Search for Optimum Operating Conditions for a Water Purification Process Integrated to a Heat Transformer with Energy Recycling using Artificial Neural Network Inverse Solved by Genetic and Particle Swarm Algorithms

Abstract

The coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling was optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with optimization methods: genetic algorithms (GAs) and particle swarm algorithm (PSA). Therefore, ANNi was solved by these optimization methods to estimate the optimal input variables when a COP is required. The paper adopts two cases studies to accomplish the comparative study. The results illustrate that the GAs outperforms the PSA. Finally, the study shows that the GAs based on ANNi is a better optimization method for control on-line the performance of the system, and constitutes a very promising framework for finding a set of “good solutions”.