Abstract： The integrating method of kernel partial least squares with Gaussian process regression (KPLSGPR) was used to predict the effluent chemical oxygen demand (COD) and effluent suspended solids (SS) in papermaking wastewater treatment processes (WWTP). The latent variables from KPLS were used to handle the high dimensionality and the complex collinearity problem of WWTP data. The GPR model was used to develop the regression between the latent variables and the output variables. Based on the WWTP data set from a paper mill, the simulation experiment was conducted. The soft sensors of artificial neural network (ANN), PLSANN and KPLSANN were proposed for the comparison. The results show that the latent variables of KPLS can be used to improve the prediction results of conventional models obviously, and KPLSGPR achieves the best prediction performance. For the prediction of effluent COD and effluent SS, the values of determination coefficient for KPLSGPR are 0.575 and 0.610, respectively, which are respectively improved by 36.90% and 43.87% in comparison with those of the conventional counterparts.
