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Effluent BOD Concentration Prediction in Wastewater Treatment Process Based on MIC and RBFNN

In order to deal with the problem that measurement of biochemical oxygen demand (BOD) in wastewater treatment process (WWTP) is difficult to achieve, a soft sensing algorithm for effluent BOD concentration prediction based on maximum information coefficient (MIC) and radial basis function neural network (RBFNN) is proposed. Firstly, the MIC is employed to filter the input variables that have close correlation with the effluent BOD concentration. Secondly, an improved K-means algorithm is used to initialize the center and width of the RBFNN, and the Levenberg-Marquardt (LM) algorithm is used to train the weight of the network. Finally, the benchmark datasets and the real data of the WWTP are used for experiments, the results indicate that the network has a good prediction on the effluent BOD concentration.