Application of Constrained Multi-objective Evolutionary Algorithm in Multi-Source Compressed-air Pipeline Optimization Problems

Abstract

To meet the request of manufacture, several compressor stations usually run at the same time. Decreasing the output pressure of compressor station is one of the major methods to reduce the power utilized by the motors of the compressors. Due to the interaction of several compressor stations with each other, how to set the output pressure of each compressor station becomes a big problem. This paper proposes the Constrained Multi-objective Optimization of Multi-Source Compressed-air Pipeline Optimization Problems (CMO-MSCPOPs) in compressed-air transmission networks of process industries. The problem formulation involves the minimization of the output pressure of each compressor station. Constraints associated with compressed-air flow rate and compressor stations guarantee the work of each downstream process. In case studies, the model is divided into two topology forms. The optimization of the model is performed using NSGA-II. The solution obtained is a set of Pareto solutions from which a decision making process is highlighted to select a specific preferred solution. Aiming to illustrate the performance of the proposed approach, the tool is applied to two typical network examples considering two compressor stations.