Energy Conversion and Management-X最新文献

{"title":"Mitigation strategies for achieving carbon neutrality in the iron and steel industry—a case study of Sichuan, China","authors":"Chao Yue ,&nbsp;Jian Zang ,&nbsp;Guangyan He ,&nbsp;Bin Luo ,&nbsp;Xu Liu ,&nbsp;Lei Zhang ,&nbsp;Jun Wang","doi":"10.1016/j.ecmx.2025.101476","DOIUrl":"10.1016/j.ecmx.2025.101476","url":null,"abstract":"<div><div>Combating climate change calls for a low-carbon shift in China's steel industry. With strong renewable energy and widespread use of electric arc furnaces, Sichuan Province is expected to follow a distinct decarbonisation pathway. This study developed a Linear Bottom-up Technology and Energy Selection Model to simulate carbon emission trajectories of Sichuan's iron and steel industry through 2060. The model explicitly incorporates three steelmaking processes: blast furnace–basic oxygen furnace (BF–BOF), scrap-based electric arc furnace (Scrap–EAF), and hydrogen-based direct reduced iron coupled with EAF (DRI–EAF), under three crude steel demand scenarios peaking in 2024, 2027, and 2030. Results indicate that, by 2060, crude steel output will decline by 50%–65%, driving energy consumption down by 71%–80%. Consequently, CO₂ emissions will be reduced by over 90%, with emissions intensity falling to 0.3–0.4 tCO₂ per ton of crude steel. The energy mix shifts significantly, with hydrogen (30%), natural gas (22%), and electricity (19%) replacing coal and coke. BF–BOF will be phased out, while Scrap–EAF will account for 70% of production, and DRI–EAF will rise to 30%, being introduced between 2044 and 2048. Despite deep reductions, carbon capture, utilisation, and storage (CCUS) remains essential for neutralising residual emissions, capturing up to 38% of gross emissions removal. Cost analysis reveals that DRI–EAF requires either a decrease in hydrogen price or the implementation of a carbon pricing mechanism to achieve cost parity. These findings highlight that achieving a cost-effective low-carbon transition requires early demand peaking, the synergistic expansion of Scrap-EAF and DRI-EAF routes, limiting the use of hot metal, and the targeted deployment of CCUS.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101476"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938725","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":"Deep reinforcement learning‑based smart control of solar‑driven power cycle with thermal energy storage: A Los Angeles case study","authors":"Araz Emami,&nbsp;Ata Chitsaz,&nbsp;Amirali Nouri","doi":"10.1016/j.ecmx.2025.101478","DOIUrl":"10.1016/j.ecmx.2025.101478","url":null,"abstract":"<div><div>Solar-driven organic Rankine cycles (ORCs) require precise coordination of working fluid superheat, turbine inlet pressure, and net efficiency. Conventional single-loop control cannot effectively manage these tightly coupled objectives. This study introduces a deep deterministic policy gradient (DDPG) supervisory controller, trained using an 8760 hour global horizontal irradiance (GHI) dataset in a MATLAB-CoolProp environment. The reward function penalizes deviations in superheat, pressure, and efficiency. Over a full–year simulation, compared to a fixed-flow baseline, the Deep reinforcement learning-based (DRL-based) controller significantly dampened seasonal and transient variability: turbine inlet pressure stayed within around 4% of 2.5 MPa, superheat within nearly 0.2 K of a +10 K target, and efficiency between 20 and 30 percentage, improving the annual mean by 6 percentage points. Thermal energy storage exhibited stable, daily state-of-charge cycles, avoiding overcharge and depletion. A genetic algorithm (GA), applied exclusively to DRL controlled data, mapped the pressure, temperature and efficiency trade space. The Pareto front highlighted non-dominated optima near 2.55 MPa and 10.1-10.7 K, while dominated clusters corresponded to off–design pressure regimes. The integrated DRL-GA framework thus enables consistently optimal ORC operation under variable solar input, enhancing efficiency, stability, and component lifespan, and offering a deployable pathway for advanced renewable energy systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101478"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938732","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":"Study on energy conservation of home appliances and equipment through questionnaires and measurement surveys in Japan","authors":"Mao Serikawa","doi":"10.1016/j.ecmx.2025.101488","DOIUrl":"10.1016/j.ecmx.2025.101488","url":null,"abstract":"<div><div>Climate change is a pressing issue that requires immediate action and calls for global efforts to conserve energy and reduce carbon dioxide (CO₂) emissions. A large proportion of energy consumption is attributed to houses, and the influence of home appliances is not negligible. However, regulations regarding energy conservation in houses apply only to attached facilities and do not consider related home appliances. Consequently, there is insufficient information on the actual situation regarding home appliances related to indoor environments, and measures to reduce power consumption are insufficient, leading to situations where occupants unintentionally use electricity. This study used questionnaires