Next Energy最新文献

{"title":"An optimized double-integral sliding mode controller based hybrid gray wolf with bald eagle search algorithm for a fuel cell power system","authors":"Issam Bekki ,&nbsp;Habiba Rizki ,&nbsp;Fatima Ez-Zahra Lamzouri ,&nbsp;El-Mahjoub Boufounass ,&nbsp;Aumeur El Amrani","doi":"10.1016/j.nxener.2025.100447","DOIUrl":"10.1016/j.nxener.2025.100447","url":null,"abstract":"<div><div>This study presents an optimal robust maximum power point tracking (MPPT) control for a proton exchange membrane fuel cell (PEMFC) system operating under specified operational conditions. The investigated PEMFC system includes a fuel cell with a DC-DC converter, providing a resistive charge. The control scheme combines the robust nonlinear double-integral sliding mode control (DISMC) and the hybrid gray wolf optimizer with bald eagle search (GWO-BES) algorithm. As a novel strategy, the GWO-BES-DISMC controller combines the benefits of double-integral sliding mode methods, where the double-integral term eliminates steady-state error and inherently reduces chattering through the generation of smooth control signals, while optimized controller gains prevent overshoot. The hybrid GWO-BES algorithm optimizes DISMC parameters by leveraging GWO's global search capability to avoid local minima and BES's exploitation strength for precise parameter fine-tuning. Moreover, the GWO-BES technique is employed to optimize the parameters of the DISMC controller. The novelty lies in the first-time integration of hybrid GWO-BES optimization with double-integral sliding mode control for PEMFC systems, addressing chattering elimination and parameter optimization simultaneously. The stability of the controlled PEMFC power system is affirmed through the application of the Lyapunov function. Additionally, several simulations of the proposed GWO-BES-DISMC are investigated and compared to the DISMC and the SMC controllers for operational conditions. The simulation results conclusively demonstrate that the proposed approach exhibits superior robustness with 85.9% faster settling time (0.184 s vs 1.309 s for SMC), 97.4% reduction in steady-state error, and 99.53% efficiency, even with external load variations, while remaining stable without overshoot.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100447"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing long short-term memory network with genetic and Bayesian optimization algorithms for accurate forecasting","authors":"M. Zulfiqar","doi":"10.1016/j.nxener.2025.100425","DOIUrl":"10.1016/j.nxener.2025.100425","url":null,"abstract":"<div><div>Accurate load forecasting is crucial for effective grid management and strategic decision-making in the energy sector, particularly due to the inherent volatility and nonlinearity in load demand. This paper introduces a hybrid forecasting framework that combines advanced feature selection and Bayesian optimization (BO) to tune the long short-term memory (LSTM) model. The feature selection employs a genetic algorithm-based wrapper to systematically eliminate irrelevant and redundant features, enhancing computational efficiency and addressing dimensionality challenges. Unlike conventional approaches, the proposed framework uses BO for LSTM hyperparameter tuning, overcoming manual tuning limitations and reducing the risk of suboptimal performance. Integrating the search capabilities of the genetic algorithm with LSTM’s nonlinear modeling strengths and the optimization precision of BO, the framework achieves superior accuracy, enhanced stability, and accelerated convergence. The proposed model achieves a mean absolute percentage error of 0.5% by iteration 12, converging 20–40% faster than counterpart algorithms. Whereas, the other models exhibit slower convergences with an error of 1.4–1.6%. Statistical analysis validates the performance of the proposed algorithm marking it as a robust solution for dynamic forecasting, with precision and stability for real-world applications.