Thermoeconomic optimization of a shell and tube condenser using both genetic algorithm and particle swarm

This paper presents a thermoeconomic optimization of a shell and tube condenser, based on two new optimization methods, namely genetic and particle swarm (PS) algorithms. The procedure is selected to find the optimal total cost including investment and operation cost of the condenser. Initial cost includes condenser surface area and operational cost includes pump output power to overcome the pressure loss. Design parameters are tube number, number of tube pass, inlet and outlet tube diameters, tube pitch ratio and tube arrangements (30, 45, 60 and 90°). Therefore, shell diameter should be selected less than 7 m, tube length should be less than 15 m, and ratio of diameter to tube length should be in the range of 1/12 to 1/3. In addition, it is found that GA provides better results for computer CPU running time, compared to PS algorithm. Finally, a sensitivity analysis of design parameters at the optimal point is conducted. Results show that an increase in the tube number leads to decrease in the objective function first then it leads to a considerable increment in objective function.