Plant‐wide optimization for gold hydrometallurgy based on the fuzzy qualitative model and interval number

Abstract

Since the difficulty of obtaining accurate online-measurement of some key variables in hydrometallurgy plant-wide production process causes that the quantitative models of some procedures are difficult to establish and the plant-wide optimization based on quantitative model is difficult to realize, a plant-wide optimization method based on interval numbers is proposed in this work. First, based on the information of expert knowledge and the experience of field workers, a fuzzy qualitative model is constructed, and outputs of the qualitative model are reasonably divided into multiple modes simultaneously. By analyzing the process properties, an optimal control problem for hydrometallurgy plant-wide production process in the steady state is proposed to achieve process requirements which is to obtain the lowest cost of sodium cyanide and zinc as well as high gold quality. Then, by using interval numbers to represent the key variables that cannot be measured, an optimization method based on interval numbers is proposed for every output mode of the qualitative model. Finally, the hydrometallurgy process is carried in industrial simulation experiment.The results demonstrate that the proposed optimization scheme has much wider applicability than conventional optimization methods, especially for its improved performance of solving optimization problem with uncertainty.KEYWORDS expert knowledge, hydrometallurgy, interval number, plant-wide optimization, qualitative model | INTRODUCTIONOptimal control is an effective way to realize fine control of the gold hydrometallurgy production process through improving its comprehensive production indicators, such as concentrate grade, metal recovery rate, mineral resources comprehensive utilization rate, etc. Furthermore, optimal control can achieve high yield and excellent quality through saving energy and reducing consumption. 1 Specifically, the gold hydrometallurgy plant-wide optimization has a great important significance in improving economic benefits and solving the problems of mineral resource shortage and underutilization. 2,3 Prior to implementing optimization and control, it is necessary to establish an accurate process model. Over the past several years, many modeling methods of hydrometallurgy process are based on quantitative mathematical models. [4][5][6] To realize the optimization of comprehensive production indicators in mineral processing, Yu et al establish a nonlinear multiobjective programming optimization model and use the improved evolutionary algorithm to optimize five production indicators. 7 To achieve comprehensive production indicator with high efficiency and low consumption in