Energy Conversion and Management-X最新文献

{"title":"Data-driven prediction of pollutants emission from small-scale heating units using temporal deep learning","authors":"Katarzyna Szramowiat-Sala ,&nbsp;Kamil Krpec ,&nbsp;Roch Penkala ,&nbsp;Jiří Ryšavý","doi":"10.1016/j.ecmx.2025.101322","DOIUrl":"10.1016/j.ecmx.2025.101322","url":null,"abstract":"<div><div>Artificial intelligence (AI), particularly its subfield of machine learning (ML), has gained increasing attention in the field of environmental modelling and energy systems. These data-driven techniques offer robust tools for handling high-dimensional, nonlinear, and noisy datasets that are common in combustion diagnostics and emission prediction. This study investigates the use of advanced machine learning models for predicting flue gas emissions from residential heating systems under real-world operating conditions. Three types of solid-fuel boilers – automatic pellet, down-draught lignite, and gasification with hard coal – were analyzed using time-series data collected during full combustion cycles. Emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), sulfur dioxide (SO₂), and organic gaseous compounds (OGC) were modelled using two deep learning approaches: a neural network with long short-term memory (NN-LSTM) and a hybrid convolutional LSTM (CNN-LSTM). In addition, Random Forest analysis was applied to identify the most influential operational parameters driving emission formation.</div><div>The results show that CO₂ emissions are predicted most reliably, especially in the gasification boiler using NN-LSTM (R² = 0.72). CNN-LSTM outperforms NN-LSTM in predicting CO and OGC in boilers with high variability, such as the down-draught system. However, both models face limitations when modelling NO_x and SO₂, suggesting the need for additional variables or physics-informed modelling. Feature importance analysis confirms oxygen concentration, flue gas temperature, and boiler heat output as key emission predictors.</div><div>The findings demonstrate the feasibility of applying AI-based models for real-time emission estimation and optimization of small-scale combustion systems. This study provides a realistic baseline for future integration of predictive emission models with adaptive boiler control systems in residential energy applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101322"},"PeriodicalIF":7.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of biomass and biofuels and their progress using digital technologies","authors":"Rahbaar Yeassin ,&nbsp;Md Minhazur Rashid Adnan ,&nbsp;Mohammed Musfique Ahmed Chowdhury ,&nbsp;Arif Mia ,&nbsp;Md Aseer Jawad Chowdhury Enan ,&nbsp;Mahamudul Hassan Fuad","doi":"10.1016/j.ecmx.2025.101254","DOIUrl":"10.1016/j.ecmx.2025.101254","url":null,"abstract":"<div><div>This review provides a critical, end-to-end synthesis of how digital technologies are reshaping the biomass and biofuel value chain—from feedstock sourcing to end use. We examine the roles of artificial intelligence and machine learning, the Internet of Things, blockchain, and plant-scale digital twins in optimizing supply chains, catalysis, and conversion systems. Evidence indicates that AI-driven predictive models can raise biofuel yields by 15–25%, while IoT-enabled, real-time monitoring cuts operational waste and greenhouse-gas emissions by up to 40%. Beyond process efficiency, we show how digital tools enable rigorous life-cycle assessment, support environmental and health risk management, and expand socioeconomic opportunities, including improved market access for smallholders and demand for skilled jobs. Policy dimensions are addressed through digital frameworks for traceability, transparency, and compliance with evolving regulations. A distinctive feature is our focus on fourth-generation biofuels and the synergistic potential of synthetic biology with AI. We conclude with a practical implementation roadmap that integrates technical, organizational, and policy actions; identifies persistent challenges in data quality, interoperability, cybersecurity, skills, and governance; and outlines research priorities in hybrid physics–ML models, open standards, trustworthy MRV, and equitable deployment. The result is a concise reference for researchers, industry, and policymakers seeking to accelerate a sustainable, low-carbon bioeconomy aligned with global climate goals.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101254"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220565","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":"Thermo-economic assessment of hybrid solar-driven supercritical CO2 cycle for multi-generation system","authors":"Md. Rasel Ahmed ,&nbsp;Md. Rokanuzzaman ,&nbsp;Md. Abdul Aziz ,&nbsp;Utpol K. Paul ,&nbsp;Md. Rabiul Islam Sarker ,&nbsp;Barun K. Das","doi":"10.1016/j.ecmx.2025.101290","DOIUrl":"10.1016/j.ecmx.2025.101290","url":null,"abstract":"<div><div>Concentrated solar power (CSP) connected to a thermodynamic power cycle is used to convert solar energy into electricity. This study investigates a combination of two power generation cycles, where the topping cycle utilizes supercritical carbon dioxide (sCO₂), and the organic Rankine cycle (ORC) serves as the bottoming cycle. The diesel engine exhaust is utilized as a backup source when solar energy is not available. An additional hydrogen production unit is connected to the system for producing hydrogen through the steam methane reforming (SMR) method process and operated when available heat is obtained from the hybrid system. This experiment was carried out in Rangpur, Bangladesh. Due to a higher coefficient of performance, Therminol VP-1, CO₂, and R245fa were selected as working fluids in parabolic trough collectors (PTC), sCO₂, and ORC, respectively. The comparison of standalone sCO₂ with the combination of sCO₂-ORC has been conducted in terms of efficiency. The optimal results for the hybrid model have been found as a mass flow rate of 82 kg/s, net power output of 6062 kW, and specific investment cost (SIC) of 5237 $/kW at the pressure of 250 bar.