Optimization of Multistage Vapor Compression Systems Employing Genetic Algorithms Available to Purchase

Genetic algorithms involve the coding of a solution into a binary string in the same manner that DNA is a biological coding. A population of binary strings are randomly created, evaluated, allowed to mate, and mutated to form a new generation of strings. There is a mating preference given to those strings which rate the highest to simulate the survival of the fittest theory that exists in nature. This process of evaluation, mating, and mutation is repeated until some termination criteria are met. A computer code was written to simulate the vapor compression systems and perpetuate the genetic algorithm. The genetic algorithm functioned adequately enough to provide general trends but it did not find a universal optimum. After numerous runs, the code produced data that suggest that systems which employ intercooler/flash tanks and operate at lower evaporating temperatures have a higher multistage effectiveness. Multistage effectiveness is a novel term defined as the ratio of the overall coefficient of performance (COP) of the multistage system and the combined coefficient of performance of a group of basic vapor compression systems with cooling capacities and evaporating temperatures that parallel the evaporators in the multistage system.