to assess the actual appliance and equipment usage conditions in Japanese houses. This study focuses on commonly overlooked appliances that consume a relatively large amount of electricity. Appliance usage varies widely depending on residential characteristics. For example, approximately 50% of households use home appliances or devices to dry clothes, mainly to avoid pollen and weather effects, and reduce the time and effort required for housework. Additionally, measurements and product surveys were conducted to determine the instantaneous power consumption and the amount of power consumed per use of home appliances. By integrating questionnaire and product survey results, the distribution of power consumption of electric heaters, bathroom dryers, and clothes dryers in Japanese houses was determined. These appliances were used frequently or for long periods in a certain percentage of houses and could reduce power consumption by switching to highly efficient devices.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101488"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938951","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":"Comparative analysis of direct-drive and gearbox-coupled electro-hydraulic energy converters","authors":"Thomas Heeger ,&nbsp;Martin West ,&nbsp;Liselott Ericson","doi":"10.1016/j.ecmx.2025.101463","DOIUrl":"10.1016/j.ecmx.2025.101463","url":null,"abstract":"<div><div>Combinations of electric and hydraulic machines, also known as e-pumps or electro-hydraulic energy converters, are essential for the electrification of mobile working machinery. Currently, these machines are typically combined by axial stacking, and the electric machine directly drives the hydraulic machine. Alternatively, the hydraulic machine can be radially integrated within the core of the electric machine, or a gearbox in combination with a downsized electric machine can be used. However, to the authors’ knowledge, no systematic comparison of these different concepts has been published.</div><div>This paper uses analytical methods to determine the dimensions of the active parts of hydraulic machines, electric machines, and gearboxes in order to compare different design concepts based on volume, aspect ratio, total mass, copper mass, magnet mass, electromagnetic efficiency, and inertia.</div><div>Axially stacked concepts can yield the highest compactness. However, they achieve this compactness at low aspect ratios, with their lengths being several times greater than their outer diameters. For balanced aspect ratios, where the outer diameter and total length of the machine are similar, the radially integrated, direct-driven concept is most compact.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101463"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938952","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":"Field-oriented power regulation and loss reduction in micro-hydro induction generation PAT off-grid systems","authors":"Samuel Amaro ,&nbsp;João F.P. Fernandes ,&nbsp;P.J. Costa Branco","doi":"10.1016/j.ecmx.2026.101519","DOIUrl":"10.1016/j.ecmx.2026.101519","url":null,"abstract":"<div><div>Pumps as Turbines (PATs) provide efficient, decentralized energy generation for remote off-grid areas, typically coupled with induction generators (IGs) for cost-effective robustness. However, using typical capacitors for their excitation limits performance under variable hydraulic and electrical conditions. This study explores field-oriented control (FOC)-based control algorithms to replace capacitor banks and improve the performance of the off-grid PAT-IG system under varying hydraulic conditions. Our main contributions are focused on analyzing system efficiency, along with the electromagnetic behavior of the induction generator and the hydraulic-mechanical dynamics of the PAT, under torque- and mechanical-power-controlled FOC strategies. In addition, flux control has been shown to be critical for maximizing the system’s efficiency. The analysis of the off-grid PAT-IG system is evaluated using dynamic non-linear simulations. The simulation results revealed that FOC techniques with optimal flux control allow for maximizing the efficiency of the system for a wide range of hydraulic and electric variables. With capacitors, the PAT-IG shows a highly variable efficiency with a maximum value of around 40% and several zones where the machine is not capable of operating. However, using torque and mechanical power FOC with flux optimization, the system is capable of operating in a wider range with very stable efficiency for different operational points. However, only mechanical power-controlled FOC can achieve almost constant electric power generation in the presence of hydraulic pressure variation. Therefore, from an electric energy generation point of view, the latter technique should be chosen instead of torque-controlled FOC. These findings highlight the need for advanced control strategies in off-grid PAT-IG systems and support their viability as a sustainable component in off-grid applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101519"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938975","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 wind power prediction using rotor