Pub Date : 2026-05-01Epub Date: 2026-01-19DOI: 10.1016/j.ecmx.2026.101588
Parisa Mojaver
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.
{"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. CO<sub>2</sub> was employed as the gasifying agent, an approach that both eliminates nitrogen dilution in the syngas stream and offers a practical pathway for CO<sub>2</sub> 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 H<sub>2</sub>/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 H<sub>2</sub>/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":"2026-05-01","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}
Pub Date : 2026-05-01Epub Date: 2026-01-17DOI: 10.1016/j.ecmx.2026.101583
Bence Biró , Csaba Kiss , Roland Molontay , Attila Aszódi
The COVID-19 pandemic and the volatility of the energy market triggered by Russia’s invasion of Ukraine have highlighted the critical importance of the reliability and transparency of electricity price forecasting. The use of artificial intelligence models based on explainable AI has become essential for market participants to develop more efficient and informed strategies by making predictions more meaningful. Within the framework of this study, we developed artificial intelligence models based on open-source data and models for forecasting the day-ahead electricity prices in 19 European countries and analyzed the importance of different features in the models for the forecasting using SHAP values. Our results showed that for both 2015–2020 and 2020–2024 periods, the tree-based machine learning models performed best in price forecasting. By analyzing our models using SHAP, we show how much feature importance has changed from 2020 to 2024, demonstrating the increased complexity of electricity price forecasting due to the energy crisis and structural changes in the electricity system. Using Germany and France as case studies, we present detailed results for the two distinct electricity markets across both modelled periods.
{"title":"Industry-adaptable explainable AI based methodology for forecasting electricity prices","authors":"Bence Biró , Csaba Kiss , Roland Molontay , Attila Aszódi","doi":"10.1016/j.ecmx.2026.101583","DOIUrl":"10.1016/j.ecmx.2026.101583","url":null,"abstract":"<div><div>The COVID-19 pandemic and the volatility of the energy market triggered by Russia’s invasion of Ukraine have highlighted the critical importance of the reliability and transparency of electricity price forecasting. The use of artificial intelligence models based on explainable AI has become essential for market participants to develop more efficient and informed strategies by making predictions more meaningful. Within the framework of this study, we developed artificial intelligence models based on open-source data and models for forecasting the day-ahead electricity prices in 19 European countries and analyzed the importance of different features in the models for the forecasting using SHAP values. Our results showed that for both 2015–2020 and 2020–2024 periods, the tree-based machine learning models performed best in price forecasting. By analyzing our models using SHAP, we show how much feature importance has changed from 2020 to 2024, demonstrating the increased complexity of electricity price forecasting due to the energy crisis and structural changes in the electricity system. Using Germany and France as case studies, we present detailed results for the two distinct electricity markets across both modelled periods.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101583"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080236","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}
Pub Date : 2026-05-01Epub Date: 2026-02-06DOI: 10.1016/j.ecmx.2026.101662
Rizwan Ali , Sadiya Mushtaq , Chin Kui Cheng , Mohammad Abu Haija , Maryam Khaleel , Khalid Al-Ali
Thermocatalytic methane splitting offers a viable route for hydrogen production without greenhouse gas emissions. In this study, a detailed kinetic and deactivation analysis was conducted for methane splitting over a Ni–Ga/KCC1 catalyst using a fixed-bed reactor at temperatures ranging from 600 to 650 °C and varying methane partial pressures (0.2–0.8 atm). Structure–activity correlations were explored via characterization of fresh and spent catalysts using X-ray diffraction (XRD), nitrogen physisorption, hydrogen temperature-programmed reduction (H2-TPR), Transmission electron microscopy (TEM) and thermogravimetric analysis (TGA). The intrinsic reaction kinetics revealed a reaction order of 0.56 and an activation energy of 62.16 kJ mol−1. Catalyst deactivation behavior was systematically investigated using two modeling approaches: the Power-Law Model (PLM) and the Exponential Decay Model (EDM). However, EDM provided an overall better fit compared to PLM. Based on decay model parameters (A and τ), the activity factor was quantitatively modeled as a function of methane partial pressure and reaction temperature, showing strong agreement with experimental data. This work establishes a comprehensive kinetic and deactivation framework for Ni–Ga/KCC1-catalyzed methane splitting, providing predictive insights for hydrogen production under varied process conditions.