<!--> <!-->The results also showed that the developed hybrid model achieved 68.5 % higher efficiency than standalone sCO₂. On the other hand, through the SMR method, 288 kg/h is found to be the ideal mass flow rate of methane, with a corresponding cost of $ 100.8/h, which produces hydrogen efficiently at a rate of 62.87 kg/h. The output of the hybrid model shows multiple outcomes, such as electricity and hydrogen, making this system a multi-generation system.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101290"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265610","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":"An efficient traffic acoustic energy harvester using optimized Helmholtz resonators for sustainable roadside power generation and smart monitoring","authors":"Pengfei Fan,&nbsp;Derong Wang,&nbsp;Yuli Zhang,&nbsp;Ruiyuan Jiang,&nbsp;Hankang Gu","doi":"10.1016/j.ecmx.2025.101289","DOIUrl":"10.1016/j.ecmx.2025.101289","url":null,"abstract":"<div><div>Current approaches to traffic noise management primarily focus on control strategies, yet traffic noise exhibits characteristics of continuous generation and widespread distribution. Therefore, it becomes highly meaningful to harvest and utilize acoustic energy while controlling traffic noise. Furthermore, analyzing the characteristics of energy generation and integrating artificial intelligence can enable monitoring of various road conditions. This paper presents an acoustic energy harvesting system based on a Helmholtz resonator incorporating a front reflector configuration for sustainable roadside power generation and intelligent traffic monitoring. We derived theoretical formulations for the resonant frequency characteristics of the front reflector-enhanced Helmholtz resonator and validated these predictions through comprehensive numerical simulations. The results demonstrate excellent agreement between theoretical predictions and simulation results across most geometric parameter changes. We characterized the frequency distribution of traffic noise and optimized the acoustic energy harvester design to match these spectral characteristics. The power generation performance was quantified and compared across different structural configurations, demonstrating the superior energy output capabilities of the proposed design. Experimental validation confirmed the system’s dual functionality in energy harvesting and noise mitigation under real-world traffic conditions. Subsequently, we implemented a Multi-Scale Convolutional Neural Network algorithm to classify vehicle speed ranges based on acoustic signatures, achieving an accuracy of 95.61% in distinguishing between different speed categories. This speed classification framework enables intelligent control of road monitoring equipment activation, allowing the system to operate only when speeding vehicles are detected while maintaining a low-power sleep mode during normal traffic conditions, thereby achieving significant energy conservation for intelligent transportation systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101289"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266250","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 Bayesian ensemble approach for improved sustainable aviation fuel modeling","authors":"Mohammed I. Radaideh ,&nbsp;Majdi I. Radaideh ,&nbsp;Angela Violi","doi":"10.1016/j.ecmx.2025.101287","DOIUrl":"10.1016/j.ecmx.2025.101287","url":null,"abstract":"<div><div>In this work, we introduce a new methodology to combine the available methods to predict the properties of complex hydrocarbon mixtures such as aviation fuels. Due to the complexity of aviation fuels, the available methods perform well individually on some of the experimental observations and vice versa on others when a surrogate aviation fuel is defined and used. To this end, we introduce a new ensemble model based on the existing methods that combine and weigh their predictions. We employ the probabilistic Bayesian approach to predict aviation fuel properties with confidence levels. This is necessary because the available experimental data for aviation fuels is generally limited, which leads to overfitting. We adopt both “interpretable” Bayesian regression and a more “black-box” approach to Bayesian neural networks. An ensemble of predictive methods provided better predictions than the individual methods with robust confidence levels for three properties considered: mass density, kinematic viscosity, and flash point. A significant reduction in the mean absolute percentage error was obtained for mass density predictions, from 1.25% to 0.57% and 0.42%, using the Bayesian linear regression (BLR) and Bayesian Neural Network (BNN), respectively. The error in kinematic viscosity predictions was reduced from 17.25% to 9.02% and 6.79% using BLR and BNN, respectively. The error in flash point predictions is reduced from 9.04% to 5.83% by BLR and to 5.51% by BNN. The importance of the methods in the ensemble did not fully follow their individual performance, where the accurate models may not be the most important. The ensemble approach allows for the inclusion of new methods, even if they are slightly less accurate. This methodology can be extended to predict other aviation fuel properties and incorporate any predictive model. It also offers a way to generate valid training data for generative Artificial Intelligence (AI) models, helping to address the scarcity of aviation fuel data.