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100425"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven regression controller-based MPPT with image encryption inspired solar PV array reconfiguration under partial shading conditions","authors":"Madavena Kumaraswamy,&nbsp;Kanasottu Anil Naik","doi":"10.1016/j.nxener.2025.100438","DOIUrl":"10.1016/j.nxener.2025.100438","url":null,"abstract":"<div><div>Partial shading and environmental variations significantly reduce the power output and efficiency of photovoltaic (PV) systems, posing challenges for conventional maximum power point tracking (MPPT) methods that suffer from slow convergence, local maxima trapping, and high computational cost. To address these limitations, this paper proposes an image encryption-inspired PV array static reconfiguration technique based on the Kolakoski sequence transform (KST), combined with data-driven regression-based MPPT controllers. The proposed KST method minimizes current mismatches by intelligently redistributing shaded modules, while decision tree (DT), support vector machine (SVM), neural network (NN), and machine learning (ML) regression methods are employed to determine the optimal duty cycle for a SEPIC converter under varying irradiance conditions. The system is evaluated on both symmetrical 5 × 5 arrays and unsymmetrical 4 × 6 arrays, including experimental validation using a 250 Wp standalone PV setup. In MPPT performance, the regression-based controllers attain GMP enhancements of 47.09%, 45.14%, 27.27%, 13.62%, and 10.73% for 5 × 5 arrays and 74.96%, 44.11%, 40.14%, 18.29%, and 7.15% for 4 × 6 arrays under diverse environmental conditions. The reconfiguration technique achieves global maximum power (GMP) improvements of 32.79%, 14.98%, and 10.15% across various shading scenarios using 9 × 9 arrays. Notably, the proposed KST integrated with SVM regression-based MPPT delivers up to 68% GMPP enhancement, with &gt;98.5% efficiency, convergence &lt;0.35 s, and ripple ≤1.5%, validated across dynamic shading, temperature variation, rapid irradiance changes, and hotspot conditions. These results confirm the robustness, adaptability, and real-time suitability of the proposed KST integrated with ML-based Regression MPPT approach for practical PV optimization.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100438"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced thermal management and energy efficiency in electronic processor cooling using MWCNT-LA NEPCM heat sink with U-tube heat pipes","authors":"S. Kalaiselvam ,&nbsp;A. Lakshmi Kanthan Bharathi ,&nbsp;A. Ameelia Roseline","doi":"10.1016/j.nxener.2025.100440","DOIUrl":"10.1016/j.nxener.2025.100440","url":null,"abstract":"<div><div>This study investigates the efficiency of a multi-walled carbon nanotube-infused lauric acid (MWCNT-LA) heatsink with U-tube heat pipes filled with n-pentane for electronic processor cooling. Experimental evaluations were conducted under varying heat loads and filling ratios to assess processor stability and energy efficiency. The investigation focused on energy savings, the thermal resistance of different heat pipe-assisted heatsink modules with multi-walled carbon nanotube-infused lauric acid phase change material, with its regeneration time, and optimal heat pipe filling ratio. Results showed that the MWCNT-LA heat sink module with 50% n-pentane filling performed best under higher heat loads, achieving the lowest thermal resistance of 0.63<!--> <!-->°C/W at 50% filling ratio and 75% heat load. This design was 3.58 times more effective than the unfilled heat pipe version and achieved 78% energy savings with minimal cooling fan energy consumption. The developed heat sink design improves thermal management by utilizing latent heat storage and enhancing heat transport efficiency through the heat pipe, thus optimizing thermal performance, heat dissipation, and temperature regulation. These improvements increased the operational reliability and energy efficiency of processors in data center cooling applications.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100440"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operational and environmental impacts on battery lifetime and vehicle performance: A case study for electric taxis","authors":"Zisis Lampropoulos ,&nbsp;Spyridon Spyridopoulos ,&nbsp;Traianos Karageorgiou ,&nbsp;Grigorios Koltsakis","doi":"10.1016/j.nxener.2025.100441","DOIUrl":"10.1016/j.nxener.2025.100441","url":null,"abstract":"<div><div>The gradual electrification of the road transport sector has raised a lot of concerns about the reliability of battery electric vehicles (BEVs). Many potential customers not only lack awareness about the benefits of electrification, total costs and charging infrastructure, but are especially worried about battery lifetime and vehicle performance, information which manufacturers