Choosing the best evolutionary algorithm to optimize the multiobjective shell-and-tube heat exchanger design problem using PROMETHEE

General Pattern Search Applied to the Optimization of the Shell and Tube Heat Exchanger

“…The result obtained by the GPS was compared to the multiobjective optimization performed by the algorithm NSGA II and transformed to the minimum annual cost criterion used in [1]. The GPS algorithm was also compared to the result found for the genetic algorithm of MATLAB toolbox.…”

“…Three constraints was considered involve the limits of the shell side pressure drop, the tube side pressure drop, and the maximum área value of the heat exchanger. The formulation was the same as the one considered in [1,3,4].…”

“…The modeling and problem of the shell and tube heat exchange used in this article are the found in [1][2][3]. The variables used for the multiobjective optimization are presented in Table 1.…”

“….0032 m to 0.011 m Follow the description of the variables: ap is the tube layout pattern, np is the number of tubes passes, Lt is the tube length, esp is the tube wall thickness, do is the tube outer diameter, Lb is the baffle spacing, bc is the baffle cut, dtb is the tube-to-baffle diametrical clearance, dsb is the shell-to-baffle diametrical clearance. Considering the material of the tube (70%Cu, 30%Ni) and of the shell (carbon steel), the total anual cost estimation ) (TC that includes direct and indirect costs ) ( BM C and operational costs ) (OC , was conducted in the same manner as in [1,2]:…”

“…During its design it is possible to obtain different configurations, however, it is desirable the optimal design, which starts from the modeling of the same. The recent literature review [1] shows the trend in the use of Evolutionary Algorithms for the mono-objective optimization of STHE. The most commonly used technique is Genetic Algorithm (GA).…”