Search Results (1,214)

Heating, ventilation, and air-conditioning (HVAC) systems dominate energy consumption in hot-climate buildings, where maintaining occupant comfort under extreme outdoor conditions remains a critical challenge, particularly under emerging time-of-use (TOU) electricity pricing schemes. While deep reinforcement learning (DRL) has shown promise for adaptive HVAC control, existing approaches often suffer from comfort violations, myopic decision making, and limited robustness to uncertainty. This paper proposes a comfort-first hybrid control framework that integrates Soft Actor–Critic (SAC) with a Cross-Entropy Method (CEM) refinement layer, referred to as SACEM. The framework combines data-efficient off-policy learning with short-horizon predictive optimization and safety-aware action projection to explicitly prioritize thermal comfort while minimizing energy use, operating cost, and peak demand. The control problem is formulated as a Markov Decision Process using a simplified thermal model representative of commercial buildings in hot desert climates. The proposed approach is evaluated through extensive simulation using Saudi Arabian summer weather conditions, realistic occupancy patterns, and a three-tier TOU electricity tariff. Performance is assessed against state-of-the-art baselines, including PPO, TD3, and standard SAC, using comfort, energy, cost, and peak demand metrics, complemented by ablation and disturbance-based stress tests. Results show that SACEM achieves a comfort score of 95.8%, while reducing energy consumption and operating cost by approximately 21% relative to the strongest baseline. The findings demonstrate that integrating comfort-dominant reward design with decision-time look-ahead yields robust, economically viable HVAC control suitable for deployment in hot-climate smart buildings. Full article

The digital and ecological transition of the industrial sector requires methodological tools that integrate information modelling, performance simulation, and operational decision support. In this context, the present study introduces and tests a semi-automatic BIM-to-BEM framework to optimise human–machine interaction and support critical data interpretation through Graphical User Interfaces. The objective is to propose and validate a BIM-to-BEM workflow for an existing industrial facility to enable comparative evaluation of energy retrofit scenarios. The information model, developed through an interdisciplinary federated approach and calibrated using parametric procedures, was exported in the gbXML format to generate a dynamic, interoperable energy model. Six simulation scenarios were defined incrementally, including interventions on the building envelope, Heating, Ventilation and Air Conditioning (HVAC) systems, photovoltaic production, and relamping. Results are made accessible through dashboards developed with Business Intelligence tools, allowing direct comparison of different design configurations in terms of thermal loads and indoor environmental stability, highlighting the effectiveness of integrated solutions. For example, the combined interventions reduced heating demand by up to 32% without compromising thermal comfort, while in the relamping scenario alone, the building could achieve an estimated 300 MWh reduction in annual electricity consumption. The proposed workflow serves as a technical foundation for developing an operational and evolving Digital Twin, oriented toward the sustainable governance of building–system interactions. The method proves to be replicable and scalable, offering a practical reference model to support the energy transition of existing industrial environments. Full article

Polyethylene is the most used plastic in the world, and over 90% of this plastic is ultimately disposed of in landfills or released into the environment, leading to severe ecological implications. In this research, the technoeconomic feasibility of upcycling low-density polyethylene (LDPE) to produce ethylene is studied. The catalytic conversion of LDPE to ethylene is considered in microwave heating mode and Joule heating mode. Experimental data is obtained under conditions where most of the upcycled products are in the gas phase. A flowsheet is developed that produces industrial quantities of ethylene for both heating modes. A technoeconomic analysis and a life cycle analysis are conducted and compared with the traditional ethane cracking process for producing ethylene. Simulation results indicate that the upcycling system exhibits a lower capital expenditure and a comparable operating expenditure relative to conventional ethane steam cracking while generating additional valuable co-products, such as propylene and aromatic hydrocarbons, leading to a higher net present value potential. Sensitivity analyses reveal that the electricity price has the most significant impact on both the net present value and levelized cost of production, followed by the low-density polyethylene feedstock cost. Life-cycle assessment reveals a substantial reduction in greenhouse-gas emissions in the upcycled process compared to the fossil-based ethane steam-cracking route, primarily due to the use of renewable electricity, the lower reaction temperature that reduces utility demand, and the use of plastic waste as the feedstock. Overall, the proposed process demonstrates strong potential for the sustainable production of ethylene from waste LDPE. Full article