{"title":"Kinetics and deactivation modelling of fibrous silica-supported Ni-Ga catalysts for COx-free hydrogen production via methane splitting","authors":"Rizwan Ali , Sadiya Mushtaq , Chin Kui Cheng , Mohammad Abu Haija , Maryam Khaleel , Khalid Al-Ali","doi":"10.1016/j.ecmx.2026.101662","DOIUrl":"10.1016/j.ecmx.2026.101662","url":null,"abstract":"<div><div>Thermocatalytic methane splitting offers a viable route for hydrogen production without greenhouse gas emissions. In this study, a detailed kinetic and deactivation analysis was conducted for methane splitting over a Ni–Ga/KCC1 catalyst using a fixed-bed reactor at temperatures ranging from 600 to 650 °C and varying methane partial pressures (0.2–0.8 atm). Structure–activity correlations were explored via characterization of fresh and spent catalysts using X-ray diffraction (XRD), nitrogen physisorption, hydrogen temperature-programmed reduction (H<sub>2</sub>-TPR), Transmission electron microscopy (TEM) and thermogravimetric analysis (TGA). The intrinsic reaction kinetics revealed a reaction order of 0.56 and an activation energy of 62.16 kJ mol<sup>−1</sup>. Catalyst deactivation behavior was systematically investigated using two modeling approaches: the Power-Law Model (PLM) and the Exponential Decay Model (EDM). However, EDM provided an overall better fit compared to PLM. Based on decay model parameters (A and τ), the activity factor was quantitatively modeled as a function of methane partial pressure and reaction temperature, showing strong agreement with experimental data. This work establishes a comprehensive kinetic and deactivation framework for Ni–Ga/KCC1-catalyzed methane splitting, providing predictive insights for hydrogen production under varied process<!--> <!-->conditions.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101662"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189855","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}
Pub Date : 2026-05-01Epub Date: 2026-02-04DOI: 10.1016/j.ecmx.2026.101657
Izabella Simonsson, Erik Jonasson, Irina Temiz, Hans Bernhoff
The Nordic synchronous area strives to achieve a fossil-free energy system, requiring significant expansion of renewable electricity generation. While onshore wind power is technologically mature, offshore wind, particularly floating installations, offers access to stronger and more consistent wind resources in deeper waters. In this study, the potential of floating offshore wind power in the Nordic synchronous area is evaluated through a 21-year (2004–2024) wind resource analysis using ERA5 reanalysis data for 11 geographically distributed sites across several seas. A MATLAB-based model was developed to simulate the wind power generation. Simulating local compressed air energy storage at each site and centralized hydropower storage, the total losses and curtailments of the proposed system are determined. A system with both local and centralized storage demonstrates greater reliability in providing a baseload to the grid than a system with only local storage. Additionally, the geographical smoothing significantly reduces variability, with correlation between sites decaying exponentially with distance. The system has the potential to provide 187.3 TWh annually. Furthermore, the seasonal variation in the Nordic synchronous area was integrated into the model. It showed higher demand during the winter and lower demand during the summer, and demonstrated reliability in providing a baseload to the grid, with an annual output of 189.5 TWh. Floating offshore wind, combined with local storage and existing hydropower flexibility, can contribute to the Nordic synchronous area for baseload supply and enhance system reliability while expanding generation and supporting the region’s decarbonization goals.
