Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants

•Our goal is to construct high predictive soft sensors for various process states.•Multiple online support vector regression (OSVR) models are constructed.•Prediction results of the OSVR models are combined using Bayes' rule.•The prediction error is estimated from the multiple predicted values.•The performance is confirmed with a simulated dataset and two industrial datasets.

A soft sensor predicts the values of some process variable y that is difficult to measure. To maintain the predictive ability of a soft sensor model, adaptation mechanisms are applied to soft sensors. However, even these adaptive soft sensors cannot predict the y-values of various process states in chemical plants, and it is difficult to ensure the predictive ability of such models on a long-term basis. Therefore, we propose a method that combines online support vector regression (OSVR) with an ensemble learning system to adapt to nonlinear and time-varying changes in process characteristics and various process states in a plant. Several OSVR models, each of which has an adaptation mechanism and is updated with new data, predict y-values. A final predicted y-value is calculated based on those predicted y-values and Bayes' rule. We analyze a numerical dataset and two real industrial datasets, and demonstrate the superiority of the proposed method.