Abstract
Heat transfer rate and cost significantly affect designs of shell and tube heat exchangers. From the viewpoint of engineering, an optimum design is obtained via maximum heat transfer rate and minimum cost. Here, an analysis of a radial, finned, shell and tube heat exchanger is carried out, considering nine design parameters: tube arrangement, tube diameter, tube pitch, tube length, number of tubes, fin height, fin thickness, baffle spacing ratio and number of fins per unit length of tube. The “Delaware modified” technique is used to determine heat transfer coefficients and the shell-side pressure drop. In this technique, the baffle cut is 20 percent and the baffle ratio limits range from 0.2 to 0.4. The optimization of the objective functions (maximum heat transfer rate and minimum total cost) is performed using a non-dominated sorting genetic algorithm (NSGA-II), and compared against a one-objective algorithm, to find the best solutions. The results are depicted as a set of solutions on a Pareto front, and show that the heat transfer rate ranges from 3517 to 7075 kW. Also, the minimum and maximum objective functions are specified, allowing the designer to select the best points among these solutions based on requirements. Additionally, variations of shell-side pressure drop with total cost are depicted, and indicate that the pressure drop ranges from 3.8 to 46.7 kPa.
Highlights
Shell and tube heat exchangers are important components in energy conversion systems, oil and chemical industries, etc
Caputo et al [18] used the genetic algorithm in Toolbox for optimizing a heat exchanger and considered an objective function on the basis of total cost of the heat exchanger
The investigation is based on an industrial case, and is performed after modeling the heat flows for a radial low-fin shell and tube heat exchanger and introducing relevant decision variables
Summary
Shell and tube heat exchangers are important components in energy conversion systems, oil and chemical industries, etc. Considered as objective functions (maximum effectiveness and minimum total cost) for a plate fin heat exchanger and chose six decision variables; they use a multi-objective genetic algorithm and depict a set of solutions on a Pareto curve. Sanaye and Haj Abdollahi [12] used a two-objective optimization genetic algorithm to obtain minimum total cost and maximum heat transfer rate They introduce a suitable limit on the Pareto curve from cost and efficiency points of view, to assist in the. Caputo et al [18] used the genetic algorithm in Toolbox for optimizing a heat exchanger and considered an objective function on the basis of total cost of the heat exchanger They minimize the objective function considering decision variables such as tube diameter, shell diameter and space of baffles, and compare their results with traditional approaches. The investigation is based on an industrial case, and is performed after modeling the heat flows for a radial low-fin shell and tube heat exchanger and introducing relevant decision variables
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