{"title":"Floating offshore wind in the Nordic synchronous area: Resource potential, geographical smoothing, and storage integration","authors":"Izabella Simonsson, Erik Jonasson, Irina Temiz, Hans Bernhoff","doi":"10.1016/j.ecmx.2026.101657","DOIUrl":"10.1016/j.ecmx.2026.101657","url":null,"abstract":"<div><div>The Nordic synchronous area strives to achieve a fossil-free energy system, requiring significant expansion of renewable electricity generation. While onshore wind power is technologically mature, offshore wind, particularly floating installations, offers access to stronger and more consistent wind resources in deeper waters. In this study, the potential of floating offshore wind power in the Nordic synchronous area is evaluated through a 21-year (2004–2024) wind resource analysis using ERA5 reanalysis data for 11 geographically distributed sites across several seas. A MATLAB-based model was developed to simulate the wind power generation. Simulating local compressed air energy storage at each site and centralized hydropower storage, the total losses and curtailments of the proposed system are determined. A system with both local and centralized storage demonstrates greater reliability in providing a baseload to the grid than a system with only local storage. Additionally, the geographical smoothing significantly reduces variability, with correlation between sites decaying exponentially with distance. The system has the potential to provide 187.3 TWh annually. Furthermore, the seasonal variation in the Nordic synchronous area was integrated into the model. It showed higher demand during the winter and lower demand during the summer, and demonstrated reliability in providing a baseload to the grid, with an annual output of 189.5 TWh. Floating offshore wind, combined with local storage and existing hydropower flexibility, can contribute to the Nordic synchronous area for baseload supply and enhance system reliability while expanding generation and supporting the region’s decarbonization goals.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101657"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189999","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}
The growth of electric vehicles (EVs) has driven an increase in the number of used lithium-ion batteries (LiBs). The used LiBs still have the potential to be reused in second-life battery (SLB) applications. Characterization of the used LiB performance is necessary to ensure its suitability for second-life battery (SLB) applications. This study aims to obtain the optimum C-rate for used online motorcycle taxi batteries. The analysis of the impact of C-rate on battery capacity and surface temperature was conducted over 50 cycles at C-rates of 1C, 0.5C, and 0.3C. The study results show that a high C-rate has a significant impact on the capacity and efficiency of used batteries in the charging-discharging process. The obtained data suggest that used batteries are more stable at a lower C-rate (0.3C). A high C-rate also affects the temperature increase of used batteries during charging and discharging. At a C-rate of 1C, the temperature increase can reach approximately 6 – 10 ℃, whereas at a low C-rate of 0.3C, the temperature increase is only around 2 – 3.5 ℃. To reduce safety risks in the second-life application of used batteries, it is recommended to operate at low C-rates.
{"title":"Characterization of new and used Lithium-Ion electric motorcycle batteries degradation through cycle testing and surface temperature evaluation for second-life applications","authors":"Suroso , Triyogi Yuwono , Alief Wikarta , Widyastuti , Surya Putra Andrianto , Anugrah Andisetiawan , Romel Hidayat , Masytha Ramdhiny , Rifdah Adya Salsabila , Liyana Labiba Zulfa , Sikandar Aftab","doi":"10.1016/j.ecmx.2026.101640","DOIUrl":"10.1016/j.ecmx.2026.101640","url":null,"abstract":"<div><div>The growth of electric vehicles (EVs) has driven an increase in the number of used lithium-ion batteries (LiBs). The used LiBs still have the potential to be reused in second-life battery (SLB) applications. Characterization of the used LiB performance is necessary to ensure its suitability for second-life battery (SLB) applications. This study aims to obtain the optimum C-rate for used online motorcycle taxi batteries. The analysis of the impact of C-rate on battery capacity and surface temperature was conducted over 50 cycles at C-rates of 1C, 0.5C, and 0.3C. The study results show that a high C-rate has a significant impact on the capacity and efficiency of used batteries in the charging-discharging process. The obtained data suggest that used batteries are more stable at a lower C-rate (0.3C). A high C-rate also affects the temperature increase of used batteries during charging and discharging. At a C-rate of 1C, the temperature increase can reach approximately 6 – 10 ℃, whereas at a low C-rate of 0.3C, the temperature increase is only around 2 – 3.5 ℃. To reduce safety risks in the second-life application of used batteries, it is recommended to operate at low C-rates.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101640"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190170","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}
Pub Date : 2026-05-01Epub Date: 2026-01-19DOI: 10.1016/j.ecmx.2026.101593
Chang Ke , Kai Han , Yongzhen Wang , Rongrong Zhang , Xuanyu Wang , Ziqian Yang , Xiaolong Li
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%.
