Two-bed adsorption refrigeration cycle integration into a hybrid biomass-gasification multigeneration system for sustainable energy production: comprehensive 4E analysis, and machine learning optimization

IF 6.9 2区工程技术 Q2 ENERGY & FUELS

Applied Thermal Engineering Pub Date : 2025-10-02 DOI:10.1016/j.applthermaleng.2025.128615

Ming Fu , Ali Basem , Sarminah Samad , Dyana Aziz Bayz , Saleh Alhumaid , Ashit Kumar Dutta , H. Elhosiny Ali , Zuhair Jastaneyah , Salem Alkhalaf , Ibrahim Mahariq

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引用次数: 0

Abstract

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.

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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

本文介绍了一种集成生物质驱动的多代能源系统，该系统结合了先进的热力学循环和两床吸附制冷循环（ARC），用于高效制冷、加热、发电和制氢。该研究的关键新颖之处在于首次将双床ARC集成到生物质驱动系统中，以实现同时多输出生成，并辅以结合人工神经网络和遗传算法的新型计算框架，以实现高效的多目标优化。对系统的性能进行了详细的分析，重点是火用破坏、成本率和各种系统输出。对气化炉、PEME和ARC等关键子系统的能源效率和经济可行性进行了评估。气化炉子系统表现出最高的火用破坏，达到4322.24 kW，成本率为20.47美元/小时。电力循环承担着重要的能量转换，其成本最高，为260.49美元/小时，火用损耗为18448.60千瓦。相比之下，PEME装置的火用损耗相对较低，为440.26 kW，成本率为23.91美元/小时。参数研究表明，水分含量的增加减少了产氢量和热负荷，同时提高了制冷量。相比之下，较高的气化炉温度和优化的生物质流量增加了制氢和热负荷。在此基础上，采用人工神经网络（ANN）和遗传算法（GA）相结合的多目标优化框架，最大限度地提高能源效率，最大限度地降低平均总排放量（LTE），降低总成本。优化得到了一组pareto最优解，其中最优折衷方案的火用效率为64.57%，总成本率为43.90美元/小时，LTE为1.21 Ton/GJ。

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来源期刊

Applied Thermal Engineering 工程技术-工程：机械

CiteScore

11.30

自引率

15.60%

发文量

1474

审稿时长

57 days

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.