Multispatial-Scale Optimal Control With Multisource Information for Nitrification in Wastewater Treatment Process

The drastic fluctuations of influent pollutant load are inevitable in wastewater treatment process, which makes it difficult for nitrification to regulate dissolved oxygen concentrations with minimal effort to ensure the effluent quality. To solve this problem, a multispatial-scale optimal control with multisource information (MSI-MSSOC) is developed in this article. First, a multispatial-scale optimization model, making use of mechanism knowledge and process data, is designed to construct reasonable objectives and constraints. Then, the performance indexes can be described to evaluate the comprehensive adjustment effect of dissolved oxygen concentrations in different areas. Second, an adaptive knowledge acquisition strategy is employed to extract the interactivity between feasible and infeasible solutions. Then, the proposed strategy can assist in the optimal control to search for the optimal solutions. Third, a knowledge-aided optimization algorithm is introduced to update the feasible region to calculate the optimal solutions. Then, the optimal dissolved oxygen concentrations in different areas can be obtained to guarantee the effluent quality and reduce the energy consumption. Finally, the proposed MSI-MSSOC is applied to Benchmark Simulation Model No. 1 to verify its effectiveness. The experimental results demonstrate that MSI-MSSOC can achieve the desirable operation performance of nitrification.