An Improved Grey Wolf Optimization Algorithm for Liquid flow Control System

Abstract

Liquid flow in a process industry is one of the significant factors which should be controlled to get the better quality and decrease the expense of generation. Customary methodology includes manual tuning of the input process parameter to obtain the required flow rate is tedious and exorbitant. Notwithstanding, estimation of a precise computational model for fluid stream control procedure can fill in as elective methodology. It is only a non-straight enhancement issue. As a contextual investigation, the WFT -20-I measure control arrangement for flow rate measurement and Control issue is thought of. In this work we proposes a hybrid improved particle swarm optimization (PSO-GWO) used to start the people's position, which can build the decent variety of the wolf pack, balance the global and neighborhood search capacity of the calculation and improve the intermingling pace of the calculation contrast with the Gray wolf enhancement (GWO) and Particle swarm advancement (PSO). Non linear models are improved utilizing those recently proposed streamlining strategies. Additionally all the utilized optimization techniques can anticipate the fluid stream rate with good exactness. The outcomes were investigated by utilizing the root mean square error (RMSE), exactness, and the different measures to evaluate the level of identification performance of the liquid flow contextual analysis model. The trustworthiness of the present models was compared with the past model for similar subsystems utilizing competitive intelligent methodologies. The measurable examination of the acquired outcomes produced the proposed HPSOGWO has most elevated generally speaking proficiency (i.e.99.96%) and it beat the others strategies for the majority of the instances of demonstrating for fluid stream control process. The outcomes of the present model show that the proposed approach gives prevalent demonstrating execution and outflanks its rivals.