Abstract
A heat exchanger is a unit operation used to transfer heat between two or more fluids at different temperatures. There are many different types of heat exchangers that are categorized based on different criteria, such as construction, flow arrangement, heat transfer mechanism, etc. Heat exchangers are optimized based on their applications. The most common criteria for optimization of heat exchangers are the minimum initial cost, minimum operation cost, maximum effectiveness, minimum pressure drop, minimum heat transfer area, minimum weight, or material. Using the data modeling, the optimization of a heat exchanger can be transformed into a constrained optimization problem and then solved by modern optimization algorithms. In this chapter, the thermal design and optimization of shell and tube heat exchangers are presented.
Highlights
Heat exchangers are systems used to transfer heat between fluids with different temperatures
Heat exchangers can be classified according to different criteria such as construction, flow arrangement, heat transfer mechanism, etc [1]
Kern’s method is based on experimental data for typical heat exchangers. It is assumed the shell flow is ideal, and leakage and bypass are negligible. Based on this flow model, only a single stream flows in the shell that is driven by baffles
Summary
Heat exchangers are systems used to transfer heat between fluids with different temperatures. The heat exchanger design can be divided into two main categories, thermal and hydraulic design and mechanical design. The thermal and hydraulic design of heat exchangers is presented. Heat Exchangers advanced optimization methods, such as genetic algorithm, non-nominated sorting genetic algorithm, bio-geography-based optimization, particle swarm optimization, Jaya algorithm, and teaching-learning-based optimization, can be more efficient in solving an optimization problem. Each of these methods has its advantages and disadvantages, which are discussed in the optimization section. Genetic algorithm and particle swarm optimization are discussed in detail due to the vast applications that arise from their acceptable accuracy, as well as short computational time [2]
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