To achieve efficient operation of the wastewater treatment plant(WWTP)

it is necessary to establish a model that accurately describes the behavior of the plan. In this paper

the radial basis function neural network (RBFNN) is utilized in the modeling of the WWTP basing on the available influent and effluent data. Considering the bounded modeling error

linear-in-parameters set membership identification algorithm is used to describe an uncertain set of each vector representing the weights of the links between all the hidden neurons and one output neuron. Comparing with the existing methods which are all proposed for a single effluent variable

the method here builds a predictor model which can compute confidence intervals for multiple effluent variables simultaneously according to the values of the influent variables. The confidence intervals can characterize the existence ranges of the effluent variables

such that reliable estimates of them are obtained. By the estimates

the effluent quality or the WWTP performance can be evaluated. Besides

the interval predictor model is also applied to the fault detection and isolation of the WWTP to realize reliable operation. The experiment results show the satisfying performance of the proposed method.