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Highlights: [•] Soft sensors are widely used to predict process variables in chemical plants. [•] Our goal is to achieve high prediction accuracy of soft sensors for new data. [•] We employ the online support vector regression (OSVR) and time variable. [•] The hyperparameters and the window size of the OSVR model were discussed. [•] The performance was confirmed with simulation data and real industrial data. [Copyright &y& Elsevier]

Abstract: Soft sensors are used in chemical plants to estimate process variables that are difficult to measure online. However, the predictive accuracy of adaptive soft sensor models decreases when sudden process changes occur. An online support vector regression (OSVR) model with a time variable can adapt to rapid changes among process variables. One problem faced by the proposed model is finding appropriate hyperparameters for the OSVR model; we discussed three methods to select parameters based on predictive accuracy and computation time. The proposed method was applied to simulation data and industrial data, and achieved high predictive accuracy when time-varying changes occurred. [Copyright &y& Elsevier]