Optimization of NGL separation process with maximum quality and energy reduction

Abstract There are different methods to separate Natural Gas Liquid (NGL) from natural gas. One of these methods is refrigeration. Temperature reduction occurs in the dew point adjustment part to condense the NGL. The aim of this paper is to present a methodology for optimizing the NGL production process by calculating the best quantity for some set-points (the temperature, pressure, and etc for some vessels or other equipment) and at the same time try to reduce the energy consumption. To do this, we use a hybrid algorithm including a Genetic Algorithm (NSGA II) and Artificial Neural Network system (ANN). Indeed, in this research, we define a multi-objective problem and try to investigate more solutions for finding the best pareto-front. Therefore, it needs more time to evaluate solutions, thus, using an ANN to evaluate the amount of objective functions according to different solutions, is faster than the simulation methods. We solve the defined multi-objective problem by using of NSGAII. In order to design a tool that is a decision-helper for selecting the appropriate set-points, the ability of the ANN algorithm along with multi-objective optimization is evaluated. We implement our proposed algorithm on one of the Iranian chemical factories, knowing as the NGL plant, which separates the Natural Gas Liquid (NGL) from natural gas, as the case study of this article. We show the effectiveness of our proposed algorithm by using the nonparametric statistical Kruskal-Wallis.