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101287"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266354","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":"Direct and indirect electrification in the German industry from a sector-coupled energy system modeling perspective","authors":"Markus Kaiser ,&nbsp;Charlotte Senkpiel ,&nbsp;Hans-Martin Henning ,&nbsp;Christoph Kost","doi":"10.1016/j.ecmx.2025.101305","DOIUrl":"10.1016/j.ecmx.2025.101305","url":null,"abstract":"<div><div>To achieve greenhouse gas (GHG) neutrality, a shift from fossil fuels to GHG-neutral energy sources is essential in the industrial sector. Direct and indirect electrification are the primary strategies for this fuel switch. This study uses four normative target scenarios to derive plausible ranges of electrification in the German industry, representing a highly industrialized European country. Scenario parameterization ranges from favoring direct to favoring indirect electrification. Here, a new perspective is added to existing literature: A sector-coupled energy system model is combined with consistent scenarios and increased technological resolution in the industrial sector to include 16 different processes and 17 process heat supplying technologies. The results show that direct electrification is essential in all scenarios, even for optimistic assumptions for synthetic energy carriers. By 2045, electricity accounts for 47–52% of industrial final energy consumption, including non-energy use. Still, gaseous and liquid energy carriers remain essential, accounting for 40–44%. To supply energy to the industrial sector in a GHG-neutral energy system, 43–46% of domestic variable renewable energy and 51–63% of domestic power-to-X capacity are required, in addition to synthetic imports and biogenic supply. The variation in electrification across scenarios is used to order different energy uses from all sectors: Trucks, cement production, and high-temperature process heat strongly depend on scenario assumptions and vary by more than 20%. Low-temperature process heat and space and water heating vary by 10–20%. Crude steel production, chemicals production, and light-duty vehicles vary by less than 5%.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101305"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220568","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":"Artificial intelligence and machine learning for smart grids: from foundational paradigms to emerging technologies with digital twin and large language model-driven intelligence","authors":"Yaser M. Banad,&nbsp;Sarah S. Sharif,&nbsp;Zahra Rezaei","doi":"10.1016/j.ecmx.2025.101329","DOIUrl":"10.1016/j.ecmx.2025.101329","url":null,"abstract":"<div><div>The evolution of modern power systems into smart grids is increasingly powered by Artificial Intelligence (AI) and Machine Learning (ML), which provide effective solutions for managing renewable intermittency, dynamic demand, and cybersecurity challenges. This paper presents a comprehensive review of AI/ML applications in smart grids, tracing their development from foundational paradigms to cutting-edge technologies such as Federated Learning (FL), Generative AI (GenAI), Large Language Models (LLMs), the Artificial Intelligence of Things (AIoT), and Digital Twin (DT)-driven intelligence. Enabling infrastructures, including IoT, 5G, edge–cloud ecosystems, and ML-based smart sensors, are discussed alongside advanced approaches such as multi-agent systems. Key applications explored include load forecasting, predictive maintenance, anomaly and cyber-attack detection, demand-side management, and electric vehicle integration. Special emphasis is placed on Digital Twin and LLM architectures, which enable real-time cyber-physical replicas and context-aware reasoning, thus improving predictive analytics, resilience, and autonomous decision-making. Despite notable advancements, challenges remain in interoperability, data privacy, computational scalability, adversarial robustness, and ethical constraints. By synthesizing these insights, the study highlights the transformative role of AI in creating resilient, sustainable, and intelligent energy systems, and outlines future research trajectories toward standardized DT frameworks, active learning paradigms, and LLM-driven energy intelligence.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101329"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing EV charging deployment in megacities: A Cairo case study using clustering and load analysis","authors":"M. Saber Eltohamy ,&nbsp;Ali M. El-Rifaie ,&nbsp;Fahmi elsayed ,&nbsp;M. Hassan Tawfiq ,&nbsp;M.M.R. Ahmed ,&nbsp;Hossam Youssef ,&nbsp;Ijaz Ahmed ,&nbsp;Amir Raouf","doi":"10.1016/j.ecmx.2025.101312","DOIUrl":"10.1016/j.ecmx.2025.101312","url":null,"abstract":"<div><div>The accelerating urbanization of Global South megacities presents considerable challenges to the equitable and technically efficient deployment of Electric vehicle charging infrastructure. This paper presents a data-driven planning framework applied to Cairo, integrating. K-Means spatial clustering, district-level demographic projections (2020–2025), and national electricity load analysis to optimize the siting of vehicle charging stations. A total of 85 public EV stations comprising 209 sockets were georeferenced and analyzed. Two novel indices were introduced to assess infrastructure equity: the socket per density index and the socket travel burden index. Results show that while central business districts are over-served, high-density residential areas such as Ain Shams and Dar Al Salam suffer from significant under-provision. Type 2 connectors dominate the network (77.5 %), leading to functional exclusion for users of CHAdeMO, CCS2, and GB/T vehicles. Vehicle-to-Grid simulation with 40 % vehicle charging participation, representing 5,078 vehicles, demonstrated a potential peak-load reduction of 25.4 MW without requiring additional infrastructure. The proposed framework offers a scalable and transferable model for equitable, resilient, and technically inclusive EV infrastructure planning in rapidly urbanizing regions of the Global South.