Process modeling based on nonlinear PLS models using a prior knowledge-driven time difference method

Abstract In this study, we proposed a new nonlinear partial least squares (NPLS) method using time difference (TD) method for modeling chemical and environmental processes. The TD-NPLS method embeds the TD method into the NPLS modeling, where a prior knowledge related to key process information can be utilized. The NPLS can model the nonlinear inner relationships in a reduced data dimension and the TD method captures the gradual changes, which results in the deterioration of the predictive accuracy. The TD-NPLS method is aimed at achieving a high predictive accuracy by simultaneously capturing the characteristics of nonlinearity and dynamics in a single modeling framework. The results in three case studies, one simulated case and two industrial environmental cases showed that the TD-NPLS method absolutely has a higher predictive accuracy in modeling nonlinear and auto-correlated processes than the linear PLS, NPLS, and TD-LPLS models.