Advanced Data Analysis And Modelling In Chemical Engineering
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Advanced Data Analysis and Modelling in Chemical Engineering
Advanced Data Analysis and Modeling in Chemical Engineering provides the mathematical foundations of different areas of chemical engineering and describes typical applications. The book presents the key areas of chemical engineering, their mathematical foundations, and corresponding modeling techniques. Modern industrial production is based on solid scientific methods, many of which are part of chemical engineering. To produce new substances or materials, engineers must devise special reactors and procedures, while also observing stringent safety requirements and striving to optimize the efficiency jointly in economic and ecological terms. In chemical engineering, mathematical methods are considered to be driving forces of many innovations in material design and process development. - Presents the main mathematical problems and models of chemical engineering and provides the reader with contemporary methods and tools to solve them - Summarizes in a clear and straightforward way, the contemporary trends in the interaction between mathematics and chemical engineering vital to chemical engineers in their daily work - Includes classical analytical methods, computational methods, and methods of symbolic computation - Covers the latest cutting edge computational methods, like symbolic computational methods
Applying Multiple-Reaction Stoichiometry to Chemical Reactor Modelling
Author: Guillermo Fernando Barreto
language: en
Publisher: Springer Nature
Release Date: 2024-03-12
This book delves into the realm of Chemical Reaction Engineering (CRE) by showcasing the practical application of multiple-reaction stoichiometry. The authors critically assess various approaches commonly taught in undergraduate CRE courses to establish the relationships between changes in chemical species. In doing so, they propose an innovative conceptual alternative that is specifically tailored for undergraduate lectures. The book carefully selects composition measures that effectively harness the power of stoichiometric relationships in elementary reacting systems and models, which are typically covered in these courses. Going beyond the basics, it also offers a profound discussion on the value of chemical stoichiometry for tackling more intricate reaction systems and detailed models. Moreover, the book presents a simplified procedure that minimizes the reliance on complex linear algebra techniques, making the book accessible to a wider range of readers.
Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing
Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing Detailed resource on the “Why,” “What,” and “How” of integrated process modeling, advanced control and data analytics explained via hands-on examples and workshops for optimizing polyolefin manufacturing. Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing discusses, as well as demonstrates, the optimization of polyolefin production by covering topics from polymer process modeling and advanced process control to data analytics and machine learning, and sustainable design and industrial practice. The text also covers practical problems, handling of real data streams, developing the right level of detail, and tuning models to the available data, among other topics, to allow for easy translation of concepts into practice. Written by two highly qualified authors, Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing includes information on: Segment-based modeling of polymer processes; selection of thermodynamic methods; estimation of physical properties for polymer process modeling Reactor modeling, convergence tips and data-fit tool; free radical polymerization (LDPE, EVA and PS), Ziegler-Natta polymerization (HDPE, PP, LLPDE, and EPDM) and ionic polymerization (SBS rubber) Improved polymer process operability and control through steady-state and dynamic simulation models Model-predictive control of polyolefin processes and applications of multivariate statistics and machine learning to optimizing polyolefin manufacturing Integrated Process Modeling, Advanced Control and Data Analytics for Optimizing Polyolefin Manufacturing enables readers to make full use of advanced computer models and latest data analytics and machine learning tools for optimizing polyolefin manufacturing, making it an essential resource for undergraduate and graduate students, researchers, and new and experienced engineers involved in the polyolefin industry.