Mesoscale Particle Size Predictive Model for Operational Optimal Control of Bauxite Ore Grinding Process

摘要

This article investigates the use of a mesoscale kinetic model to cooperate with the operational optimal control of bauxite ore grinding process. In this article, we propose a new modeling framework where a discretized distributed parameter macroscale model and a mesoscale kinetic model are combined to predict the grinding product particle size. The mesoscale kinetic method does not need an explicit model of the process because it describes the process as a stochastic process. However, the high computational demand has prevented the kinetic model from using an online setting. We overcome this problem by embedding an acceleration algorithm based on the tau-leap method. The proposed model is validated using experimental data. Finally, a solution of the bauxite ore grinding operational optimal control is proposed and the cooperation of the predictive model with other modular is demonstrated.