Ming Fu , Ali Basem , Sarminah Samad , Dyana Aziz Bayz , Saleh Alhumaid , Ashit Kumar Dutta , H. Elhosiny Ali , Zuhair Jastaneyah , Salem Alkhalaf , Ibrahim Mahariq
{"title":"将两床吸附式制冷循环集成到混合生物质气化多代系统中,用于可持续能源生产:综合4E分析和机器学习优化","authors":"Ming Fu , Ali Basem , Sarminah Samad , Dyana Aziz Bayz , Saleh Alhumaid , Ashit Kumar Dutta , H. Elhosiny Ali , Zuhair Jastaneyah , Salem Alkhalaf , Ibrahim Mahariq","doi":"10.1016/j.applthermaleng.2025.128615","DOIUrl":null,"url":null,"abstract":"<div><div>This paper presents an integrated biomass-driven multigeneration energy system incorporating advanced thermodynamic cycles and a two-bed adsorption refrigeration cycle (ARC) for efficient cooling, heating, power generation, and hydrogen production. The key novelty of this study is the first-time integration of a dual-bed ARC into a biomass-driven system for simultaneous multi-output generation, complemented by a novel computational framework combining artificial neural networks and genetic algorithms for efficient multi-objective optimization. A detailed analysis of the system’s performance was conducted, focusing on exergy destruction, cost rates, and various system outputs. Key subsystems, including the gasifier, PEME, and ARC, were evaluated for their exergy efficiency and economic viability. The gasifier subsystem exhibited the highest exergy destruction, amounting to 4322.24 kW, with a cost rate of 20.47 $/h. The power cycle, responsible for significant energy conversion, incurred the highest cost of 260.49 $/h with an exergy destruction of 18,448.60 kW. In comparison, the PEME unit demonstrated a relatively low exergy destruction of 440.26 kW and a cost rate of 23.91 $/h. Parametric studies revealed that the increased moisture content reduced hydrogen production and heating load, while raising the cooling capacity. In contrast, higher gasifier temperatures and optimized biomass flow rates enhanced hydrogen generation and heating load. Furthermore, a multi-objective optimization framework, combining artificial neural networks (ANN) with genetic algorithms (GA), was applied to maximize exergy efficiency, minimize levelized total emission (LTE), and reduce total cost. The optimization revealed a set of Pareto-optimal solutions, with the best compromise achieving an exergy efficiency of 64.57 %, a total cost rate of 43.90 $/h, and an LTE of 1.21 Ton/GJ.</div></div>","PeriodicalId":8201,"journal":{"name":"Applied Thermal Engineering","volume":"281 ","pages":"Article 128615"},"PeriodicalIF":6.9000,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Two-bed adsorption refrigeration cycle integration into a hybrid biomass-gasification multigeneration system for sustainable energy production: comprehensive 4E analysis, and machine learning optimization\",\"authors\":\"Ming Fu , Ali Basem , Sarminah Samad , Dyana Aziz Bayz , Saleh Alhumaid , Ashit Kumar Dutta , H. Elhosiny Ali , Zuhair Jastaneyah , Salem Alkhalaf , Ibrahim Mahariq\",\"doi\":\"10.1016/j.applthermaleng.2025.128615\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper presents an integrated biomass-driven multigeneration energy system incorporating advanced thermodynamic cycles and a two-bed adsorption refrigeration cycle (ARC) for efficient cooling, heating, power generation, and hydrogen production. The key novelty of this study is the first-time integration of a dual-bed ARC into a biomass-driven system for simultaneous multi-output generation, complemented by a novel computational framework combining artificial neural networks and genetic algorithms for efficient multi-objective optimization. A detailed analysis of the system’s performance was conducted, focusing on exergy destruction, cost rates, and various system outputs. Key subsystems, including the gasifier, PEME, and ARC, were evaluated for their exergy efficiency and economic viability. The gasifier subsystem exhibited the highest exergy destruction, amounting to 4322.24 kW, with a cost rate of 20.47 $/h. The power cycle, responsible for significant energy conversion, incurred the highest cost of 260.49 $/h with an exergy destruction of 18,448.60 kW. In comparison, the PEME unit demonstrated a relatively low exergy destruction of 440.26 kW and a cost rate of 23.91 $/h. Parametric studies revealed that the increased moisture content reduced hydrogen production and heating load, while raising the cooling capacity. In contrast, higher gasifier temperatures and optimized biomass flow rates enhanced hydrogen generation and heating load. Furthermore, a multi-objective optimization framework, combining artificial neural networks (ANN) with genetic algorithms (GA), was applied to maximize exergy efficiency, minimize levelized total emission (LTE), and reduce total cost. The optimization revealed a set of Pareto-optimal solutions, with the best compromise achieving an exergy efficiency of 64.57 %, a total cost rate of 43.90 $/h, and an LTE of 1.21 Ton/GJ.</div></div>\",\"PeriodicalId\":8201,\"journal\":{\"name\":\"Applied Thermal Engineering\",\"volume\":\"281 \",\"pages\":\"Article 128615\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Thermal Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1359431125032077\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Thermal Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359431125032077","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Two-bed adsorption refrigeration cycle integration into a hybrid biomass-gasification multigeneration system for sustainable energy production: comprehensive 4E analysis, and machine learning optimization
This paper presents an integrated biomass-driven multigeneration energy system incorporating advanced thermodynamic cycles and a two-bed adsorption refrigeration cycle (ARC) for efficient cooling, heating, power generation, and hydrogen production. The key novelty of this study is the first-time integration of a dual-bed ARC into a biomass-driven system for simultaneous multi-output generation, complemented by a novel computational framework combining artificial neural networks and genetic algorithms for efficient multi-objective optimization. A detailed analysis of the system’s performance was conducted, focusing on exergy destruction, cost rates, and various system outputs. Key subsystems, including the gasifier, PEME, and ARC, were evaluated for their exergy efficiency and economic viability. The gasifier subsystem exhibited the highest exergy destruction, amounting to 4322.24 kW, with a cost rate of 20.47 $/h. The power cycle, responsible for significant energy conversion, incurred the highest cost of 260.49 $/h with an exergy destruction of 18,448.60 kW. In comparison, the PEME unit demonstrated a relatively low exergy destruction of 440.26 kW and a cost rate of 23.91 $/h. Parametric studies revealed that the increased moisture content reduced hydrogen production and heating load, while raising the cooling capacity. In contrast, higher gasifier temperatures and optimized biomass flow rates enhanced hydrogen generation and heating load. Furthermore, a multi-objective optimization framework, combining artificial neural networks (ANN) with genetic algorithms (GA), was applied to maximize exergy efficiency, minimize levelized total emission (LTE), and reduce total cost. The optimization revealed a set of Pareto-optimal solutions, with the best compromise achieving an exergy efficiency of 64.57 %, a total cost rate of 43.90 $/h, and an LTE of 1.21 Ton/GJ.
期刊介绍:
Applied Thermal Engineering disseminates novel research related to the design, development and demonstration of components, devices, equipment, technologies and systems involving thermal processes for the production, storage, utilization and conservation of energy, with a focus on engineering application.
The journal publishes high-quality and high-impact Original Research Articles, Review Articles, Short Communications and Letters to the Editor on cutting-edge innovations in research, and recent advances or issues of interest to the thermal engineering community.