equivalent wind speed and machine learning models","authors":"Qamar Abbas ,&nbsp;Shahzada Zaman Shuja ,&nbsp;Hassan Bin Shahid ,&nbsp;Hafiz Muhammad Ali","doi":"10.1016/j.ecmx.2025.101479","DOIUrl":"10.1016/j.ecmx.2025.101479","url":null,"abstract":"<div><div>Accurate wind-power prediction is essential for optimizing wind-farm operation and maintaining grid stability. Traditional approaches based solely on hub-height wind speed often yield substantial errors for large-diameter turbines due to vertical wind-speed variability. This study addresses this limitation by employing the Rotor Equivalent Wind Speed (REWS) method, which accounts for wind-speed distribution across the rotor-swept area to enhance power estimation accuracy. Eleven months of wind data were obtained from the National Renewable Energy Laboratory’s National Solar Radiation Database (NREL-NSRDB) for the Goldwind 2S MW turbine located in Lewes, Delaware, USA. The REWS was computed by dividing the rotor into seven annular segments, and the Weibull distribution was used to characterize the wind profile and estimate power generation. Multiple machine-learning regression models were trained and tested in MATLAB using a 90% – 10% data split to ensure seasonal representation. The models included Decision Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), Ensemble methods (Bagged and Boosted Trees), and Neural Networks. Hyperparameters were optimized through five-fold cross-validation, with the Fine Tree model achieving the lowest prediction error (RMSE = 1.19 ms⁻¹, R² = 0.83). The predicted REWS values showed an average deviation of 14% from the measured data, resulting in an 18% difference in the estimated monthly energy output. These results demonstrate that combining REWS with interpretable machine learning models provides a reliable, computationally efficient framework for wind-power forecasting, particularly when high-resolution or long-term datasets are unavailable. The findings also highlight the potential of REWS-based modeling for future applications to larger turbines and complex terrain conditions.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101479"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938976","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":"DORES: a streamlined software for the design and operational planning of photovoltaic and storage systems in buildings and communities","authors":"Sofiane Kichou ,&nbsp;Nikolaos Skandalos","doi":"10.1016/j.ecmx.2026.101531","DOIUrl":"10.1016/j.ecmx.2026.101531","url":null,"abstract":"<div><div>Photovoltaics (PV) and electrical energy storage (EES) are key technologies for decarbonizing buildings and communities. Yet, planning integrated PV storage systems remains challenging due to solar variability, battery degradation, and economic uncertainty, and it requires simulating operational strategies to support informed design. Existing tools often address only part of this challenge, focusing on PV yield, economics, or microgrid optimization, without combining technical, economic, and environmental indicators across both building and community scales.</div><div>To address this gap, this paper introduces DORES (Design and Operational Planning of Renewable Energy Systems), a modular and user-friendly simulation tool for PV-storage integration. DORES models load profiles (measured or generated), PV systems, and batteries, and implements multiple energy management strategies, ranging from rule-based control to optimization-based methods that include battery wear costs. The tool automatically calculates key performance indicators (self-sufficiency, self-consumption, net present value, life cycle cost, and CO₂ savings) through a graphical interface developed in MATLAB. Validation against monitored data and a benchmark commercial tool (PV*SOL) shows high accuracy, with deviations in key indicators typically below 5%. In addition, DORES enables scenario testing at the community scale, supporting aggregated prosumer systems and shared storage assessment.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101531"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975928","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":"Nighttime thermoelectric power generation beyond 1 W/m2 achieved with concentrated photothermal storage","authors":"Abdulrahman M. Alajlan ,&nbsp;Saad Alrezihy ,&nbsp;Raid Almattairi ,&nbsp;Musaad Alotaibi ,&nbsp;Saichao Dang ,&nbsp;Qiaoqiang Gan","doi":"10.1016/j.ecmx.2026.101524","DOIUrl":"10.1016/j.ecmx.2026.101524","url":null,"abstract":"<div><div>Amid growing environmental challenges, autonomous sensing technologies have become essential tools for sustainable development by reducing dependence on traditional energy sources. Through an unconventional approach, we employ concentrated optical systems to enable nighttime power generation, extending the traditionally daytime-restricted use of solar energy. We achieve nighttime power generation at a density of 1.2 W/m² for the first time in thermoelectric systems, surpassing previous experimental limitations and setting a new benchmark in renewable energy technology. Furthermore, we demonstrate an autonomous sensing application that utilizes the generated nighttime thermoelectric power, highlighting the feasibility and promise of this approach for continuous, sustainable energy use.