Y. A. Liu Libros

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.

This text provides a comprehensive review of the theory and practice behind simulating and optimizing petroleum refining processes. It covers the quantitative modeling of key refinery reactions and fractionation processes, introducing essential concepts in thermodynamics and physical property predictions for hydrocarbon components. The authors, experts in the field, detail procedures and essential data needed for constructing reaction and fractionation models using commercial software. They emphasize filtering extensive refinery data and utilizing plant data to calibrate models, extending them to incorporate crucial fractionation sub-models. This foundation enables a better understanding of plant phenomena, ultimately enhancing yield, consistency, and performance. Additionally, the authors discuss applying models within the broader context of refinery planning through linear programming. Key topics include thermodynamics, physical property predictions, and the development of detailed models and workflows for atmospheric and vacuum distillation units, as well as modeling for FCC, catalytic reforming, and hydroprocessing units. Aimed at chemical and process engineers, this resource explores advanced simulation tools and techniques to support both experienced professionals and newcomers in the field.