Energy Conversion and Management-X最新文献

{"title":"CO2 utilization for H2-rich syngas production in a combined system: Bi-objective optimization and machine learning analysis","authors":"Parisa Mojaver","doi":"10.1016/j.ecmx.2026.101588","DOIUrl":"10.1016/j.ecmx.2026.101588","url":null,"abstract":"<div><div>This study aimed to mitigate environmental risks in energy production through the design of a system that generates high-quality syngas from a blend of poplar wood and polyethylene terephthalate waste. CO₂ was employed as the gasifying agent, an approach that both eliminates nitrogen dilution in the syngas stream and offers a practical pathway for CO₂ utilization from industrial emissions, thereby linking clean energy production with greenhouse gas reduction. To assess the validity and robustness of the developed models, a residual analysis was performed. Subsequently, a bi-objective optimization was conducted to simultaneously maximize cold gas efficiency and the H₂/CO ratio. The reliability of the machine learning model was evaluated by comparing its predictions with the outcomes derived from thermodynamic simulations. The results demonstrated that the optimal operating range was within a gasifier agent to fuel of 1.95–2.15 and a water gas shift reactor agent to fuel of 1.75–1.90. In this range, the system achieved cold gas efficiencies between 97% and 98%, along with H₂/CO ratio percentage ranging from 80% to 90%. The comparative analysis indicated that the results predicted by machine learning models showed strong agreement with those obtained from the engineering equation solver simulation software.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101588"},"PeriodicalIF":7.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080235","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 hybrid degradation prediction method for PEMFC integrating model-based degradation index extraction and Bayesian-optimized Bi-directional long short-term memory","authors":"Chang Ke ,&nbsp;Kai Han ,&nbsp;Yongzhen Wang ,&nbsp;Rongrong Zhang ,&nbsp;Xuanyu Wang ,&nbsp;Ziqian Yang ,&nbsp;Xiaolong Li","doi":"10.1016/j.ecmx.2026.101593","DOIUrl":"10.1016/j.ecmx.2026.101593","url":null,"abstract":"<div><div>Accurately estimating the state of health of proton exchange membrane fuel cell (PEMFC) and predicting the degradation trend are essential prerequisites for effective health management to enhance durability. This paper proposes a generalized hybrid degradation prediction method for PEMFC that is applicable to diverse operating conditions. Firstly, the internal polarization dynamics are characterized via the distribution of relaxation times method, and a third-order equivalent circuit model is established to quantify the polarization losses. The voltage losses are quantified using a polarization curve model. Degradation characteristic analysis from both approaches consistently reveals that deterioration in mass transfer kinetics and charge transfer kinetics is the primary cause of performance degradation. Subsequently, component-level degradation indexes are extracted based on degradation models, and a novel weighted fusion method is proposed to construct a hybrid degradation index characterizing the overall degradation state of PEMFC. Finally, the Bayesian-optimized Bi-directional long short-term memory (Bi-LSTM) model is employed to predict PEMFC degradation trend under various prediction horizons, enabling accurate estimation of remaining useful life (RUL). The results show that the optimized Bi-LSTM achieves higher RUL estimation accuracy than the baseline Bi-LSTM, and the hybrid method outperforms the AutoML-based method and the cascaded echo state network reported in previous studies. For the first stack, the estimation error remains below 7.78%, with a minimum error of 0.50%. For the second stack, the estimation error does not exceed 12.28% overall and drops below 10% when the prediction horizon is within 300 h, with a minimum error of 2.67%.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101593"},"PeriodicalIF":7.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080386","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":"Simultaneous sizing of a photovoltaic system and compressed air energy storage in a microgrid","authors":"Tshilumba Kalala,&nbsp;Mwana Wa Kalaga Mbukani","doi":"10.1016/j.ecmx.2026.101571","DOIUrl":"10.1016/j.ecmx.2026.101571","url":null,"abstract":"<div><div>The integration of Compressed Air Energy Storage (CAES) with photovoltaic (PV) systems, complemented by grid interconnection capabilities and diesel generator backup, represents an advanced approach to sustainable microgrid design for future energy systems. In this study, a multi-objective optimization model for sizing PV-CAES systems is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem with two primary objective functions: (1) minimization of total system investment costs (CAPEX) and operational costs (OPEX), and (2) enhancement of system reliability and maximization of RE penetration. The Augmented <span><math><mi>ϵ</mi></math></span>-constraint method is applied to solve this multi-objective optimization problem by incorporating the reliability and RE penetration objectives as inequality constraints, while maintaining cost minimization as the overall optimization goal. In application to a case study of a South African commercial building, the optimized design saves annual operational costs by 35.2% and achieves 41.5% penetration of RE and 2.4% increase in reliability compared with conventional designs. The results demonstrate the success of the framework in providing economically viable PV-CAES configurations that simultaneously enhance sustainability and system reliability via comprehensive mathematical optimization.