A systematic data-driven modelling framework for nonlinear distillation processes incorporating data intervals clustering and new integrated learning algorithm

Abstract

The distillation process is an important chemical process, and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling, thus improving the efficiency of process optimization or monitoring studies. However, the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals, which brings challenges to accurate data-driven modelling of distillation processes. This paper proposes a systematic data-driven modelling framework to solve these problems. Firstly, data segment variance was introduced into the K-means algorithm to form K-means data interval (KMDI) clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction. Secondly, maximal information coefficient (MIC) was employed to calculate the nonlinear correlation between variables for removing redundant features. Finally, extreme gradient boosting (XGBoost) was integrated as the basic learner into adaptive boosting (AdaBoost) with the error threshold (ET) set to improve weights update strategy to construct the new integrated learning algorithm, XGBoost-AdaBoost-ET. The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.