often struggle to provide accurately. This work proposes a methodology to predict BEV lifetime based on complete vehicle simulation employing a physics-based, electrochemical-thermal-aging battery model. In addition, the model calculates the performance degradation over time in terms of energy consumption, range, battery charging efficiency and vehicle acceleration. Physics-based models are harder to develop and computationally costlier than data-driven models. However, once developed, they can be used in a much broader range of conditions and, more importantly, be applied also when no adequate on-road data are yet available. The proposed methodology is applied in a case study of BEV taxis in the city of Thessaloniki, Greece. In particular, the impact of battery preheating prior to charging is evaluated by simulation, showing that preheating could increase lifetime and mileage of BEV taxis by 14% in South European climates. In another application, it is calculated that mid-shift fast-charging could even double the life of the battery compared to fast-charging only before shift change, leading simultaneously to improved performance when compared within the same operational period. Such results could support battery and vehicle manufacturers as well as fleet managers to guide BEV taxi owners towards optimal charging behavior. The modeling approach presented in this paper can be further extended to other vehicle groups, environmental, driving and charging conditions, making it a powerful tool not only for manufacturers, but also for policymakers and charging infrastructure companies.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100441"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gliding arc plasma dry reforming of n-dodecane for H2 production: A mechanism study combined with experimental methods and kinetic modeling","authors":"Baowei Wang,&nbsp;Weiyue Huo,&nbsp;Yi Cheng,&nbsp;Shize Liu,&nbsp;Jijun Zou","doi":"10.1016/j.nxener.2025.100449","DOIUrl":"10.1016/j.nxener.2025.100449","url":null,"abstract":"<div><div>Dry reforming technology is promising because it can simultaneously produce syngas (H₂ and CO) and efficiently convert the greenhouse gas CO₂. This study investigated the dry reforming (DR) of n-dodecane in a gliding arc plasma (GAP) reactor through integrated experimental and kinetic simulation approaches. Key operating parameters—O/C molar ratio, input power, and residence time—were evaluated for their influence on syngas production and reactant conversion. Optical emission spectroscopy (OES) identified active species, with spectral line intensities analyzed across varying O/C ratios. A validated zero-dimensional kinetic model, aligned with experimental data, revealed that H₂ generation during C₁₂H₂₆ conversion is predominantly driven by recombination of n-dodecane with H atoms. H₂ production primarily arises from hydrocarbon electron impact reactions (e.g., C₂H₆, C₂H₄) and H atom recombination with species such as C₃H₆, CH₄, and C₃H₈. Detailed reaction pathways and mechanisms in the dry reforming system are elucidated through integrated experimental and kinetic modeling analyses. The n-dodecane conversion follows the following order: X(DR) &gt; X(SR) &gt; X(POR).</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100449"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the hydrogen supply chain so far—An assessment and review of critical aspects for an economy supported by a “hydrogen infrastructure”","authors":"Julien Göthel ,&nbsp;Andreas Corsten ,&nbsp;Olena Volkova","doi":"10.1016/j.nxener.2025.100448","DOIUrl":"10.1016/j.nxener.2025.100448","url":null,"abstract":"<div><div>This paper critically evaluates hydrogen's role as a decarbonization strategy, addressing key technical and economic constraints that challenge its widespread adoption. We analyze inherent energy conversion losses that make hydrogen-based systems less efficient than direct electrification for many applications, along with persistent issues in scalable storage, distribution, and production costs. The analysis highlights the specific limitations of the 2 primary production pathways: green hydrogen, constrained by the high cost of electrolyzers and the sourcing of critical materials, and blue hydrogen, which faces concerns regarding methane leakage and the long-term viability of carbon capture technologies. We argue that hydrogen's most effective application is as a strategic enabler for hard-to-abate sectors, such as heavy industry and long-distance transport. The paper also explores innovative concepts like thermochemical looping with metal oxides and the integration of hydrogen into a circular carbon economy as pathways to enhance its efficiency and economic viability. Ultimately, the transition to a global hydrogen economy is a complex, multi-decade undertaking that necessitates a pragmatic, targeted approach with substantial investment and coordinated international policy to realize its full potential.