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101571"},"PeriodicalIF":7.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039770","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 textile nanogenerators for wearable energy harvesting: synergistic mechanisms, challenges, and future directions","authors":"Bekinew Kitaw Dejene ,&nbsp;Misganaw Engdasew Woldeab","doi":"10.1016/j.ecmx.2026.101589","DOIUrl":"10.1016/j.ecmx.2026.101589","url":null,"abstract":"<div><div>The rapid expansion of wearable electronics and the Internet of Things (IoT) has intensified the demand for sustainable, lightweight, and flexible power solutions beyond conventional batteries, which suffer from short lifespans, bulkiness, and environmental concerns. Hybrid textile nanogenerators (HTNGs) offer a sustainable and transformative alternative by integrating multiple energy conversion mechanisms, such as piezoelectric, triboelectric, thermoelectric, solar, enzymatic biofuel cell, and electromagnetic effects, into flexible textile platforms. By exploiting the synergistic interactions between different nanogenerator modes, HTNGs achieve superior energy output, multifunctionality, and system stability compared to single-mode devices. This review first introduces the fundamental principles and classifications of HTNGs in textile systems, followed by a discussion of the materials and fabrication strategies that enable seamless integration into fabrics while preserving softness, comfort, and breathability. Recent hybridization strategies, performance metrics, and design innovations are critically evaluated, with attention to durability, washability, and large-scale manufacturability, which are crucial for practical applications. Applications in wearable health monitoring, self-powered sensing, smart garments, and the IoT are examined alongside key challenges such as scalability and user comfort. Finally, future perspectives are outlined, emphasizing cross-disciplinary opportunities, including eco-friendly materials, scalable manufacturing, and intelligent energy management, such as AI-assisted optimization, to accelerate the transition of HTNGs from laboratory prototypes to commercially viable, self-sustaining wearable systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101589"},"PeriodicalIF":7.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039791","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":"Leveraging machine learning for advanced flow field design in PEMFCs","authors":"Mehrdad Ghasabehi,&nbsp;Mehrzad Shams","doi":"10.1016/j.ecmx.2026.101586","DOIUrl":"10.1016/j.ecmx.2026.101586","url":null,"abstract":"<div><div>The overall performance of proton exchange membrane fuel cells (PEMFCs) strongly depends on the design of the flow field. This study presents a novel, enhanced tapered parallel flow field featuring sub-channels with widths that vary in a precisely engineered converging–diverging pattern. This innovative design significantly improves oxygen transport in both through-plane and in-plane directions, thereby enhancing water management and ensuring highly consistent reactant delivery to reaction sites. In addition, a machine-learning-based optimisation framework is developed for this flow field. Using a rigorously validated three-dimensional, two-phase CFD model, an extensive dataset of 184 cases is generated to train seven distinct data-driven surrogate models: adaptive neuro fuzzy inference system (ANFIS), artificial neural network (ANN), response surface methodology (RSM), random forest (RF), CatBoost, XGBoost, and LightGBM. Notably, CatBoost demonstrates superior predictive accuracy for key oxygen mass-transfer metrics and was consequently employed in a sophisticated multi-objective optimization. This process yields an optimal flow-field geometry with a tapering ratio of 3.8, a cycling amplitude of 0.57 mm, and eight cycles at 0.694 V, achieving a high mean oxygen concentration of 0.020 kmol.m⁻³ and an excellent uniformity index of 0.93. This integrated machine learning-accelerated optimization framework enables rapid and reliable flow-field optimisation and provides practical, actionable design guidelines for effectively reducing oxygen starvation in next-generation, high-performance fuel-cell stacks.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101586"},"PeriodicalIF":7.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190003","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":"Photovoltaic energy forecast; influence of two numerical weather forecast datasets on the performance of an analytical and three machine learning models","authors":"Garazi Etxegarai ,&nbsp;Juan Hernández ,&nbsp;Irati Zapirain ,&nbsp;Haritza Camblong ,&nbsp;Jon Saenz ,&nbsp;Octavian Curea","doi":"10.1016/j.ecmx.2026.101577","DOIUrl":"10.1016/j.ecmx.2026.101577","url":null,"abstract":"<div><div>The European Union aims to reduce reliance on fossil fuels by achieving a 42.5% share of renewable energy sources by 2030. Self-consumption and the associated energy management systems (EMS) are essential to the integration of renewable energy sources, of which photovoltaic energy is a key component. As part of the