This detailed resource explores the integration of process modeling, advanced control, and data analytics to optimize polyolefin manufacturing. It provides hands-on examples and workshops, addressing the "Why," "What," and "How" of these topics. The book covers polymer process modeling, advanced process control, data analytics, machine learning, and sustainable industrial practices. It tackles practical challenges, including real data stream handling, model detail development, and model tuning, facilitating the application of concepts in real-world scenarios. Key topics include segment-based modeling of polymer processes, thermodynamic method selection, and physical property estimation. It also delves into reactor modeling, convergence tips, and data-fit tools, alongside various polymerization methods such as free radical, Ziegler-Natta, and ionic polymerization. The text emphasizes improving operability and control through steady-state and dynamic simulation models, as well as model-predictive control and the application of multivariate statistics and machine learning in optimizing manufacturing processes. This resource equips undergraduate and graduate students, researchers, and engineers—both new and experienced—with the knowledge to leverage advanced computer models and cutting-edge data analytics tools, making it indispensable for anyone involved in the polyolefin industry.
