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Development of accurate soft sensors for online prediction of Mooney viscosity in industrial rubber mixing processes is a difficult task because the modeling data set often contains various outliers. A correntropy kernel learning (CKL) method for robust soft sensor modeling of nonlinear industrial processes with outlier samples is proposed. Simultaneously, the candidate outliers can be found out once the CKL-based soft sensor model is built. An index for description of the uncertainty of the CKL model is designed. Furthermore, to obtain more robust and accurate predictions, an ensemble CKL (ECKL) method is formulated by introducing the simple bagging strategy. Consequently, by detecting the outliers in a sequent manner, the database becomes more reliable for long-term use. The application results in an industrial rubber mixing process exhibit the superiority of ECKL in terms of better prediction performance.

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