Robust coordinated optimization of the thickening-dewatering process under uncertain feeding conditions

Abstract

The thickening-dewatering process is extensively used in mineral processing process, but there is usually strong uncertainty in the feeding condition of the thickener. In this work, we present a scenario tree-based mixed-integer linear programming (MILP) method for addressing the coordinated optimization of the thickening-dewatering process under uncertain feeding conditions. First, we developed a prediction model using partial least squares (PLS) to estimate the underflow concentration and operation time of each piece of equipment. Subsequently, we formulated a robust MILP model to minimize the energy consumption of the process. This model incorporates several constraints, including starting time, operation time, shutdown time, safety limits, and ladder electricity prices for each piece of equipment. Finally, we divided the uncertain feeding condition into three scenarios to solve the robust coordinated optimization problem. Case studies show that the proposed method can effectively mitigate constraint violations, and reduce safety risks.