An enhanced just-in-time learning methodology for process modeling

A new just-in-time learning methodology for nonlinear process modeling is developed in this paper. In the proposed method, both distance measure and angle measure are used to evaluate the similarity between data, which is not exploited in the conventional methods. In addition, parametric stability constraints are incorporated into the proposed method to address the stability of local models. Furthermore, a new procedure of selecting the relevant data set is proposed. The proposed methodology is illustrated by a case study of modeling a polymerization reactor. The adaptive ability of the just-in-time learning is also evaluated.