Search Results (748)

Phase change materials (PCMs) offer potential for passive thermal regulation in building envelopes through latent heat storage; however, their effectiveness remains strongly climate-dependent, and configurations optimized for one region often underperform in others. Existing evaluation approaches rely largely on location-specific simulations or surrogate models with limited climate transferability. This study develops a physics-informed, climate-aware machine-learning framework to assess PCM-integrated wall assemblies across diverse climates. A structured dataset of 720 EnergyPlus simulations was generated across nine PCM materials, ten ASHRAE climate zones, two placement configurations, and four thickness levels using automated model generation and batch simulation through Eppy-based workflows. Ensemble-based models (XGBoost, LightGBM, CatBoost, Random Forest) were trained under climate-grouped validation to predict total annual energy consumption, peak cooling demand, and peak heating demand. The models achieved high predictive accuracy for total annual energy use (R² ≈ 0.98–0.99) and peak cooling demand (R² ≈ 0.93–0.96), outperforming statistical, climate-only, and PCM-agnostic baselines. In contrast, peak heating demand showed low predictability (R² ≤ 0.26), indicating limited sensitivity to PCM parameters under the studied configuration. These results demonstrate that climate-aware validation enables defensible cross-climate PCM assessment, supporting energy demand reduction and sustainable envelope design decisions aligned with global building decarbonization goals. Full article

The traditional Yikeyin dwellings in central Yunnan exhibit a distinctive spatial layout and skywell design that passively adapt to the mild plateau monsoon climate through natural ventilation. Although their courtyard-based configuration and skylight design are widely recognized for climatic adaptability, the quantitative relationship between courtyard orientation and ventilation performance remains insufficiently explored. This study integrates on-site environmental monitoring with validated Computational Fluid Dynamics (CFD) simulations to investigate how different courtyard orientations influence airflow organization and the indoor thermal environment. Based on detailed field surveys and measured data, three representative orientation schemes were established. The RNG k-ε turbulence model was adopted, and one-way coupled simulations using OpenFOAM and EnergyPlus were conducted to evaluate seasonal ventilation behavior and indoor thermal comfort. The findings reveal synergistic design principles between building orientation and courtyard spatial configuration, as well as spatial differentiation patterns contributing to thermal environment stability. Three orientation types—leeward, windward, and transitional—were identified, each demonstrating distinct advantages and limitations. The study quantitatively confirms the effectiveness of Yikeyin dwellings in utilizing natural ventilation for environmental regulation during both summer and winter seasons. These results provide scientific evidence and design support for modern buildings seeking to achieve enhanced ventilation performance and climatic adaptability. Full article

Health emergencies linked to epidemic outbreaks in vulnerable contexts require rapid and effective architectural responses. Natural ventilation represents a key strategy for infection control and indoor comfort, yet traditional airflow calculation methods require climatic and construction data, which are often unavailable or incomplete. In emergency situations, this results in the inapplicability of such methods and creates a critical information gap. This study proposes a simplified equation to estimate airflow rate (Q) in single-sided and cross-ventilation configurations, based on openable surface area and a reference Effective Window Air Speed (EWAS). Two infectious disease treatment centers were modeled and simulated using EnergyPlus (E+) under five climatic scenarios—two real and three hypothetical—characterized by low, medium, and high wind exposure. Simulation results were compared with existing formulas and with the proposed simplified equation. Although the simplified model introduces a margin of error compared with dynamic simulations, it provides meaningful estimates, with mean deviations typically in the 20–35% range, lower in single-sided conditions and higher for cross-ventilation under medium-to-high wind exposure. The study demonstrates that an ultra-simplified approach can serve as a support tool for the design of temporary healthcare facilities in resource-limited contexts, where rapidity and data accessibility are essential. Full article

