Fault detection of dissolved oxygen sensor in wastewater treatment plants

In wastewater treatment processes, the monitoring of dissolved oxygen sensor is the key to ensure the quality of effluent. In this paper, a method for fault detection of dissolved oxygen sensor is proposed using set membership identification and radial basis function(RBF) neural network. The time series model of KLa5 is built by RBF neural network in virtue of its universal approximation ability. Considering the bounded modeling error, the set description of the output weights of the network is obtained by linear-in-parameters set membership identification algorithm. This built model can give a one-step prediction of the confidence interval of KLa5 under the fault-free case. If the real of KLa5 exceeds the predicted confidence interval, a failure of the dissolved oxygen sensor can be determined.