Soft sensor modeling of industrial process data using kernel latent variables-based relevance vector machine

A composite model integrating latent variables of kernel partial least squares with relevance vector machine (KPLS-RVM) has been proposed to improve the prediction performance of conventional soft sensors when facing industrial processes. First, the latent variables are extracted to cope with the high dimensionality and complex collinearity of nonlinear process data by using KPLS projection. Then, the probabilistic method RVM is used to develop predictive function between latent variables and the output variable. The performance of the proposed method is evaluated through two case studies based on subway indoor air quality (IAQ) data and wastewater treatment processes (WWTP) data, respectively. The results show the superiority of KPLS-RVM in prediction performance over the other counterparts including least squares support vector machine (LSSVM), PLS-LSSVM, PLS-RVM, and KPLS-LSSVM. For the prediction of effluent chemical oxygen demand in WWTP data, the coefficient of determination value of KPLS-RVM has been improved by approximately 7.30-19.65% in comparison with the other methods. (C) 2020 Elsevier B.V. All rights reserved.