Mountainous areas are prone to extreme climatic conditions, and the lack of modern infrastructure makes it difficult to achieve sustainable construction. To overcome the challenges of thermal comfort, robustness, and post-occupancy performance in hazard zones like the Neelum Valley in Pakistan, this research proposes a Digital–Vernacular Integration Model (DVIM), which integrates traditional architectural expertise with modern digital technology. The research design was based on mixed-methods research with the integration of qualitative information obtained through interviews and household surveys (n = 120), and quantitative measures of indoor thermal environments and hazards-based spatial analysis. Vernacular buildings made of wood, stone, and mud were digitally reconstructed using geometric modeling with SketchUp and Autodesk Revit with building information (BIM)-based modeling for assigning materials’ properties. Simulations were carried out using DesignBuilder software with EnergyPlus engines for assessing thermal environment, snow resistance, and seismic resistance to local hazards. The incorporation of the double-layered wall resulted in the improvement of heat retention by 12 to 15%. Moreover, the optimized roof and walls of the hybrid model resulted in the reduction of the sensible heating demand by 42% when compared to the conventional log houses and nearly 80% when compared to the conventional concrete block houses of the modern era. The proposed hybrid model resulted in R-values ranging from 33 to 40 m²·K/W, which are significantly higher when compared to the R-values for conventional timber walls (R = 15 m²·K/W) and concrete block walls (R = 1.0 to 1.3 m²·K/W). These results show the effectiveness of the digitally optimized hybrid model in improving the thermal performance in severe climatic conditions. The results clearly show that the integration of traditional architecture with digital simulation can ensure that modern comfort and safety standards are met without affecting the cultural identity of the region. The proposed framework will be implemented in pilot projects to ensure that the hybrid architectural models are incorporated into regional building regulations. Full article

The digital and green transitions in the AEC sector require rapid, data-driven workflows to redefine sustainability through real-time performance evaluation. However, the high computational cost of traditional energy simulations often lacks evidence-based feedback during early-stage design. This study introduces a surrogate machine learning framework (S-TCML) designed to bypass traditional energy simulation by providing an instantaneous assessment of thermal comfort. Using a parametric Grasshopper–Honeybee environment, a dataset of 3072 configurations was generated for an office room in Cairo, Egypt. Six machine learning algorithms were benchmarked, with Gradient Boosting and Random Forest demonstrating superior performance in capturing non-linear thermal physics. Validation against the EnergyPlus engine confirmed that S-TCML models deliver predictions in milliseconds—a 99.9% reduction in computational time. The Gradient Boosting model achieved exceptional accuracy with an R² of 0.999 and RMSE of 0.013 for PMV and an R² of 0.995 and RMSE of 0.46% for PPD prediction. Feature importance analysis proved that a tree-based ML model can capture the underlying physical relationship between variables. To bridge the feedback gap, a web-based graphical user interface (GUI) was developed to facilitate proactive design exploration. This framework supports sustainable decision-making and design efficiency, offering scalable, user-friendly tools that protect occupant health and ensure thermal resilience in hot–arid environments. Full article

The proton exchange membrane (PEM) electrolyzer is vital for converting surplus renewable energy (RE) into hydrogen, underpinning the efficient and stable operation of the electric–hydrogen system. However, frequent start–stop cycles and load variations accelerate the degradation of proton exchange membranes and catalyst layers, incurring significant lifetime costs that existing studies ignore. To explore how the PEM electrolyzer’s dynamic traits impact system performance, we introduce an optimized operation approach for the electricity–hydrogen integrated energy system (IES) that incorporates these dynamic features and the novel Loss of Life Cost (LLC) model. Initially, to rectify the inadequacy in modeling the PEM electrolyzer within the current electricity–hydrogen IES operational framework, we integrate its dynamic characteristics based on electrochemical properties and establish a quantitative relationship between operational cycles and degradation costs. This enhanced model accurately reflects how operational conditions affect the electrolyzer’s hydrogen production efficiency and lifetime consumption, enabling precise performance simulation and economic assessment. This, in turn, promotes high-quality renewable energy utilization via hydrogen production while ensuring asset longevity, meeting the rising demand for hydrogen energy applications. Building on this, we further factor in constraints related to diverse energy conversion and safe operation within the electricity–hydrogen IES, as well as the operational limits of hydrogen fuel cells, various energy storage (ES) options, cogeneration units, and other pertinent equipment, aiming to minimize the system’s total daily costs (operational plus degradation costs). Consequently, we develop an optimization operation model for the electricity–hydrogen IES that accounts for the PEM electrolyzer’s dynamic characteristics and degradation economics. Finally, through simulation examples validated against published experimental data, we comprehensively analyze how the PEM electrolyzer’s dynamic traits influence system operation, confirming the effectiveness of our proposed model and methodology. Simulation findings reveal that, under varying electrolyzer capacities, ignoring the PEM electrolyzer’s dynamic characteristics can result in a deviation in system operating. Compared with the proposed method, it can reduce the equipment degradation speed by a maximum of 5.78 times. Full article