Microalgae photobioreactors (PBRs) are promising building-integrated biotechnologies for carbon capture and biomass production; however, their high energy requirements for artificial lighting remain a significant energy barrier in cold climates. This study developed an integrated spectral–optical energy modeling framework to evaluate two PBR deployment strategies in State College, PA: rooftop daylight-exposed integration and basement installation with solar-assisted lighting. Results show that fiber-optic daylighting can supply a substantial fraction of photosynthetically useful light without introducing additional internal heat loads, while photovoltaics sized at approximately 0.40–0.55 kWDC per reactor can offset the annual PBR lighting energy use when sufficient roof area is available. Whole-building energy simulations further reveal that rooftop PBR integration reduces total annual space energy consumption by ~21% relative to basement placement due to lower artificial lighting and cooling loads. When combined, PV and fiber systems can fully meet basement PBR lighting demand, whereas rooftop configurations may rely more on grid electricity. Economically, fiber-optic daylighting achieves comparable lighting offsets at roughly half the annualized cost of PV-based systems, subject to surface-area and routing constraints. Overall, solar-assisted lighting strategies markedly improve the operational sustainability of building-integrated PBRs in cold climates, with fiber-optic daylighting offering substantial spectral and thermal advantages, subject to surface-area availability and routing-related design constraints. Full article

Although technologies used in non-fossil methane and fossil resources to produce blue hydrogen are relatively mature, a system-integrated approach to reference system (RS)-based purification of H₂, CO₂ capture and storage, and UHS is relatively unexplored and requires research to fill gaps in the literature regarding balanced permutations and geological viability for net-zero requirements. This research proposes a system-integrated process for H₂ production through a PSA-based purification technique coupled with amine-based CO₂ capture and underground hydrogen storage (UHS). The intellectual novelty of the research is its first quantitative treatment of synergistic effects such as heat recovery and pressure-matching across units. Additionally, a site separation technique is applied, where H₂ and CO₂ reservoirs are selected based on the permeability of rock formations and fluids. On a research methodology front, a base case of a steam methane reforming process with the production of 99.99% pure H₂ at a production rate of 5932 kg/h is modeled and simulated using Aspen Plus™ to create a balanced permutation of mass and energy across units. As per the CO₂ capture requirements of this research, a capture of 90% of CO₂ is accomplished from the production of 755 t/d CO₂ within the model. The compressed CO₂ is permanently stored at specifically identified rock strata separated from storage reservoirs of H₂ to avoid empirically identified hazards of rock–fluid interaction at high temperatures and pressures. The lean amine cooling of CO₂ to 60 °C and elimination of tail-gas recompression simultaneously provides 5.4 MW_th of recovered heat. The integrated design achieves a net primary energy penalty of 18% of hydrogen’s LHV, down from ~25% in a standalone configuration. This corresponds to an energy saving of 8–12 MW, or approximately 15–18% of the primary energy demand. The research computes a production cost of H₂ of 0.98 USD per kg of H₂ within a production atmosphere of a commercialized WGS and non-fossil methane-based production of H₂. Additionally, a sensitivity analysis of ±23% of the energy requirements of the reference system shows no marked sensitivity within a production atmosphere of a commercially available WGS process. Full article

The built environment is responsible for 40% of global energy demand, and, in line with urbanisation and population growth, this demand is expected to increase steadily. Urban areas are mostly composed of materials that can absorb energy from solar radiation and dissipate the accumulated energy in the form of heat. This study integrates a UAV-based Zenmuse XT S IR camera and handheld FLIR C5 thermal camera with ENVI-met microclimate simulation, providing quantitative insights for sustainable urban planning. From the 24 h experiment results, the characteristics of building surface materials are profiled for lowering energy use for internal thermal control during the operation stage of buildings. This study shows that building surface materials with the lowest solar reflectance and highest specific heat capacity reached a peak surface temperature of 73.5 °C in Jakarta (tropical hot climate) and 44.3 °C in Xiamen (subtropical late winter climate). In contrast, materials with the highest solar reflectance and lowest specific heat only reach a peak surface temperature of 58.1 °C in Jakarta and 27.9 °C in Xiamen. The peak surface temperature occurs at 2 PM in the afternoon. Moreover, we demonstrate the capability of an infrared drone to identify the peak surface temperatures of 55.8 °C at 2 PM in the study area in Xiamen. In addition, the ENVI-met validated model shows satisfactory correlation values of R > 0.9 and R2 > 0.8. This result demonstrates UAV-IR and ENVI-met simulation integration as a scalable method for city-level UHI diagnostics and monitoring. Full article

