A two-layer chance-constrained optimization model for a thickening-dewatering process with uncertain variables

The feed mass and the filter-press mass per cabinet (FMP) are uncertain variables in the thickening-dewatering (TD) process. These uncertain variables must be considered for the optimization; otherwise, the energy economic index (EEI) and the safety risks will increase. Therefore, in this paper, a two-layer chance-constrained optimization model for the TD process with uncertain variables is proposed. The optimization model is a sample average approximate-expected value model (SAA-EVM), and scenarios are generated by Monte-Carlo simulation. To reduce the computational time, the optimization model is divided into a two-layer chance-constrained optimization model. The computational time is reduced by reducing the dimensions of the decision variables. Simulation results show that this two-layer chance-constrained optimization model can reduce the EEI and safety risks and improve the stability of the process, while the computational time meets the requirements of mineral processing plants.