With the rapid advancement of information technology, the energy consumption of data centers has become a critical issue. Accurate cooling load prediction is essential for optimizing cooling system operations and improving energy efficiency. However, conventional models often struggle to capture the complex nonlinearities and multi-variable coupling effects inherent in data centers. To address the limitations of existing models in terms of training efficiency and generalization performance, this study proposes a cooling load prediction model that integrates the light gradient boosting machine (LightGBM) algorithm with Bayesian optimization. The model was validated using data generated from an EnergyPlus simulation of a representative medium-scale data center. Comparative analysis demonstrates that the proposed model surpasses naive benchmarks (T-1, T-24, and T-168) and other machine learning models (SVR, XGBoost, and LSTM), achieving superior performance with a Root Mean Squared Error (RMSE) of 4.3234 kW, R² of 0.9999, and Mean Absolute Percentage Error (MAPE) of 0.07%. A noise robustness analysis further reveals that the model maintains excellent performance under realistic uncertainties, achieving an R² above 0.99 and an RPD exceeding 12 even at high noise levels (SNR = 20 dB). The total runtime and Relative Prediction Deviation (RPD) were 33.45 s and 86.2685, respectively, indicating an excellent balance between computational efficiency and robust predictive reliability. The key contribution of this research is the effective integration of LightGBM and Bayesian optimization to provide a highly accurate and efficient tool for data center cooling load prediction. This approach offers a scientific foundation for the intelligent control of cooling systems and energy efficiency optimization in data centers, with direct practical implications for building energy management. Full article

Decarbonising the building sector is an energy policy priority due to its major contribution to global energy consumption and related emissions. Accurate energy modelling is crucial, with significant scientific advancements being made in the last decade. As data gathering is a primary bottleneck, the potential of Geographic Information Systems and Remote Sensing for streamlining data acquisition and integrating data sources has gained specific interest. This study aims to identify prevailing trends in scales, inputs, and outputs of energy modelling, focusing on Remote Sensing and Geographic Information Systems applications. A structured literature review was conducted, encompassing screening, textual analysis, and findings synthesis to identify key research trends. The results highlight a predominance of the neighbourhood scale (54%) and the reliance on building geometries as principal input (91% of studies). Remote Sensing, used in 36% of cases, is employed for defining geometric (41%) and non-geometric (45%) attributes, while 17% of studies leverage it to determine climatic variables. EnergyPlus remains the most widespread simulation engine (37%), frequently coupled with construction archetypes (50% of cases) to address data gaps. The increasing integration of these technologies in energy modelling is expected to diversify the number of inputs, ultimately enhancing output accuracy, scalability, and generalisability. Full article

The retrofit of existing residential buildings plays a critical role in reducing energy consumption and carbon emissions in the building sector. However, previous retrofit evaluations often fail to account for the age-related thermal and lighting requirements of residents in aging residential buildings, thereby overlooking the substantial behavioral heterogeneity that shapes retrofit effectiveness. This study evaluates the comprehensive performance of different building envelope retrofit strategies, considering occupants’ thermal and visual comfort, from the perspectives of energy efficiency, economic feasibility, and environmental sustainability. First, age-specific differences in occupancy patterns, thermal preferences, and lighting requirements between elderly and non-elderly comparison group occupants were systematically extracted from the literature. Then, a typical high-rise residential building was modeled in EnergyPlus to serve as the reference building, within which the differentiated occupant behavior models were implemented, and the pre-retrofit condition was defined as the baseline scenario. Next, six commonly applied exterior wall insulation materials and different glass configurations and window frames were parameterized and evaluated under varying insulation thicknesses and remaining building service life scenarios. Finally, the energy-saving performance, economic benefits, and carbon reduction potential of envelope retrofit measures were quantitatively assessed across three primary functional zones (bedroom, living room, and study), using area-normalized indicators. The results indicate that, in the retrofit of existing residential buildings, bedrooms and study rooms exhibit greater retrofit benefits than living rooms, primarily due to longer occupancy durations and higher heating demand. In terms of retrofit strategies, exterior wall insulation consistently outperforms window retrofitting in energy-saving potential, with energy-saving rates of approximately 3.2–4.3% depending on functional zone, material type, and insulation thickness. Among the evaluated materials, vitrified microbead insulation performs best overall in terms of energy, economic, and carbon benefits at 40–60 mm thickness. These findings support occupant-informed, low-carbon retrofit decision-making for existing residential buildings. Full article

