On‐Line Monitoring Device for Gas Phase Composition Based on Machine Learning Models and Its Application in the Gas Phase Copolymerization of Olefins

Abstract

This study addresses the challenges of time‐delay and low accuracy in online gas‐phase composition monitoring during olefin copolymerization processes. Three flowmeters based on different mechanisms were installed in series to measure the real‐time exhaust gas flowrate from the reactor. For the same gas flow, the three flowmeters display different readings, which vary with the properties and composition of the gas mixture. Consequently, the composition of the mixed gas can be determined by analyzing the reading of the three flowmeters. Fitting equations and three machine learning models, namely decision trees, random forests, and XGBoost, were employed to calculate the gas composition. The results from cold‐model experimental data demonstrate that the XGBoost model outperforms others in terms of accuracy and generalization capabilities. For the concentration of ethylene, propylene, and hydrogen, the determination coefficients (R2) were 0.9852, 0.9882, and 0.9518, respectively, with corresponding normalized root mean square error (NRMSE) values of 0.0352, 0.0312, and 0.0706. The effectiveness of the online monitoring device was further validated through gas phase copolymerization experiments involving ethylene and propylene. The yield and composition of the ethylene and propylene copolymers were successfully predicted using the online measurement data.This article is protected by copyright. All rights reserved