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101524"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975983","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":"Enhancing Energy Efficiency in a Jojoba Oil Biorefinery: A Case Study in Performance Optimisation","authors":"Mamdouh Gadalla ,&nbsp;Radhi H. Alazmi ,&nbsp;Fatma Ashour ,&nbsp;Thokozani Majozi ,&nbsp;Basudeb Saha","doi":"10.1016/j.ecmx.2026.101546","DOIUrl":"10.1016/j.ecmx.2026.101546","url":null,"abstract":"<div><div>The biorefinery concept, which focuses on converting waste feedstocks into value-added chemicals and biofuels, plays a vital role in the transition to sustainable industrial systems. A critical determinant of the economic viability of such processes is their energy efficiency. This study presents a systematic and hierarchical methodology for the performance analysis and energy-driven optimisation of biorefinery designs. The proposed approach integrates advanced energy integration techniques to enhance process-wide energy utilisation. These techniques combine Pinch Analysis principles, rigorous process simulation using Aspen® packages, steam generation, and carbon credits consideration. A novel Jojoba oil biorefinery is employed as a case study, targeting the production of high-value Jojobyl alcohols (JAs) and fatty acid methyl esters (FAME) as a co-product. Initial utility demands were calculated as 9794.3 kW (cold) and 1978.0 kW (hot) for an annual output of 2[300 metric tons of JAs. Pinch Analysis revealed the process as a threshold problem, requiring only cold utility. The process exhibits a surplus of heat content in the process hot streams capable of satisfying the heat requirement of the cold streams. Therefore, no hot utility is further necessary. Two energy-saving scenarios were developed to optimise the heat exchanger network (HEN): one minimising energy consumption (Design 1) and another incorporating steam generation (Design 2). Exchangers in the network are of the shell-and-tube type. Aspen Energy |Analyzer® (v12) has been used for the simulation of the heat exchanger networks. Aspen HYSYS® (v12) was used to simulate the properties of process stream components rigorously. Results achieved a 20.2% reduction in cooling utility demand for Design 1, while Design 2 achieved 39.6% reduction, with both designs eliminating the hot utility requirements. The steam generation-based solution delivered an estimated annual profit increase of $519898.1 and an overall CO₂ emissions cut of 10326.5 t/y. Carbon credits worth $774,487.5 were gained annually as additional revenue through participation in the EU Emission Trading System. These results demonstrate that strategic energy integration significantly enhances both the economic and environmental performance of biorefineries. The study directly contributes to the advancement of a sustainable bioeconomy and supports the objectives of UN Sustainable Development Goal 7: Affordable and Clean Energy.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101546"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976516","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":"Hybrid Al2O3-MWCNT nano-coatings for advanced solar thermal systems: enhancing energy conversion and sustainability","authors":"S. Dhivya ,&nbsp;Jayant Giri ,&nbsp;Refka Ghodhbani ,&nbsp;Moaz Al-lehaibi ,&nbsp;Ahmad O. Hourani ,&nbsp;Likius Shipwiisho Daniel ,&nbsp;Thandiwe Sithole ,&nbsp;Kassian T.T. Amesho","doi":"10.1016/j.ecmx.2025.101452","DOIUrl":"10.1016/j.ecmx.2025.101452","url":null,"abstract":"<div><div>Evacuated tube collectors (ETCs) are advanced solar thermal systems designed to capture and retain solar energy for heating, offering reliable performance even in low-temperature environments. This study is motivated to improve the thermal performance and efficiency of evacuated tube solar collectors by developing a durable, high-absorptivity coating. Conventional coatings often suffer from heat loss and limited energy conversion. By introducing a hybrid Al₂O₃–MWCNT nano-enhanced coating, this study aims to enhance solar absorption, thermal conductivity, and overall energy utilization, contributing to more efficient and sustainable solar energy systems. The hybrid nano-enhanced coatings were applied using the spray pyrolysis technique at 300 °C, with two thickness variations: 1 mm and 2 mm. The results reveal that the 2 mm hybrid Al₂O₃/MWCNT coating significantly enhances ETC performance. The maximum fluid temperature reached 93.6 °C, with a heat absorption of 623.1 W. The heat transfer coefficient improved to 561.9 W/m²K, achieving a thermal efficiency of 82.7 %. Exergy efficiency increased to 20.7 %, indicating better energy utilization. The enviro-economic analysis demonstrated an energy output of 548.1 kWh, CO₂ savings of 5.12 kWh/$, and an annual CO₂ reduction cost of $64.71. These findings highlight the potential of hybrid nano-coatings in optimizing solar thermal systems for sustainable energy solutions.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"29 ","pages":"Article 101452"},"PeriodicalIF":7.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938978","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}