This study develops an interpretable machine learning (ML) surrogate to predict hourly indoor air temperature and discomfort indicators for a representative Mexican social-housing prototype in San Luis Potosí (cold semi-arid, Köppen–Geiger BSk). A four-zone EnergyPlus model with constant window opening (50%) and no internal gains was used to generate a parametric dataset spanning 24 building orientations, seven roof solar absorptance levels, and two neighborhood configurations (surrounded vs. corner). Zone-specific bagged-tree regression models were trained in MATLAB using weather predictors, temporal indicators, and weather-memory features (including outdoor temperature lags and rolling averages). Orientation and roof absorptance were included as explicit design predictors, enabling the surrogate model to generalize across the full combinatorial design space rather than requiring a separate model for each configuration. Interpretability was assessed with SHAP values. Evaluated on orientation–absorptance combinations deliberately held out during training, the surrogate achieved high accuracy across zones of the house (R² = 0.98–0.99; RMSE = 0.31–0.67 °C) with stable, near-zero-centered residuals. When propagated into adaptive-comfort metrics computed directly relative to the monthly neutral temperature $T_n$, ML predictions preserved the main cold and hot discomfort degree-hour patterns across the full design space. The proposed surrogate enables rapid, physically consistent comfort-oriented screening of roof finishes and orientation choices in naturally ventilated social housing. Full article

The separation of carbon dioxide (CO₂) and hydrogen sulfide (H₂S) from sour natural gas is an important step in gas processing and emission control. This study applies a rate-based Aspen Plus simulation to examine solvent-based CO₂/H₂S removal under conditions representative of the Shurtan Gas Chemical Complex in Uzbekistan. Monoethanolamine (MEA) and methyldiethanolamine (MDEA) are evaluated as reference solvents with respect to separation performance and energy demand. The rate-based modeling framework accounts for reaction kinetics and mass transfer effects in the absorber–regenerator system. Simulation results indicate that both solvents achieve high acid gas removal efficiencies. From an engineering perspective, the results provide practical guidance for solvent selection and energy optimization in existing acid gas removal units, supporting pilot-scale deployment under industrial operating conditions. Sensitivity analysis suggests that increasing gas throughput beyond 30 t/h leads to a gradual reduction in CO₂ capture efficiency, primarily due to mass transfer limitations. From a techno-economic perspective, the lower energy demand of the MDEA-based system may imply reduced operating costs. The captured CO₂ stream reaches a purity of around 99.5%, which is compatible with downstream soda ash production. Overall, the results provide a screening-level assessment supporting further detailed evaluation. Full article

Power control algorithms using the signal-to-interference-plus-noise ratio (SINR) metric in distributed radar systems (DRS) may suffer from performance degradation in infeasible conditions. In this paper, we present a Sigmoid-based Power Allocation Game (SigPAG) algorithm for target detection in DRS to minimize total power consumption while meeting predetermined target detection performance. Firstly, a physically interpretable Sigmoid function is designed to model radar detection probability as the utility function, overcoming the performance limitations and potential deviations of SINR-based utilities. Secondly, by integrating the proposed Sigmoid utility, SigPAG is established to describe the interaction among radar nodes in the DRS. The existence and uniqueness of the Nash equilibrium (NE) solution are proven by the closed-form expressions of the best response function. Furthermore, an iterative power allocation algorithm is proposed to adjust the transmit powers towards the NE point. Finally, simulation results obtained in a 4-node DRS with Radar Cross Section (RCS) values of [1, 0.3, 2, 5] m² demonstrate that the proposed algorithm achieves an energy efficiency improvement of 36.1% in target detection compared with the traditional SINR-based methods. Full article