To reconcile the intensifying trade-off between chronic water scarcity and escalating forage demand in the Yellow River Basin, this study optimized integrated irrigation and fertilization regimes for silage maize. Leveraging the AquaCrop model, validated by 2023–2024 field experiments and a 35-year (1990–2024) meteorological dataset, we systematically quantified the impacts of multi-factorial water–fertilizer–heat stress under drip irrigation with mulch (DIM) and shallow-buried drip irrigation (SBDI). Model performance was robust, yielding high simulation accuracy for soil moisture (RMSE < 3.3%), canopy cover (RMSE < 3.95%), and aboveground biomass (RMSE < 4.5 t·ha⁻¹), with EF > 0.7 and R² ≥ 0.85. Results revealed distinct stress dynamics across hydrological scenarios: mild temperature stress predominated in wet years, whereas severe water and fertilizer stresses emerged as the primary constraints during dry years. To mitigate these stresses, a medium fertilizer rate (555 kg·ha⁻¹) was identified as the stable optimum, while dynamic irrigation requirements were determined as 90, 135, and 180 mm for wet, normal, and dry years, respectively. Comparative evaluation indicated that DIM achieved maximum productivity in wet years (aboveground biomass yield 70.4 t·ha⁻¹), whereas SBDI exhibited superior “stable yield–water saving” performance in normal and dry years. The established “hydrological year–irrigation method–threshold” framework provides a robust decision-making tool for precision management, offering critical scientific support for the sustainable, high-quality development of livestock farming in arid regions. Full article

This paper addresses the critical challenge of multi-objective optimization in residential Home Energy Management Systems (HEMS) by proposing a novel framework based on an Improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. The study specifically targets the low convergence efficiency of Multi-Agent Deep Reinforcement Learning (MADRL) for coupled Battery Energy Storage System (BESS) and Air Source Heat Pump (ASHP) operation. The framework synergistically integrates an action constraint projection mechanism with an economic-performance-driven dynamic learning rate modulation strategy, thereby significantly enhancing learning stability. Simulation results demonstrate that the algorithm improves training convergence speed by 35–45% compared to standard MAPPO. Economically, it delivers a cumulative cost reduction of 15.77% against rule-based baselines, outperforming both Independent Proximal Policy Optimization (IPPO) and standard MAPPO benchmarks. Furthermore, the method maximizes renewable energy utilization, achieving nearly 100% photovoltaic self-consumption under favorable conditions while ensuring robustness in extreme scenarios. Temporal analysis reveals the agents’ capacity for anticipatory decision-making, effectively learning correlations among generation, pricing, and demand to achieve seamless seasonal adaptability. These findings validate the superior performance of the proposed centralized training architecture, providing a robust solution for complex residential energy management. Full article

Crude distillation units operate as the most energy-intensive refinery operations and generate substantial carbon dioxide emissions. This research models the crude distillation system through its three main components: the atmospheric distillation unit, the naphtha stabilisation unit, and the vacuum distillation unit. The simulation platform Aspen HYSYS version 14.1 enabled optimisation of the preflash drum under product quality constraints, and the analysis included pinch analysis techniques and techno-economic evaluation. The optimisation results demonstrated an 8.95% reduction in atmospheric furnace duty, a 7.38% decrease in total hot utility consumption with the crude distillation system, and an increase in heat recovery capability from 35.57% to 42.71%. Although the preflash process alone decreases profitability because of increased steam demand, combining preflash operation with heat recovery measures maintains both energy conservation and favourable economic performance. The study shows that refinery optimisation requires treating the crude distillation system as a fully integrated process. This approach offers effective strategies to improve energy performance and reduce carbon dioxide emissions while sustaining economic viability. The work differs from previous studies by evaluating the entire distillation system as an integrated sequence and demonstrating how preflash optimisation affects overall energy demand, heat-recovery potential, and economic outcomes while maintaining product quality. Full article

