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

Rubber mixing is a nonlinear batch process that lasts for very a short time (ca. 2–5 min). 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...