Knowledge-Data Driven Multitime-Scale Optimal Control for Wastewater Treatment Process

The increasing demand for wastewater treatment processes is to improve the effluent quality and reduce the energy consumption. However, due to the existence of different time scales data acquisition for effluent quality and energy consumption, it is difficult to achieve optimum operation of wastewater treatment processes with multitime-scale property. To solve this problem, a knowledge-data driven multitime-scale optimal control (KDD-MTSOC) is designed in this article. First, a knowledge-data driven optimal control system is established by dividing the optimal control problem into different time scales, and solving it by matching with appropriate optimization algorithms. Then, the frequency of optimal control is improved and data is fully and reasonably used. Second, a knowledge-based regression kernel strategy is employed to establish the reasonable objective functions and constraints. Then, the objective functions and constraints are favorable to balance performance indexes and describe the multitime-scale characteristics. Third, a knowledge decision-based particle swarm optimization (KDPSO) algorithm is presented to solve the multitime-scale optimization problem of KDD-MTSOC. Then, the KDPSO algorithm can effectively improve the operational performance. Finally, the proposed KDD-MTSOC is applied to the Benchmark Simulation Model No. 1 to verify its effectiveness. The experimental results demonstrate that the KDD-MTSOC method can achieve outstanding operational performance.