An adaptive task-oriented RBF network for key water quality parameters prediction in wastewater treatment process

The real-time availability of key water quality parameters is of great importance for an advanced and optimized process control in wastewater treatment plants (WWTPs). However, due to the complex environment conditions and costly measuring instruments, it is generally difficult and time-consuming to measure certain key water quality parameters online, such as the effluent biochemical oxygen demand (BOD) and the effluent total nitrogen (TN). Recently, artificial neural networks have powered the online prediction tasks in several WWTPs. Hence, in this paper, an adaptive task-oriented radial basis function (ATO-RBF) network is developed to design prediction models for accurate timely acquirements of the effluent BOD and the effluent TN. The advantage of ATO-RBF network is that the architecture is not designed by human engineers; it is adaptively generated from the data to be processed. First, to enhance the learning ability and generalization performance of prediction models, an error correction-based growing strategy and a second-order learning algorithm are combined to design the ATO-RBF network. Then, RFB nodes with low significance would be pruned without sacrificing the learning accuracy, making the prediction model more compact. Additionally, the convergence of the ATO-RBF network is analyzed based on the Lyapunov criterion, which can guarantee its feasibility in practical applications. Finally, the proposed methodology is verified by benchmark simulations and real industrial data, showing superior prediction accuracy in compared with conventional methods.