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100448"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of grid-connected inverter topologies and control strategies (2020–2025)","authors":"Rajen Pudur,&nbsp;Mrinal Kanti Rajak","doi":"10.1016/j.nxener.2025.100433","DOIUrl":"10.1016/j.nxener.2025.100433","url":null,"abstract":"<div><div>This comprehensive review examines grid-connected inverter technologies from 2020 to 2025, revealing critical insights that fundamentally challenge industry assumptions about technological advancements and deployment strategies. Quantitative analysis demonstrates that conventional topologies have approached efficiency limits, with 2-level voltage source inverters achieving 96.5%, while advanced multilevel systems reach 98.9%. However, exponential cost increases for marginal gains indicate diminishing returns, which will reshape investment priorities across the $85 billion market evolution. The investigation reveals a previously unquantified performance-reliability trade-off, where 13-level T-type inverters achieve a total harmonic distortion of 0.6% but sacrifice operational lifetime, reducing it from 45,000 h to 18,000 h due to component scaling laws, fundamentally questioning whether advanced topologies provide superior value propositions. Transformerless H5 and highly efficient and reliable inverter concept (HERIC) designs successfully suppress leakage currents by 95%, while maintaining an efficiency of 98% or higher, representing critical breakthroughs that enable widespread photovoltaic integration. Artificial intelligence-based control demonstrates 15–20% dynamic response improvements, despite computational constraints limiting inference to 1.25 <em>μ</em>s, which reveal fundamental barriers to intelligent grid implementation. Supply chain analysis reveals critical deployment vulnerabilities, with component lead times exceeding 26 weeks and 18.9% quarterly price volatility, indicating that technological superiority alone cannot guarantee market success without supply chain resilience. The study identifies 5 priority research areas—wide-bandgap semiconductors, intelligent control, grid-forming capabilities, cybersecurity infrastructure, and advanced materials—providing strategic direction for future development. This approach prioritises grid stability by successfully balancing technical performance against economic viability and supply chain constraints, rather than pursuing efficiency optimisation alone.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100433"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction of power demand and fuel consumption of a power plant: A case study from Bangladesh","authors":"Riaz Ul Hasan,&nbsp;Moinul Islam Moin,&nbsp;Anup Saha,&nbsp;Md Aman Uddin","doi":"10.1016/j.nxener.2025.100444","DOIUrl":"10.1016/j.nxener.2025.100444","url":null,"abstract":"<div><div>This study aims to address the issue of power and fuel shortages in developing economies like Bangladesh, where, despite having sufficient capacity to generate more electricity than needed, the country often faces challenges due to limited fuel availability. To mitigate this problem, the study proposes a predictive model that enables power plants to accurately estimate the required power output at specific times, along with the corresponding fuel needs. Unlike models that rely on extensive sensor networks, this study develops a solution that remains effective under sparse instrumentation, making it suitable for low-resource environments. Machine learning (ML) models were applied to predict power demand and fuel consumption (FC) for a 150 MW heavy fuel oil (HFO) power plant. The research utilized 7 years of operational data (2017–2023) and evaluated the performance of ML algorithms, including K-nearest neighbors (KNNs), artificial neural networks (ANNs), and gradient-boosted regression trees (GBRTs). Power demand was predicted based on 6 input parameters: working hours, FC, auxiliary consumption, atmospheric temperature, relative humidity, and atmospheric pressure. The GBRT algorithm outperformed the others, achieving the highest accuracy with a coefficient of determination (R²) of 0.9994 and a root mean square error (RMSE) of 1.102. The findings highlight the potential of ML in enhancing energy management, with the GBRT model offering precise predictions that can support proactive fuel procurement strategies and help mitigate energy shortages.