Nonlinear multiobjective model-predictive control scheme for wastewater treatment process

• NMMPC has two loops: the first loop deals with the control of DO and the second one is taking care of the nitrate.
• The multivariate analysis of the process behavior can be obtained to avoid undesired interactions.
• The errors between the set-point values and the control output remain within the range of ±0.05 mg/L (±2.5%) – SO5SO5.
• The errors of the set-point values and the control output can be kept within the range of ±5% – SNO2SNO2.

A nonlinear multiobjective model-predictive control (NMMPC) scheme, consisting of self-organizing radial basis function (SORBF) neural network prediction and multiobjective gradient optimization, is proposed for wastewater treatment process (WWTP) in this paper. The proposed NMMPC comprises a SORBF neural network identifier and a multiple objectives controller via the multi-gradient method (MGM). The SORBF neural network with concurrent structure and parameter learning is developed as a model identifier for approximating on-line the states of WWTP. Then, this NMMPC optimizes the multiple objectives under different operating functions, where all the objectives are minimized simultaneously. The solution of optimal control is based on the MGM which can shorten the solution time. Moreover, the stability and control performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control strategy gives satisfactory tracking and disturbance rejection performance for WWTP. Experimental results show the efficacy of the proposed method.