Enhancing quality control of polyethylene in industrial polymerization plants through predictive multivariate data‐driven soft sensors

Abstract

Measuring polyethylene properties in the laboratory is time‐consuming and usually unavailable in real‐time, posing significant challenges for controlling product quality in polymerization plants. This research focuses on developing multivariate data‐driven soft sensors for online monitoring and prediction of key characteristics. The targeted properties for prediction include the melt flow index (MFI), density, and average particle diameter in the gas‐phase fluidized bed reactor, as well as the MFI and flow rate ratio (FRR) in the slurry‐phase process. We conducted an exhaustive examination using an ensemble learning approach to quantify the impact of process variables on the model's responses. Various machine learning (ML) algorithms were trained and validated using datasets from industrial ethylene polymerization plants. The precision of the ML models was improved by splitting the datasets into categories comprising high and low MFI and FRR, as well as linear low‐density and high‐density clusters. Then, segmented ML models were developed for each cluster. The results demonstrated that the segmented ML models utilizing optimized Gaussian process regression models with suitable kernel functions and ensemble bagged tree models offered the highest accuracy in predicting the MFI, FRR, and density. Additionally, the comprehensive ML model without clustering, utilizing Gaussian process regression with an isotropic exponential kernel function, proved to be the most effective at predicting the average particle diameter.