EMS tasks, photovoltaic power forecasting is critical due to the weather-related variability in generation. This research study evaluates the performance of 24-hour photovoltaic production forecasting considering meteorological data obtained from two different numerical weather prediction models, the European ECMWF and the Galician MeteoGalicia, three machine learning (ML) models, Feedforward Neural Networks (FFNN), Support Vector Regression and Nonlinear Autoregressive Neural Network with Exogenous Inputs, and an analytical model. Results show that the FFNN performs best, especially in summer, with a R² of 0.9 when using predicted weather data. Furthermore, ML models trained with MeteoGalicia data outperform ECMWF-based models. For instance, the FFNN obtains an improvement over benchmark of 8.6% with MG data and 5.7% with ECMWF data, during November. Winter forecasting challenges highlight the need for good ML models to address variability. Moreover, analytical models underperformed compared to ML methods when using forecasted weather data, emphasizing the partial compensation from ML models for weather prediction errors.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101577"},"PeriodicalIF":7.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039761","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":"Optimal placement of EV charging Stations, DSTATCOM, BESS, and DGs in radial distribution systems using an enhanced Fractional-Order differential Evolution-Based optimization algorithm","authors":"Vivekananda Pattanaik ,&nbsp;Binaya Kumar Malika ,&nbsp;Pravat Kumar Rout ,&nbsp;Binod Kumar Sahu ,&nbsp;Shubhranshu Mohan Parida ,&nbsp;Subhasis Panda ,&nbsp;Mohit Bajaj ,&nbsp;Vojtech Blazek ,&nbsp;Lukas Prokop","doi":"10.1016/j.ecmx.2026.101594","DOIUrl":"10.1016/j.ecmx.2026.101594","url":null,"abstract":"<div><div>Electric Vehicle Charging Stations (EVCS), Distributed Static Compensators (DSTATCOM), Battery Energy Storage Systems (BESS), and Distributed Generators (DGs) are integrated and operate in a coordinated way into radial distribution systems (RDS) to offer substantial aids in terms of voltage support, loss minimization, and reliability enhancement during regular operation. However, the decision to take their placement optimally and simultaneously is a complex task due to the nonlinear, bidirectional, and highly constrained nature of the radial distribution system. In response to this issue, this paper proposes an enhanced fractional order differential evolution (EFODE) for a more accurate, reliable, and optimal solution. Unlike non-adaptive versions of differential evolution (DE), which have insufficient exploration ability and lack adaptability to historical information, this study proposes an innovative approach to fractional-order DE (FODE). The proposed strategic formulation impact on enhancing DE performance. A bi-strategy co-deployment framework is incorporated, combining the concepts of population-based and parameter-based strategies to leverage their respective individual advantages, nullifying their limitations through mutual influence. In addition, the fractional order (FO) calculus is used to enhance the differential vector’s exploration and exploitation abilities, which are achieved through the incorporation of historical information from populations in the formulation, thereby ensuring the diversity of populations in an evolutionary process. By adaptively varying the most sensitive system factors dynamically according to the system’s performance, it accelerates convergence and prevents premature stagnation. The proposed method is simulated and validated on standard IEEE RDS 33, 69 and 85 test systems, considering multiple constant load, voltage-dependent variable load, and penetration scenarios. Simulation and comparative results demonstrate significant improvements in terms of voltage profile, reduction of active power loss, and overall solution quality. The comparative analysis with conventional metaheuristics confirms the effectiveness and robustness of the approach.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101594"},"PeriodicalIF":7.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039788","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":"Synergistic integration of phase change materials in solar stills for continuous and high-efficiency desalination: a comprehensive review","authors":"Farhan Lafta Rashid ,&nbsp;Karrar A. Hammoodi ,&nbsp;Najah M.L. Al Maimuri ,&nbsp;Mushtaq K. Abdalrahem ,&nbsp;Saif Ali Kadhim ,&nbsp;Ali M. Ashour ,&nbsp;Abdallah Bouabidi ,&nbsp;Hayder I. Mohammed ,&nbsp;Arman Ameen","doi":"10.1016/j.ecmx.2026.101548","DOIUrl":"10.1016/j.ecmx.2026.101548","url":null,"abstract":"<div><div>The underlying challenge of solar intermittency still undermines the operational lifetime and freshwater output of typical solar desalination systems and, hence, this extensive review was carried out to summarize recent efforts (2020–2025) directed at the incorporation of PCM (and specifically, NEPCM) into solar distillation-based systems. Using a systematic thematic approach, the literature selected for review was classified into four macro areas, NEPCM-based improvements, hybrid solar–thermal systems, advanced absorber and condenser designs and PCM materials, with performance data being extracted and reported to evaluate their synergistic contribution towards desalination efficiency. The integrated results show that NEPCM integration can lead to more