{"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 , Kai Han , Yongzhen Wang , Rongrong Zhang , Xuanyu Wang , Ziqian Yang , 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":"2026-05-01","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}
Pub Date : 2026-05-01Epub Date: 2026-01-24DOI: 10.1016/j.ecmx.2026.101584
Florian Altmann , Dominik Kuzdas , Dominik Murschenhofer , Johanna Bartlechner , Christoph Hametner , Stefan Jakubek , Stefan Braun
To enhance the durability and performance of proton exchange membrane fuel cells, it is essential to capture both spatial and temporal variations of internal states during dynamic operation. While existing reduced-order models (0D/1D) lack spatial resolution, 3D models are often too computationally expensive for transient simulations. To bridge this gap, we present a quasi-2D, time-dependent multiphase model capable of predicting distributed cell states with high computational efficiency. The model accounts for key transport phenomena, including convection, multicomponent diffusion, capillary effects, and membrane water dynamics via electro-osmotic drag and diffusion. It also includes nitrogen crossover, finite-rate sorption/desorption at membrane interfaces, and heat generation from electrochemical reactions, proton conduction, and phase change. A linearisation scheme combined with Chebyshev collocation ensures low computational cost and near real-time capability. Validation against high-resolution 3D computational fluid dynamics simulations confirms the model’s accuracy in predicting polarisation curves, gas species distributions, liquid water accumulation, and temperature profiles. Dynamic simulations under load transients further demonstrate its ability to capture key physical processes, underpinning the importance of spatially resolved water transport. By enabling fast and accurate simulations of both steady-state and dynamic fuel cell behaviour, the proposed model supports extensive parametric studies, control system development, and predictive diagnostics. Its computational efficiency makes it a valuable tool for improving fuel cell efficiency, longevity, and system-level control strategies.
{"title":"A quasi-2D multiphase flow proton exchange membrane fuel cell model for efficient distributed cell state prediction","authors":"Florian Altmann , Dominik Kuzdas , Dominik Murschenhofer , Johanna Bartlechner , Christoph Hametner , Stefan Jakubek , Stefan Braun","doi":"10.1016/j.ecmx.2026.101584","DOIUrl":"10.1016/j.ecmx.2026.101584","url":null,"abstract":"<div><div>To enhance the durability and performance of proton exchange membrane fuel cells, it is essential to capture both spatial and temporal variations of internal states during dynamic operation. While existing reduced-order models (0D/1D) lack spatial resolution, 3D models are often too computationally expensive for transient simulations. To bridge this gap, we present a quasi-2D, time-dependent multiphase model capable of predicting distributed cell states with high computational efficiency. The model accounts for key transport phenomena, including convection, multicomponent diffusion, capillary effects, and membrane water dynamics via electro-osmotic drag and diffusion. It also includes nitrogen crossover, finite-rate sorption/desorption at membrane interfaces, and heat generation from electrochemical reactions, proton conduction, and phase change. A linearisation scheme combined with Chebyshev collocation ensures low computational cost and near real-time capability. Validation against high-resolution 3D computational fluid dynamics simulations confirms the model’s accuracy in predicting polarisation curves, gas species distributions, liquid water accumulation, and temperature profiles. Dynamic simulations under load transients further demonstrate its ability to capture key physical processes, underpinning the importance of spatially resolved water transport. By enabling fast and accurate simulations of both steady-state and dynamic fuel cell behaviour, the proposed model supports extensive parametric studies, control system development, and predictive diagnostics. Its computational efficiency makes it a valuable tool for improving fuel cell efficiency, longevity, and system-level control strategies.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101584"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080387","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}
Pub Date : 2026-05-01Epub Date: 2026-01-23DOI: 10.1016/j.ecmx.2026.101612
Majid J. Almheiri , Haris M. Khalid , Abdulla Ismail , Asif Gulraiz , Zafar Said
Data centers face new cooling challenges due to the growth of digital infrastructure as well the rise in computing needs. This makes it urgent to find more sustainable ways to manage heat. To address this challenge, this proposed study surveys the chilled water-based hybrid cooling systems as a practical way to meet higher cooling demands while being environmentally responsible. To achieve this, the proposed research uses several methods: 1) it reviews cooling basics, 2) looks at recent technology, 3) studies control systems, and 4) examines real-world case studies. By reviewing current literature and industry examples, the proposed study highlights key performance and sustainability measures that could show the benefits of this choice towards hybrid cooling. Results show that combining water and air cooling with smart sensors, the Internet of Things (IoT), and artificial intelligence (AI) control can greatly improve energy efficiency. This would also make operations more resilient to climate change. The utilization of advanced chillers (chilled water-based hybrid cooling), heat exchangers, and phase-change materials helps transfer heat more efficiently, while using renewable energy can lower carbon emissions. Though there are challenges, such as saving water and working with older systems, the long-term savings and environmental gains make these hybrid systems more important for sustainable data centers. This proposed paper also offers useful insights for industry professionals who are working to adopt greener cooling solutions while keeping data centers reliable and high-performing.
