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Two objective optimization in shell-and-tube heat exchangers using genetic algorithm

机译:基于遗传算法的管壳式换热器的两个目标优化

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In this research, optimization of shell-and-tube heat exchangers is performed for two objectives. These objectives are an increment in heat transfer rate and a decrement in the total cost for a certain fluids with certain mass flow rates and specified inlet temperatures. Feasible and standard ranges for geometries, standard ranges of velocities and a maximum pressure drop constraint in both shell and tube sides are considered in the optimization process. Eleven optimization variables are considered. The relation between the objective functions and optimization variables has many complexities. A genetic algorithm is used to statistically approach the objective functions. With a genetic algorithm, the probability of getting trapped in a local optimum is very little. In this research for two sample studies, both increase in heat transfer rate and decrease in the total cost relative to available results have been obtained. The corresponding optimized values of variables for each case study have been reported. The data proposed in this study are practical suggestions for the construction of heat exchangers.
机译:在这项研究中,为实现两个目标而对管壳式换热器进行了优化。这些目标是,对于具有一定质量流量和特定入口温度的某些流体,要提高传热速率,并要降低总成本。在优化过程中,考虑了几何形状的可行范围和标准范围,速度的标准范围以及壳体和管体两侧的最大压降约束。考虑了11个优化变量。目标函数和优化变量之间的关系有很多复杂性。遗传算法用于统计地接近目标函数。使用遗传算法,陷入局部最优的可能性很小。在这项针对两个样本研究的研究中,相对于可获得的结果,既提高了传热速度,又降低了总成本。已经报告了每个案例研究的相应变量优化值。这项研究中提出的数据是建造热交换器的实用建议。

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