Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique
Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique
复制标题
DOI：
10.1016/j.applthermaleng.2010.03.001
复制
发表时间：
2010-08
影响因子：
6.4
通讯作者：
V. Patel;R. Rao
中科院分区：
文献类型：
--
作者：
V. Patel;R. Rao
管壳式热交换器（STHEs）是最常见的热交换器类型，在许多工业应用中广泛使用。这些热交换器的成本最小化是设计师和用户的关键目标。换热器的设计是一个复杂的过程，包括几何参数和工作参数的选择。管壳式换热器的传统设计方法包括对大量不同的换热器几何形状进行评级，以确定那些满足给定热负荷和一组几何和操作约束的换热器。然而，这种方法很耗时，并且不能保证最佳解决方案。因此，本研究探索了非传统优化技术的使用；粒子群算法（PSO），从经济角度对管壳式换热器进行优化设计。年总成本的最小化被认为是一个目标函数。考虑了壳体内径、外管径和挡板间距三个设计变量进行优化。还考虑了三角形和方形两种管形布局的优化。给出了四个不同的案例研究，以证明所提出算法的有效性和准确性。将粒子群优化算法与遗传算法的优化结果进行了比较。
Shell-and-tube heat exchangers (STHEs) are the most common type of heat exchangers that find widespread use in numerous industrial applications. Cost minimization of these heat exchangers is a key objective for both designer and users. Heat exchanger design involves complex processes, including selection of geometrical parameters and operating parameters. The traditional design approach for shell-and-tube heat exchangers involves rating a large number of different exchanger geometries to identify those that satisfy a given heat duty and a set of geometric and operational constraints. However, this approach is time-consuming and does not assure an optimal solution. Hence the present study explores the use of a non-traditional optimization technique; called particle swarm optimization (PSO), for design optimization of shell-and-tube heat exchangers from economic view point. Minimization of total annual cost is considered as an objective function. Three design variables such as shell internal diameter, outer tube diameter and baffle spacing are considered for optimization. Two tube layouts viz. triangle and square are also considered for optimization. Four different case studies are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO technique are compared with those obtained by using genetic algorithm (GA).