Modeling and optimization of heat exchanger using artificial neural network and genetic algorithm

Abstract

Artificial Neural Network (ANN) and Genetic Algorithm (GA) techniques have been widely used for thermal analysis and multi-objective optimization of heat exchanger systems across the world. This report investigates the applications of artificial neural networks (ANN) and GA for enhancing the efficiency of heat exchangers and thereby improving the quality of the production of crude oil. The world of artificial neural network is increasingly growing in the fields of engineering applications, and the examples of these applications are detections of faults, processing of signal, process modelling and control. The ANN technology is able to provide models that are able to solving complex nonlinear processes that can be widely used since the world is constantly developing their processes to being fully-automated and the need for quick fixes to errors arising is very crucial for maintaining the flow of the processes. It is also worth noting that designing a heat exchanger with optimal specifications in a timely manner is cost effective and time efficient as well. The study will focus on the modelling and optimization aspect of the heat exchanger using artificial neural networks.