advancing next-generation hydrogen production systems. Full article

In a building, the thermal satisfaction an individual may experience generally influences their health, well-being, productivity, and energy consumption. The concept of thermal comfort and its importance in buildings has been known for some time. However, in the Dominican Republic, discussing thermal comfort in social housing is a challenge since there have not been many studies applied to this context, especially to social housing. For this reason, this research analyzed the thermal behavior of a typical social housing building through energy simulation, aiming to highlight the importance of passive strategies to improve comfort in a warm and humid climate without using air conditioning. The simulation was conducted using OpenStudio v3.9, which utilizes the EnergyPlus v9.4 calculation engine. Three case studies were analyzed, implementing passive measures and seeking to achieve temperatures within the comfort ranges of the housing prototype. The results show that combining different passive strategies for warm-humid climates significantly reduces temperature, achieving reductions of up to 2.8 °C in the colder period and up to 3.2 °C in the warmer period. Full article