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Employing static and dynamic optimization approaches on a desiccant-enhanced indirect evaporative cooling system

机译：在干燥剂增强的间接蒸发冷却系统上采用静态和动态优化方法

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摘要

The best possible condition for employing desiccant-enhanced indirect evaporative cooling system (DEVap) as efficient as possible in four cities of Iran is found. To achieve this purpose, both static and dynamic optimization approaches are employed for design as well as retrofit stages, using the combination of non-dominated sorting genetic algorithm II (NSGA-II) and TOPSIS method as the ordering technique by similarity to ideal solution. As a result, four optimization scenarios are defined. Through a comprehensive comparative study among the optimum results, the best optimization strategy for each city is suggested. Both dimensional characteristics and operational parameters of the cooler are considered as decision variables while necessary technical and geometrical constraints such as thermal comfort condition are imposed. In addition, life-cycle cost (LCC), annual average coefficient of performance (ACOP), annual water consumption (AWC), and annual carbon dioxide emission (ACE) are optimized as the objective functions. It is found that for all the four investigated cities and both design and retrofit stages, dynamic optimization approach offers better results. Compared to the base condition, which is the static retrofit optimization, dynamic retrofit optimization results in 23.7, 50.5, 153.2, and 57.8% improvement in LCC, ACOP, AWC, and ACE, respectively. The average enhancement of the four mentioned objective functions for dynamic design optimization are 34.6, 60.1, 390.0, and 69.9%, respectively.
机译：找到了在伊朗的四个城市中尽可能有效地使用增强干燥剂的间接蒸发冷却系统（DEVap）的最佳条件。为了达到这个目的，静态和动态优化方法都被用于设计和改造阶段，结合了非理想排序遗传算法II（NSGA-II）和TOPSIS方法作为排序技术，类似于理想解决方案。结果，定义了四个优化方案。通过对最佳结果的综合比较研究，提出了每个城市的最佳优化策略。冷却器的尺寸特性和运行参数均被视为决策变量，同时施加了必要的技术和几何约束（如热舒适条件）。此外，优化了生命周期成本（LCC），年平均性能系数（ACOP），年耗水量（AWC）和年二氧化碳排放量（ACE）作为目标函数。结果发现，对于所有四个被调查城市以及设计和改造阶段，动态优化方法都能提供更好的结果。与基本条件（即静态改造优化）相比，动态改造优化使LCC，ACOP，AWC和ACE分别提高了23.7、50.5、153.2和57.8％。用于动态设计优化的四个提到的目标函数的平均增强分别为34.6％，60.1％，390.0和69.9％。

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