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101312"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145265602","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 PMU in wide area monitoring system of transmission network using quasi-oppositional-based artificial rabbit optimization","authors":"Sourav Paul ,&nbsp;Sneha Sultana ,&nbsp;Susanta Dutta ,&nbsp;Provas Kumar Roy ,&nbsp;Sunanda Hazra ,&nbsp;Ghanshyam G. Tejani ,&nbsp;Seyed Jalaleddin Mousavirad","doi":"10.1016/j.ecmx.2025.101271","DOIUrl":"10.1016/j.ecmx.2025.101271","url":null,"abstract":"<div><div>Due to the massive volume of data generated by the PMU implementation in the current power system during the data collection process, the data transmission system becomes overburdened. The trade-off between installation cost, communication congestion, and full system observability makes it difficult to decide where PMUs should be placed in large-scale transmission networks. Additionally, the placement of PMUs in the optimal placement has a significant impact on both installation costs and traffic congestion. The wide area monitoring system (WAMS) is a practical solution for this data system congestion. Additionally, the incorporation and integration of the zero injection bus (ZIB) into the current system may allow for a further decrease in the number of PMUs necessary to achieve full system observability. To achieve perfect observability in the PMU placement problem, the researchers in this study developed a hybrid quasi oppositional-based artificial rabbit optimization. In order to survive, rabbits use detour foraging, random hiding, and energy shrinkage. Rabbits imitate other foragers while disregarding their own strategies. This tactic helps with exploration. The rabbits can choose a random burrow from among their own borrows to hide in, lowering the likelihood that the predator would locate and capture them. These tactics assist in exploitation. A balance between exploration and exploitation is finally maintained by the energy shrink. In the current work, the authors used these special techniques to examine total observability, WAMS data traffic, ZIB, and cost installation index in the PMU placement problem. On the IEEE 14-bus, IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus, the proposed techniques have been tested. In order to demonstrate the superiority of the suggested technique in the white scenario, the computed results were compared with other published studies. The outcomes of the suggested methods also show a faster convergence and speedier data scenario.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101271"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220056","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":"Analytical investigation of the compressor layout for off-design operation of a transcritical CO2 cycle in an electrothermal energy storage","authors":"J.M. Guisado ,&nbsp;A. Carro ,&nbsp;S. Unger ,&nbsp;Alexios-Spyridon Kyriakides ,&nbsp;Ioannis N. Tsimpanogiannis ,&nbsp;U. Hampel ,&nbsp;Simira Papadopoulou ,&nbsp;R. Chacartegui","doi":"10.1016/j.ecmx.2025.101299","DOIUrl":"10.1016/j.ecmx.2025.101299","url":null,"abstract":"<div><div>Storage technologies are critical for the massive integration of renewable sources. Among the most promising storage technologies are those based on Carnot batteries. This work analyses CO₂-based electrothermal energy storage coupled with geological storage (CEEGS) under off-design conditions when integrated into a fluctuating system such as a PV system. The integration of simulation models over an annual cycle provides valuable insights into the dynamic behavior of the system, supporting informed decision-making for future research on such innovative technologies. The developed model enables the optimisation of both design and operational parameters, tailored to specific applications and geographic locations. The valuable information obtained has shown a bottleneck in the compressor that leads to a strong effect on the overall performance of the storage system based on technological limits and stored energy losses. In this regard, this work proposes different pathways based on compressor layout that can improve the integration of storage systems in general and, in particular, the system under study when integrated into fluctuating systems. The multi-compressor operating mode leads to an improved efficiency of up to 18% and an average increase in daily operating range of 1.5 h based on improved compressor adjustment. The proposed split flow for identical compressor operating mode shows a daily operating range equal to the multi-compressor operating mode and a slight loss of efficiency with respect to the multi-compressor operating mode because the first adjusts better with constraining system fluctuations. However, the latter is a simpler configuration, and it is highly efficient, 16.3% more efficient than the basic compressor layout.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"28 ","pages":"Article 101299"},"PeriodicalIF":7.6,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220567","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}