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Techno-economic optimization of a shell and tube heat exchanger by genetic and particle swarm algorithms

机译：基于遗传和粒子群算法的管壳式换热器技术经济优化

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The use of genetic and particle swarm algorithms in the design of techno-economically optimum shell-and-tube heat exchangers is demonstrated. A cost function (including costs of the heat exchanger based on surface area and power consumption to overcome pressure drops) is the objective function, which is to be minimized. Selected decision variables include tube diameter, central baffles spacing and shell diameter. The Delaware method is used to calculate the heat transfer coefficient and the shell-side pressure drop. The accuracy and efficiency of the suggested algorithm and the Delaware method are investigated. A comparison of the results obtained by the two algorithms shows that results obtained with the particle swarm optimization method are superior to those obtained with the genetic algorithm method. By comparing these results with those from various references employing the Kern method and other algorithms, it is shown that the Delaware method accompanied by genetic and particle swarm algorithms achieves more optimum results, based on assessments for two case studies. (C) 2015 Elsevier Ltd. All rights reserved.
机译：证明了遗传和粒子群算法在设计技术经济上最理想的管壳式换热器中的应用。成本函数（包括基于表面积的热交换器成本和克服压降的功耗）是目标函数，应将其最小化。选择的决策变量包括管直径，中央挡板间距和壳体直径。特拉华方法用于计算传热系数和壳侧压降。研究了所提算法和特拉华方法的准确性和效率。两种算法获得的结果的比较表明，使用粒子群优化方法获得的结果优于使用遗传算法方法获得的结果。通过将这些结果与采用Kern方法和其他算法的各种参考文献的结果进行比较，可以得出结论，基于对两个案例研究的评估，结合了遗传算法和粒子群算法的特拉华方法获得了更好的结果。 （C）2015 Elsevier Ltd.保留所有权利。

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