Study on plant-wide optimization based on interval number for gold hydrometallurgical process

Abstract

Considering the difficulty of accurate online-measurement of some key variables in hydrometallurgy plant-wide production process, which leads to process modeling difficult and optimization control based on conventional plant-wide optimization methods is difficult to realize, it is very necessary to establish an accurate plant-wide optimization model of the gold hydrometallurgical process. A plant-wide optimization method based on interval number is proposed for realizing the gold hydrometallurgy plant-wide process modelling and optimization in this paper. By using interval numbers to replace the key variables that cannot be measured, the established plant-wide optimization model can further satisfy with practical productive process. Furthermore, considering the optimization solution based on non-linear mechanism model is very time-consuming, a back propagation neural network is constructed and used to represent the local procedures models in this paper. Finally, a second-order oscillation partical swarm optimization (PSO) algorithm with high gloval convergence is used to solve the optimization problems. Simulation results indicate that the proposed model has a better optimization performance in gold hydrometallurgical process.