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Modeling and thermo-economic optimization of a new multi-generation system with geothermal heat source and LNG heat sink

机译:具有地热热源和LNG散热器的新型多代系统的建模和热经济优化

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摘要

LNG re-gasification process usually occurs through heat exchange with seawater, causing a large amount of exergy destruction. However, in this study, this process is employed as the heat sink of a geothermal driven multi-generation system. Because of the high temperature difference between the heat source (geothermal fluid) and the heat sink (LNG re-gasification process), a cascade of two organic Rankine cycles is placed between them. The system also includes an absorption refrigeration cycle and a PEM electrolyzer to form an efficient multi generation system. A comprehensive analysis is carried out to assess the performance of the system both thermodynamically and economically. Furthermore, the paper presents a parametric study to illustrate the influences of major parameters on the system performance. To simultaneously optimize total cost rate, hydrogen production capacity, and exergy efficiency of the system, a multi-objective optimization procedure is developed (through coupling Genetic Algorithm with Artificial Neural Network) and applied to the system. Consequently, a system design with a total cost rate of 423.5 ($/hr), hydrogen production capacity of 276.1 (kg/hr), and exergy efficiency of 24.92% is obtained as the optimal solution. The proposed system shows great improvements in cooling, power generation, and hydrogen production capacities compared to literature.
机译:LNG再气化过程通常是通过与海水进行热交换而发生的,从而导致大量的火用气破坏。但是,在这项研究中,此过程被用作地热驱动多代系统的散热器。由于热源(地热流体)和散热器(LNG再气化过程)之间存在很高的温差,因此在它们之间放置了两个有机兰金循环的级联。该系统还包括吸收式制冷循环和PEM电解槽,以形成高效的多代系统。进行了全面的分析,以从热力学和经济角度评估系统的性能。此外,本文提出了一项参数研究,以说明主要参数对系统性能的影响。为了同时优化系统的总成本率,制氢能力和火用效率,开发了多目标优化程序(通过将遗传算法与人工神经网络耦合)并应用于系统。因此,作为最佳解决方案,系统设计总成本率为423.5($ / hr),制氢能力为276.1(kg / hr),火用效率为24.92%。与文献相比,拟议的系统在冷却,发电和制氢能力方面显示出极大的改进。

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