Due to the complex varying operational conditions and weak stability to resist the impact loads of influent flow rate and quality

it is difficult to satisfy operational objective of wastewater treatment plants.Therefore

the effective identification of the operational conditions for the complex wastewater treatment processes becomes one of the most important issues due to the potential advantages to be gained from reduced costs

improved productivity and increased production quality.A multi-classification probabilistic extreme learning machine(ELM)was proposed to enhance the reliability of classification by using the combination of extreme learning machine and the Bayesian decision theory.Extreme learning machine was used to build the output coding model between the real time process variables and the operational condition coding in the wastewater treatment process.ELM prediction values fluctuated around the class encoding following a normal Gaussian distribution.A potential function was calculated for each training sample based on the density methods.The potential functions of the training samples for each class were averaged to obtain the probability density function of each class.Parameters of probability density function for each class were estimated by the nonlinear least squares method.It could decrease the misclassification to classify the unknown samples due to the uncertainty of ELM predictions.The proposed method was verified with the data from a small scale industrial wastewater treatment plant

located in Liaoning province

China.Experimental results showed that the proposed method had a relatively better performance than ELM on classification accuracy.