Improving the architecture and control strategies of thermal management systems (TMSs) is crucial for minimizing energy consumption in heating and cooling components, thereby enhancing the driving range of Battery Electric Vehicles (BEVs). This study presents a holistic approach for developing an Integrated Thermal Management System (ITMS) based on an Octo-valve-type architecture, designed to efficiently manage the thermal demands of both the cabin and powertrain components. Empirical data were collected under various heating and cooling scenarios across a wide operating temperature range (−20 °C to 40 °C), and these data were used to parametrize and validate key ITMS components. Experimental results demonstrated that the parametrized simulation model closely replicated the cabin and battery thermal behavior observed in vehicle tests, particularly under cooling conditions. Minor deviations, such as cabin temperature overshoot during heating scenarios, were attributed to duct thermal effects and control tuning limitations. Overall, the optimized Octo-valve-based ITMS architecture exhibited thermal trends consistent with literature references and effectively validated the proposed control strategy, demonstrating improved thermal efficiency and potential range enhancement for BEVs across diverse environmental conditions. Furthermore, ITMS energy consumption over the indicated temperature range is quantified in this research paper. Full article

The global shift toward renewable energy and circular economy models requires industrial systems that minimize waste and recover value across entire life cycles. This review synthesizes recent advances in by-product recovery technologies supporting renewable energy and circular industrial processes. Thermal, biological, chemical/electrochemical, and biotechnological routes are analyzed across battery and e-waste recycling, bioenergy, wastewater, and agri-food sectors, with emphasis on integration through Life Cycle Assessment (LCA), techno-economic analysis (TEA), and multi-criteria decision analysis (MCDA) coupled to process simulation, digital twins, and artificial intelligence tools. Policy and economic frameworks, including the European Green Deal and the Critical Raw Materials Act, are examined in relation to technology readiness and environmental performance. Hybrid recovery systems, such as pyro-hydro-bio configurations, enable higher resource efficiency and reduced environmental impact compared with stand-alone routes. Across all technologies, major hotspots include electricity demand, reagent use, gas handling, and concentrate management, while process integration, heat recovery, and realistic substitution credits significantly improve life cycle outcomes. Harmonized LCA-TEA-MCDA frameworks and digitalized optimization emerge as essential tools for scaling sustainable, resource-efficient, and low-impact industrial ecosystems consistent with circular economy and renewable energy objectives. Full article

The growing demand for energy-efficient buildings necessitates innovative approaches to reduce energy consumption during early design stages. While traditional physics-based simulations remain time- and expertise-intensive, automated machine learning (AutoML) offers a promising alternative by enabling data-driven building performance prediction with minimal human intervention. This study conducts a benchmark evaluation of AutoML’s potential in building energy applications through three objectives: (1) a literature review revealing AutoML’s nascent adoption (10 identified studies) and primary use cases (heating/cooling prediction, energy demand forecasting); (2) a benchmark comparing three AutoML frameworks (AutoGluon, H2O, Auto-sklearn) against baseline and ensemble ML models using R², RMSE, MSE, and MAE metrics; and (3) SHAP (SHapley Additive exPlanations)-based interpretability analysis. Results demonstrate AutoGluon’s superior accuracy (R² = 0.993, RMSE = 2.280 kWh/m²) in predicting energy performance, outperforming traditional methods. Key influential features, including solar heat gain coefficient (SHGC) and U-values, were identified through SHAP, offering actionable design insights. The primary novelty of this work lies in its two-step methodology: a focused review to identify pertinent AutoML frameworks, followed by a comparative benchmarking of these frameworks against traditional ML for early-stage prediction. This process substantiates AutoML’s potential to democratize energy modeling and deliver practical, interpretable workflows for architectural design. Full article

