Multi‐stage economic model predictive control for a gold cyanidation leaching process under uncertainty

Abstract

ABSTRACTIn order to dynamically operate the gold cyanidation leaching process (GCLP) under uncertainty, a multi‐stage economic model predictive control (EMPC) is proposed for GCLP for the transient and steady‐state economic optimization. The proposed multi‐stage EMPC is composed of two steps. In the first step, the unmeasurable uncertain parameters are estimated by using Tikhonov regularization based method, so as to avoid amplification and propagation of the noise measurements into the estimation. Based on the estimated results, the scenario tree for multi‐stage EMPC is generated from the historical data using a data‐driven approach, and the control inputs are obtained from solving the resulting large nonlinear programming problem (NLP) at each sampling point. The resulting uncertainty model and the probability of each scenario are more consistent with the actual industrial GCLP, and the solutions are less conservative. The efficiency of the proposed multi‐stage EMPC is verified through a simulated industrial GCLP. Compared with other EMPC methods, including classic EMPC and multi‐stage EMPC with box uncertainty region, the proposed method can reduce the economic cost while accounting for the constraints at the same time.