Neural Network-Based Adaptive Fault-Tolerant Control of Dissolved Oxygen and Nitrate Concentrations in WWTPs

Wastewater treatment process (WWTP) is one of the most means to achieve the water resource protection and sustainable utilization. Dissolved oxygen and nitrate are main factors limiting effluent quality, which are involved with carbon consumption, nitrification and denitrification. To achieve efficacious control of dissolved oxygen and nitrate concentrations under actuator faults and saturation, a nonlinear mapping-based adaptive neural fault-tolerant control method is developed in this article for WWTP. The coordinate transformation is employed to boil down the constrained denitrification and aeration processes to unconstrained issues. To obtain the appointed steady-state tracking performance, nonlinear mapping is incorporated into the adaptive fault-tolerant controller, because of many categories of physical, chemical, and biological phenomena existing in associated and treatment zones, radial basis function neural networks are used to approximate the uncertain dynamics in WWTPs. The proposed control scheme is verified via the benchmark simulation model 1.