Soft sensor development for online quality prediction of industrial batch rubber mixing process using ensemble just-in-time Gaussian process regression models

Abstract

Rubber mixing is a nonlinear batch process that lasts for very a short time (ca. 2–5min). However, the lack of online sensors for quality variable (e.g., the Mooney viscosity) has become a main obstacle of controlling rubber mixing accurately, automatically and optimally. This paper proposes a novel soft sensing method based on Gaussian process regression (GPR) models fortified with both ensemble learning and just-in-time (JIT) learning, which ensures precision and robustness at the same time. More specifically, this method first builds multiple input variable sets from random local datasets, then uses the obtained input variable sets to establish local models and send them to ensemble learning with Bayesian inference and finite mixture mechanism before making the final prediction output. The superiority of the proposed method is demonstrated using an industrial rubber mixing process.