Collaborative Optimization Framework for the Industrial Thickening–Dewatering Process Based on Mixed Integer Linear Programming

Reducing the economic expenditure on electricity in the thickening–dewatering process is a viable approach to enhancing production efficiency and minimizing energy consumption. However, limited research has been conducted on co-optimization strategies for this process thus far. To address this gap and decrease the energy economic index (EEI) in the thickening–dewatering process, this work introduces a collaborative optimization framework. This work begins by developing a process model capable of simulating the thickening–dewatering process, thereby addressing the issue of insufficient ON-site production data. Leveraging the operational data generated by the process model, data-driven predictive models are constructed to facilitate the formulation of an explicit optimization model. Subsequently, a continuous-time mixed integer linear programming (MILP) problem is established to optimize the process, with the objective of minimizing the EEI while accounting for process safety and electricity price constraints. Finally, the collaborative optimization framework is applied to a gold hydrometallurgy plant, yielding significant improvements. The process achieves a 58.67% reduction in EEI compared with manual operations, alongside a 53.62% reduction in equipment operation time.