Knowledge-Based Evolution Method for Optimizing Refrigerant Circuitry of Fin-and-Tube Heat Exchangers

Abstract

Fin-and-tube heat exchangers are widely used in HVAC applications, and the optimization of the refrigerant circuitry (RC) is important to improve their performance. The genetic algorithm (GA) is a commonly used optimization method but cannot be directly used for RC optimization. This paper presents a new optimization method based on the GA, which is simpler and more effective for RC optimization than existing methods. The optimization method for RC optimization presented in this paper is a knowledge-based evolution method (KBEM) that includes an improved genetic algorithm (IGA) and domain knowledge-based RC search methods. The IGA, as the basis of the KBEM, uses a tailor-made RC coding method, RC generation method, and improved genetic operators. The knowledge-based RC search methods increase the optimization efficiency of the IGA by reasonably reducing the search space according to the domain knowledge. Case studies show that the KBEM can generate RC designs better than those generated by the existing RC optimization methods, and the KBEM is suitable for various types of fin-and-tube heat exchangers.