{"title":"Chilled water-based hybrid cooling solution for data centers: A comprehensive survey of technologies, developments, and regenerative energy transitions","authors":"Majid J. Almheiri , Haris M. Khalid , Abdulla Ismail , Asif Gulraiz , Zafar Said","doi":"10.1016/j.ecmx.2026.101612","DOIUrl":"10.1016/j.ecmx.2026.101612","url":null,"abstract":"<div><div>Data centers face new cooling challenges due to the growth of digital infrastructure as well the rise in computing needs. This makes it urgent to find more sustainable ways to manage heat. To address this challenge, this proposed study surveys the chilled water-based hybrid cooling systems as a practical way to meet higher cooling demands while being environmentally responsible. To achieve this, the proposed research uses several methods: 1) it reviews cooling basics, 2) looks at recent technology, 3) studies control systems, and 4) examines real-world case studies. By reviewing current literature and industry examples, the proposed study highlights key performance and sustainability measures that could show the benefits of this choice towards hybrid cooling. Results show that combining water and air cooling with smart sensors, the Internet of Things (IoT), and artificial intelligence (AI) control can greatly improve energy efficiency. This would also make operations more resilient to climate change. The utilization of advanced chillers (chilled water-based hybrid cooling), heat exchangers, and phase-change materials helps transfer heat more efficiently, while using renewable energy can lower carbon emissions. Though there are challenges, such as saving water and working with older systems, the long-term savings and environmental gains make these hybrid systems more important for sustainable data centers. This proposed paper also offers useful insights for industry professionals who are working to adopt greener cooling solutions while keeping data centers reliable and high-performing.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101612"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189893","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}
Pub Date : 2026-05-01Epub Date: 2026-02-02DOI: 10.1016/j.ecmx.2026.101631
Arman Ashabi , Mohamed Mostafa , Andriy Hryshchenko , Ken Bruton , Dominic T.J. O’sullivan
Transitioning industrial processes towards sustainability necessitates robust decision-support solutions. This paper introduces FlexiHeat-DST, a cutting-edge web-based tool dedicated to power-to-heat applications. The platform comprises three core functionalities: technology selection, multi-criteria decision analysis, and scenario exploration, offering an integrated approach to identifying optimal electrification pathways. It incorporates eight techno-economic and environmental parameters within a normalization framework to ensure fair comparisons, while an intuitive dashboard allows stakeholders to readily customize applications, adjust parameters, and dynamically visualize outcomes. A practical validation in the steel industry compared four electric-based alternatives for replacing fossil-fuel-intensive furnaces. The results indicated Induction and Resistance furnaces as top performers, scoring 0.669 and 0.578, followed by Plasma and Electric Arc furnaces, which scored 0.500 and 0.367, respectively. Scenario analyses revealed that while Induction and Resistance furnaces excelled under criteria such as technology maturity and installation simplicity, Electric Arc and Plasma furnaces were superior in scenarios prioritising efficiency and decarbonisation. In addition, sensitivity analyses recognised strong correlations between electrification stages, installation complexity, and overall technology rankings. Also, technologies superior in efficiency and carbon reduction typically incurred higher expenditures, adversely affecting their overall scores due to lower ratings in other critical areas such as Technology Readiness Level and lifespan. Finally, the analysis of energy costs and carbon emissions indicated that, despite significant environmental advantages, electrification remains economically challenging under prevailing electricity pricing structures. This underscores the necessity for strategic policy shifts and comprehensive decarbonisation plans within industrial sectors to achieve economically viable and efficient heat decarbonisation.
