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A Hybrid Metaheuristic–Deterministic Optimization Strategy for Waste Heat Recovery in Industrial Plants
Industrial & Engineering Chemistry Research ( IF 4.2 ) Pub Date : 2021-02-28 , DOI: 10.1021/acs.iecr.0c06201
Francisco Javier López-Flores 1 , Luis Germán Hernández-Pérez 1 , Luis F. Lira-Barragán 1 , Eusiel Rubio-Castro 2 , José M. Ponce-Ortega 1
Affiliation  

This work presents a novel approach to recover industrial waste heat and to integrate it into utilities, refrigeration, and electricity production through the incorporation of heat exchanger networks and thermal engines (steam Rankine cycle, organic Rankine cycle, and absorption refrigeration cycle). The solution approach is based on the iteration between metaheuristic–deterministic optimization strategies. Metaheuristic optimization is established through the MS Excel-VBA-Aspen Plus link to obtain accurate modeling results. Deterministic optimization is implemented in the GAMS platform, where the mathematical formulation is based on a superstructure that considers all of the energy interconnections between the heat exchanger network, utilities, and thermal engines. Furthermore, economic, environmental, and social targets are evaluated. A case study is presented to show the applicability of the proposed methodology. The operating conditions obtained are presented (working fluid flow rate, temperatures, pressures, and efficiencies) for each thermal engine. Furthermore, the results show an increase in the total annual profit by 148%.

中文翻译:

工业工厂余热回收的混合元启发式-确定性优化策略

这项工作提出了一种新颖的方法,可通过结合热交换器网络和热力发动机(蒸汽朗肯循环,有机朗肯循环和吸收式制冷循环)来回收工业废热并将其整合到公用事业,制冷和电力生产中。解决方案方法基于元启发式确定性优化策略之间的迭代。通过MS Excel-VBA-Aspen Plus链接建立元启发式优化,以获得准确的建模结果。确定性优化是在GAMS平台中实现的,该平台的数学公式基于上层结构,该上层结构考虑了热交换器网络,公用事业和热力发动机之间的所有能源互连。此外,还评估了经济,环境和社会目标。案例研究表明了所提出方法的适用性。列出了每个热力发动机获得的运行条件(工作流体的流量,温度,压力和效率)。此外,结果显示,年度总利润增加了148%。
更新日期:2021-03-10
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