文献类型：期刊文献

英文题名：Kinetics Parameter Identification of Chain Shuttling Polymerization Based on Physics-Informed Neural Networks

作者：Zhao, Jieming[1]; Tian, Zhou[1]; Zhang, Xixiang[1]; Duan, Zhaoyang[1]; Lu, Jingyi[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

年份：2024

卷号：58

期号：14

起止页码：184

外文期刊名：IFAC-PapersOnLine

收录：EI(收录号：20243817074464)

语种：英文

外文关键词：Chain length - Polymerization - Structural dynamics

摘要：Chain-shuttling polymerization is widely used to synthesize specialized polymer materials with customized properties. The significance of modeling in chemical process simulation lies in accurately describing and analyzing complex chemical systems through mathematical and computational models, thereby enhancing the efficiency and reliability of process design. Due to limited and noisy measurements and the complex model structure, parameter identification for chain-shuttling polymerization has been a long-standing problem. To address this issue, in this work, we propose to first describe the dynamic process with a set of ordinary differential equations (ODEs) based on the method of moments. This method characterizes the dynamic variations of the average chain length. After that, we introduce Physics-Informed Neural Networks (PINNs) to estimate the unknown parameters in the ODEs. Since PINNs can incorporate the ODEs constraints during the training process, they can effectively integrate the polymerization mechanism with the observed process data, thereby reducing the amount of data needed for training. A comparative analysis between parameters estimated using PINNs and the ground truth values demonstrates high accuracy and efficiency, even with sparse and limited observations. This showcases the potential value of PINNs in chemical process identification. Copyright ? 2024 The Authors.