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Attainable Region Theory: An Introduction to Choosing an Optimal ReactorBy David Ming, David Glasser, Diane Hildebrandt, Benjamin Glasse
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Learn how to effectively interpret, select and optimize reactors for complex reactive systems, using Attainable Region theory
- Teaches how to effectively interpret, select and optimize reactors for complex reactive systems, using Attainable Region (AR) theory
- Written by co-founders and experienced practitioners of the theory
- Covers both the fundamentals of AR theory for readers new to the field, as we all as advanced AR topics for more advanced practitioners for understanding and improving realistic reactor systems
- Includes over 200 illustrations and 70 worked examples explaining how AR theory can be applied to complex reactor networks, making it ideal for instructors and self-study
- Interactive software tools and examples written for the book help to demonstrate the concepts and encourage exploration of the ideas
- Published on: 2016-10-10
- Original language: English
- Number of items: 1
- Dimensions: .0" h x .0" w x .0" l, .0 pounds
- Binding: Hardcover
- 352 pages
From the Back Cover
Learn how to effectively interpret, select and optimize reactors for complex reactive systems, using Attainable Region theory
With so many different reactor types available, and infinitely ways to combine these types together, how should we go about decoding and designing these systems, and how do we know that there are not other designs that could do better?
Attainable Region (AR) theory provides a means of understanding chemical reactor networks from a geometric perspective of reactors. This approach allows us to find all possible outcomes for all possible designs — even the designs we cannot imagine — giving us confidence that what we design is always optimal for a given duty.
Attainable Region Theory: An Introduction to Choosing an Optimal Reactor discusses how to effectively interpret, select and optimize reactors for complex reactive systems, using AR theory. Covering both fundamentals and advanced concepts, this book demonstrates how this approach can lead to powerful insights and discoveries that improve the performance of complex reactor designs.
Written by respected figures on AR research, including co-developers of the founding theory, this textbook features:
- Over 70 worked examples and 200 illustrations, including interactive software tools written in Python, which demonstrate AR theory
- Fundamentals of AR theory to readers without any prior knowledge of chemical reactors or optimization
- Advanced AR topics including construction algorithms, higher dimensional and variable density systems
This book serves as a companion textbook for self-study or a reference for instructors, and may also be used as a module of a larger course on reactor network design and optimization.
David Ming holds a B.Sc. and Ph.D. in chemical engineering from the University of the Witwatersrand, Johannesburg. His research interests involve using AR theory to optimize chemical reactors, including batch reactors, and AR numerical methods.
David Glasser is a Professor of Chemical Engineering and co-director of the Material and Process Synthesis (MaPS) research unit at the University of South Africa (UNISA). He was Head of Department of Chemical Engineering, and Dean of the Faculty of Engineering at University of the Witwatersrand, and is one of the co-founders of AR theory. He holds a B.Sc. in chemical engineering from University of Cape Town, and a Ph.D. in chemical engineering from Imperial College.
Diane Hildebrandt is a Professor of Chemical Engineering and co-Director of the MaPS research unit at UNISA. She was the first woman in South Africa to be appointed a full professor of Chemical Engineering when she was the Unilever Professor of Reaction Engineering at the University of the Witwatersrand, and is also a co-developer of AR theory. She holds a B.Sc., M.Sc. and Ph.D. in chemical engineering from University of the Witwatersrand. Her research area is the reduction of CO2 emissions through the design of energy efficient processes.
Benjamin Glasser is a Professor of Chemical and Biochemical Engineering at Rutgers University, New Jersey, USA. He holds a B.Sc. and M.Sc. in chemical engineering from University of the Witwatersrand, and a Ph.D. in chemical engineering from Princeton University. His research interests include heat and mass transfer, multiphase reactors and particle technology applied to chemical and pharmaceutical manufacturing.
Matthew Metzger is a Senior Scientist at Merck & Co., Inc. He has co-authored over 14 publications, holds a B.S. in chemical engineering from Lafayette University, and a Ph.D. in chemical engineering from Rutgers University.
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