than 124.2% increase in freshwater productivity, over 82% thermal efficiency and remarkable nocturnal distillate and these values are achievable by the constant operation of a solar still for full day using this strategy. Economic studies also indicate that the proposed optimal solar stills incorporating PCMs deliver the lowest water production cost to date of ∼$0.0082/L and substantially shortened payback periods as low as 25 days, whilst environmental scenarios reveal CO₂ mitigation potentials in excess of 34 tons per year. In summary, this review represents a shift in the design paradigm of sustainable desalination, suggesting orchestrated PCM use as a fundamental breakthrough to realize an affordable water generation solution that operates continually in less developed regions plagued by poor energy infrastructure. These results together narrow the bridge between emerging demonstration and scale device for desalination practice, providing a powerful paradigm to tackle worldwide water shortage with advanced thermal energy storage assembly.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101548"},"PeriodicalIF":7.6,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039771","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":"Transforming sustainable energy through microgrids: Bangladesh perspectives","authors":"Md. Kawsar Hossain ,&nbsp;Abu Saleh Molla ,&nbsp;Prangon Chowdhury ,&nbsp;Faysal Amin Tanvir ,&nbsp;Omar Farrok","doi":"10.1016/j.ecmx.2026.101585","DOIUrl":"10.1016/j.ecmx.2026.101585","url":null,"abstract":"<div><div>Despite achieving universal access to electricity, Bangladesh faces severe energy issues that hinder its sustainable development. The country experiences frequent power outages, transmission losses, and overreliance on imported fuels. Many rural communities still face unreliable electricity, despite having plenty of untapped renewable resources. Existing studies have discussed microgrids’ technical, economic, and environmental aspects in different regions. To the best of the authors’ knowledge, none have evaluated detailed implementation challenges or their contribution to sustainable development. In this regard, this paper investigates how microgrids can transform Bangladesh’s energy landscape while meeting sustainability goals. It identifies key barriers to adoption such as weak infrastructure, cybersecurity risks, financial constraints, complex funding arrangements, difficulties in public–private partnerships, and challenges around social acceptance. It also reviews the technical requirements and policy frameworks within the country’s wider development agenda. Potential sites for microgrid deployment are mapped out, along with the policies needed to integrate them into the grid. Using SWOT and PESTLE frameworks, this paper observes factors influencing microgrid adoption, considering Bangladesh’s geographical and socioeconomic conditions. Based on the assessment, this study finds that microgrids can offer viable solutions to current energy problems by reducing import dependency, creating local employment, and delivering reliable rural power supply. However, regulatory ambiguity, limited technical capacity, high capital costs, and poor inter-agency coordination present significant obstacles. It can stimulate economic growth, enhance educational and healthcare services, and strengthen climate resilience. This analysis provides actionable recommendations for policymakers, investors, and development organisations addressing similar energy access challenges in other developing nations.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101585"},"PeriodicalIF":7.6,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039794","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":"Analysis of dominant tars production kinetics at low reaction temperature using small scale downdraft gasifier with wood pellet and reaction kinetics simulation","authors":"Hiroshi Enomoto","doi":"10.1016/j.ecmx.2026.101572","DOIUrl":"10.1016/j.ecmx.2026.101572","url":null,"abstract":"<div><div>Wood biomass with low abundance density is best utilized in small-capacity processing equipment. In small-capacity pyrolysis equipment, as the impact of heat loss is significant, the effect of low-temperature reactions is large. At low temperatures, the composition of tar, which causes mechanical damage, differs greatly. However, there are few cases in which the impact of these low-temperature reactions has been properly considered based on results obtained under realistic operating conditions. Therefore, in this paper, the main components of tar (acetic acid, benzene, phenol, and naphthalene) were measured experimentally and compared with the calculation results of a conventional kinetic model using CHEMKIN with the CRECK model applied. For the experiment, a self-made downdraft gasifier (self-heating type, without special insulation) using commercially available wood pellets (log cedar) was used. The results showed a significant difference between the experimental and calculated values for benzene density in the low reaction temperature range below 800 °C. This low-density phenomenon should be caused by 1) low reaction temperature, 2) short residence time, and 3) insufficient low-temperature reaction kinetics model.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101572"},"PeriodicalIF":7.6,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080382","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}