{"title":"Design and utilisation of a multi-criteria decision support tool to analyse power-to-heat technologies for advancing industrial electrification","authors":"Arman Ashabi , Mohamed Mostafa , Andriy Hryshchenko , Ken Bruton , Dominic T.J. O’sullivan","doi":"10.1016/j.ecmx.2026.101631","DOIUrl":"10.1016/j.ecmx.2026.101631","url":null,"abstract":"<div><div>Transitioning industrial processes towards sustainability necessitates robust decision-support solutions. This paper introduces FlexiHeat-DST, a cutting-edge web-based tool dedicated to power-to-heat applications. The platform comprises three core functionalities: technology selection, multi-criteria decision analysis, and scenario exploration, offering an integrated approach to identifying optimal electrification pathways. It incorporates eight techno-economic and environmental parameters within a normalization framework to ensure fair comparisons, while an intuitive dashboard allows stakeholders to readily customize applications, adjust parameters, and dynamically visualize outcomes. A practical validation in the steel industry compared four electric-based alternatives for replacing fossil-fuel-intensive furnaces. The results indicated Induction and Resistance furnaces as top performers, scoring 0.669 and 0.578, followed by Plasma and Electric Arc furnaces, which scored 0.500 and 0.367, respectively. Scenario analyses revealed that while Induction and Resistance furnaces excelled under criteria such as technology maturity and installation simplicity, Electric Arc and Plasma furnaces were superior in scenarios prioritising efficiency and decarbonisation. In addition, sensitivity analyses recognised strong correlations between electrification stages, installation complexity, and overall technology rankings. Also, technologies superior in efficiency and carbon reduction typically incurred higher expenditures, adversely affecting their overall scores due to lower ratings in other critical areas such as Technology Readiness Level and lifespan. Finally, the analysis of energy costs and carbon emissions indicated that, despite significant environmental advantages, electrification remains economically challenging under prevailing electricity pricing structures. This underscores the necessity for strategic policy shifts and comprehensive decarbonisation plans within industrial sectors to achieve economically viable and efficient heat decarbonisation.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101631"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189894","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}
Pub Date : 2026-05-01Epub Date: 2026-01-28DOI: 10.1016/j.ecmx.2026.101624
Peter A. Fokker , Eloisa Salina Borello , Francesca Verga , Dario Viberti
Well testing and conventional Pressure Transient Analysis (PTA) are fundamental and well-established methodologies for characterizing well and reservoir parameters. However, the applicability of PTA is limited during production or injection operations, since it requires a shut-in of the tested well, and it is significantly affected by interferences from neighboring wells.
In previous works, we proposed, implemented, and validated against real data a methodology called Harmonic Pulse Testing (HPT). HPT is complementary to PTA. By specifically deploying the periodicity of rate and pressure signals, it has been designed to be applied during ongoing field operations.
In this work, we present a new analytical solution for HPT in naturally fractured reservoirs. The proposed solution is also applied to geothermal systems, as it is coupled with a radial composite model capable of approximating the thermal front. The model has been validated against well-established analytical and numerical models under different scenarios. The calculation steps for converting the numerical dual-porosity model into storativity ratio and inter-porosity flow coefficient are also provided.
The results of a validation exercise demonstrate that our model is robust against potential interference from other wells and allows the detection of the thermal front. The methodology can therefore be successfully applied during ongoing operations in naturally fractured geothermal reservoirs.
{"title":"Dual-porosity model for harmonic pulse testing in fractured geothermal reservoir","authors":"Peter A. Fokker , Eloisa Salina Borello , Francesca Verga , Dario Viberti","doi":"10.1016/j.ecmx.2026.101624","DOIUrl":"10.1016/j.ecmx.2026.101624","url":null,"abstract":"<div><div>Well testing and conventional Pressure Transient Analysis (PTA) are fundamental and well-established methodologies for characterizing well and reservoir parameters. However, the applicability of PTA is limited during production or injection operations, since it requires a shut-in of the tested well, and it is significantly affected by interferences from neighboring wells.</div><div>In previous works, we proposed, implemented, and validated against real data a methodology called Harmonic Pulse Testing (HPT). HPT is complementary to PTA. By specifically deploying the periodicity of rate and pressure signals, it has been designed to be applied during ongoing field operations.</div><div>In this work, we present a new analytical solution for HPT in naturally fractured reservoirs. The proposed solution is also applied to geothermal systems, as it is coupled with a radial composite model capable of approximating the thermal front. The model has been validated against well-established analytical and numerical models under different scenarios. The calculation steps for converting the numerical dual-porosity model into storativity ratio and inter-porosity flow coefficient are also provided.</div><div>The results of a validation exercise demonstrate that our model is robust against potential interference from other wells and allows the detection of the thermal front. The methodology can therefore be successfully applied during ongoing operations in naturally fractured geothermal reservoirs.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101624"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080232","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}
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