文献类型：期刊文献

英文题名：Control-Oriented Modeling for Industrial Propylene Polymerization Process based on Physics-Informed Neural Network

作者：Li, Shenjie[1,2]; Zhang, Xixiang[1]; Tian, Zhou[1]; Lu, Jingyi[1]; Du, Wenli[1]; Qian, Feng[1]

机构：[1] Ministry of Education, East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Shanghai, 200237, China; [2] Qingyuan Innovation Laboratory, Quanzhou, 362801, China

年份：2024

起止页码：2497

外文期刊名：14th Asian Control Conference, ASCC 2024

收录：EI(收录号：20244117170398)

语种：英文

外文关键词：Gluing - Lagrange multipliers

摘要：Loop reactors are extensively employed in the industrial production of polypropylene. Nonetheless, the high non-linearity and stiffness of this process pose significant challenges. Traditional methods are time-consuming in simulating this process. This work is aimed at using Physics-Informed Neural Network (PINN) to improve the computation efficiency. PINN can use neural network to directly give the solution without solving the ODEs through explicit numerical methods. Particularly, we extend the application of PINNs to include initial states and control inputs, making them suitable for control tasks. In order to enable the model to track the stiffness variables and to improve the generalization performance, we combine Latin Hypercube Sampling (LHS) Gaussian-Lagrange method for sampling time, states and control collocation points. Furthermore, we utilize the Extended Physics-Informed Neural Networks framework, which ensures that solutions inherently satisfy initial conditions and constraints. The Control-Oriented Physics-Informed Neural Network (COPINN) proposed here achieves calculation accuracy comparable to the Backward Differentiation Formula (BDF) method while significantly improving computational efficiency. ? 2024 Asian Control Association.