The metabolic control analysis (MCA) was applied to several subsystems selected from the model of human erythrocyte energy metabolism. These subsystems represent varying degrees of simplification of energy metabolism, from the simplest subsystem of the first three glycolytic reactions that determine the steady-state rate of glycolysis, to an expanded subsystem that includes all glycolytic reactions plus passive and active ion transport across the cell membrane. The control coefficients of enzyme activities for the rate of glycolysis are found to be very different in different subsystems. However, no specific trend is observed in changes in control coefficients as the subsystem becomes more complex. Thus, in subsystems containing only glycolysis, the control coefficients of hexokinase (HK) and phosphofructokinase (PFK) together amount to 0.99. When ATPases are added, this value decreases to 0.18 and below, and the maximum control coefficient goes to ATPase (0.82–1.00). It would seem that there is a natural decrease in the contribution of HK and PFK to the regulation of the rate of glycolysis as the dimension of the system increases. However, disabling the allosteric regulation of PFK by AMP completely changes the picture. In a subsystem containing only glycolysis, disabling this regulation does not affect the control coefficients. After adding ATPase to such a subsystem, the HK and PFK control coefficients increase, and the control coefficient of ATPase takes on a negative value. Thus, we found that in extended subsystems involving glycolysis and ATPase or transmembrane ion transport, information on the initial regulation of glycolysis may not be revealed in the MCA results. It appears that the MCA alone cannot reveal regulatory mechanisms of metabolic systems in the presence of strong allosteric and feedback regulation. Full article

The conversion of plastic waste into hydrogen offers a promising waste-to-value pathway, but its industrial viability is constrained by high external energy demand associated with thermochemical processing. This study evaluates the energy performance of hydrogen production from mixed plastic waste via pyrolysis and in-line steam reforming, with a focus on reducing utility consumption through systematic heat integration. A steady-state process model was developed in Aspen Plus for a representative mixture of polyethylene, polypropylene, and polystyrene, followed by detailed energy analysis and pinch-based heat integration using Aspen Energy Analyser. Baseline utility requirements were quantified and compared against optimised configurations incorporating targeted heat exchanger network modifications. The base-case analysis identified significant recoverable heat, enabling a reduction in total external utilities from 7.14 to 2.88 GJ h⁻¹, corresponding to a 59.6% decrease in utility demand. Sequential heat integration scenarios further reduced heating and cooling duties while maintaining process operability, demonstrating the effectiveness of iterative, pinch-guided design. The results show that high-temperature waste-plastic-to-hydrogen systems need not be utility-dominated when energy integration is embedded at the design stage. These findings highlight heat integration as a critical enabler for improving the energy efficiency and sustainability of pyrolysis–reforming routes and provide a robust framework for developing scalable, low-carbon hydrogen production from plastic waste. Full article

Globalization has reorganized industrial spatial patterns, embedding regional economies into complex global production systems. However, the existing literature primarily focuses on the national level, leaving the “global-national-local” multi-scale linkages of sub-national regions underexplored. Focusing on Guangdong, which is China’s most open economic gateway, this study constructs a nested Multi-Regional input–output (MRIO) model to systematically reveal its industrial linkage paths across multiple scales. The results demonstrate that Guangdong features “strong local services and extensive global connections.” Specifically, the network is led by the high-R&D-intensity category and supported by energy and low-R&D categories, highlighted by two core supply paths, which are non-metallic mineral supply for construction and metal product support for optical–electrical manufacturing. Four heterogeneous modes are identified: resource security, innovation-driven dual circulation, cost-competitive regional division, and export-oriented service support. Crucially, the provincial “domestic intermediate chains plus international core chains” logic underscores Guangdong’s role as a bridge connecting Global and Domestic Value Chains. Theoretically, this work enriches the local dimension of Global Production Network theory. Methodologically, it provides an operational tool for nested analysis. Practically, it offers policy evidence for open economies to optimize industrial layouts and enhance supply chain resilience. Full article