Against the global backdrop of urbanization and sustainable development, urban parks—key public spaces for carbon sequestration, heat island mitigation, and public health promotion—have made their service performance a critical metric for evaluating urban human settlement quality. However, traditional evaluations relying on static questionnaires and aggregate indicators often fail to capture the spatiotemporal dynamics of park usage and complex supply–demand relationships. To address this gap, this study developed a three-dimensional dynamic evaluation model (“Vitality Level, Demand Matching, Service Supply”) for 59 urban parks in Gongshu District, Hangzhou, integrating multi-source data (mobile phone signaling, POIs, park vectors, demographic statistics). The model includes nine indicators (e.g., Temporal Activity Difference, Vitality Stability Index) with weights determined via the entropy weight method. Empirical results show: (1) Gongshu’s park service performance presents a “core-periphery” spatial disparity, with high-performance parks concentrated in central areas (e.g., West Lake Culture Square) due to convenient transportation and diverse functions; (2) Performance levels vary significantly between weekdays and weekends, with higher stability on weekdays and more pronounced supply–demand mismatches on weekends; (3) Time-series cross-validation and Monte Carlo simulations confirmed the model’s robustness. This framework shifts park research from “static quantitative description” to “dynamic performance diagnosis,” providing a scientific basis for refined planning and efficient management of parks in high-density cities. Full article

Agro-industrial facilities host processes and products that are highly sensitive to thermal fluctuations. Given the projected increase in air temperatures in tropical regions due to climate change, improving indoor thermal conditions in these facilities has become critically important. Conventional cooling systems are widely used to maintain adequate indoor temperatures; however, they are associated with high energy consumption. In this context, Ground Source Heat Pump (GSHP) technology emerges as a promising alternative to reduce cooling loads by exchanging heat with the ground. This study evaluates the reductions in cooling energy consumption and the return on investment of a GSHP system integrated with conventional cooling system, considering a prototype agro-industrial room located in two ecotones of the Brazilian Midwest: the Amazon Forest (AF) and Brazilian Savanna (BS). Building energy simulations were performed using EnergyPlus software v. 9 under current climate conditions and climate change scenarios for 2050 and 2080. Initially, the prototype room was conditioned using a conventional HVAC system; subsequently, a GSHP system was integrated to enhance energy efficiency and reduce energy demand. Under current conditions, cooling energy demand in the BS and AF ecotones is projected to increase by 16.5% and 18.3% by 2050, and by 24.5% and 23.5% by 2080, respectively. The payback analysis indicates that the average return on investment improves under future climate scenarios, decreasing from 14.5 years under current conditions to 10.13 years in 2050 and 9.86 years in 2080. The findings contribute to understanding the thermal resilience and economic feasibility of ground-coupled heat exchangers as a sustainable strategy for mitigating climate change impacts in the agro-industrial sector. Full article

Energy efficiency in hospitals—where continuous operation with high internal gains and strict comfort needs—demands facade strategies tailored to climate. This study quantifies how the window-to-wall ratio (WWR) distribution and city-specific envelope properties affect the annual heating and cooling loads of a four-story, 3000 m² hospital in Turkey. Energy simulations were conducted using DesignBuilder (2021) with EnergyPlus under a controlled modeling framework, following ASHRAE healthcare guidelines for internal loads and TS 825:2024 for envelope compliance. Three locations were selected to span national variability: Bursa (Marmara—temperate/transition), Mersin (Mediterranean—hot–humid), and Kars (humid continental—cold). Scenario 1 (S1) assigned a graduated WWR on the south facade by floor—20%, 30%, 40%, and 50% from ground to top—while the north, east, and west facades were held at 20%, 30%, and 20%. Scenario 2 (S2) preserved the same geometry and WWR values but applied the graduated WWR to the north facade instead, keeping the south at 20%, east at 30%, and west at 20%. Within each city, opaque and glazing properties were kept constant across scenarios to isolate WWR–orientation effects. For every city–scenario combination, annual space-heating and space-cooling loads were computed, and window heat gains and losses were analyzed on the facade with variable WWR to support interpretation of performance mechanisms. The results indicate that S2 outperforms S1 in Mersin, S1 outperforms S2 in Kars, and S2 offers a moderate advantage in Bursa. Full article