</div></div>","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100444"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New optimal sizing and accelerated testing to reliably and cost-effectively improve and predict freezer energy performance","authors":"Boaz Wadawa ,&nbsp;Joseph Yves Effa ,&nbsp;Abdellatif Obbadi ,&nbsp;Smail Sahnoun ,&nbsp;Youssef Errami","doi":"10.1016/j.nxener.2025.100445","DOIUrl":"10.1016/j.nxener.2025.100445","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Challenges in terms of developing increasingly efficient systems for cooling and freezing agricultural or pharmaceutical products, are intensifying due to the requirements related to preserving the quality and quantity of products during their storage, transport and distribution on the one hand. And on the other hand, due to the constraints related to the optimal design of systems for producing and maintaining cold, in quantity, over time, regulated, environmentally friendly and cost-effective. Therefore, the major contribution in this work is based on the optimization of the energy performance of cooling and freezing systems, like freezers. To do this, the proposed methodology consists first of establishing a thermal balance of the freezer studied, in order to determine the overall heat transfer coefficients of the walls of the freezer cabin. Then, the formalism combining both the Cobb Douglas type utility function and the Lagrange equation resolution method is used to maximize the heat transfer coefficients of the walls that constitute the freezer compartment. A 99-liter freezer of the RCF-120-B brand is used for the study and data collection under the manufacturer's operating conditions (T&lt;sub&gt;in&lt;/sub&gt; = −19&lt;!--&gt; &lt;!--&gt;°C and T&lt;sub&gt;ex&lt;/sub&gt; = 30.4&lt;!--&gt; &lt;!--&gt;°C). In addition, to analyze and predict the operating reliability of the freezer, we rely on an accelerated Weibull and Vaca-trigo test model which uses 5 samples of 335 measured values (5 * 335) respectively of internal and external temperatures (T&lt;sub&gt;in&lt;/sub&gt; and T&lt;sub&gt;ex&lt;/sub&gt;), internal and external relative humidity (RH&lt;sub&gt;in&lt;/sub&gt; and RH&lt;sub&gt;ex&lt;/sub&gt;), and each time interval (Δt). In addition, the evaluation of the reliability of maintaining the refrigerating capacity when the freezer is stopped is based on a Brownian model which uses the reliability results of Weibull and Vaca-trigo, as well as the 3 samples of 347 measured values (3 * 347) respectively of internal and external temperatures, and each time interval. It appears that the maximum values of the pairs of heat transfer coefficients obtained for the walls of the 99 liter, 200 liter and 282 liter compartments are respectively the following: (K&lt;sub&gt;199max&lt;/sub&gt; = 0.459 W/m&lt;sup&gt;2&lt;/sup&gt;K and K&lt;sub&gt;299max&lt;/sub&gt; = 1.23 W/m&lt;sup&gt;2&lt;/sup&gt;K), (K&lt;sub&gt;1200max&lt;/sub&gt; = 0.641 W/m&lt;sup&gt;2&lt;/sup&gt;K and K&lt;sub&gt;2200max&lt;/sub&gt; = 1.243 W/m&lt;sup&gt;2&lt;/sup&gt;K) and (K&lt;sub&gt;1282max&lt;/sub&gt; = 0.97 W/m&lt;sup&gt;2&lt;/sup&gt;K and K&lt;sub&gt;2282max&lt;/sub&gt; = 0.9832 W/m&lt;sup&gt;2&lt;/sup&gt;K). These maximum values allow the 110 W compressor to be used instead of the 125 W and 200 W compressors respectively in the 200 liter and 282 liter compartments of conventional freezers. The accelerated test simulations show that, unlike the 282-liter freezer which operates for 63 min at 110 W to reach the standard temperature level, the 200-liter freezer at 110 W operates for about 44 min before reaching the standard temperature of −19&lt;!--&gt; &lt;!--&gt;°C. However, the 282-liter freezer at 110 W has ","PeriodicalId":100957,"journal":{"name":"Next Energy","volume":"9 ","pages":"Article 100445"},"PeriodicalIF":0.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}