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Monte Carlo Methods in Polymer Reaction Engineering de Joao B. P. Soares

Monte Carlo Methods in Polymer Reaction Engineering

Autor Joao B. P. Soares, Amanda L. T. Brandao, Mohammed Al-Khayyat
en Limba Engleză Hardback – 27 ian 2027
This first practical approach to introduce Monte Carlo methods to those working in polymer science makes use of case studies to present a unified and comprehensive overview of various methods.
The author, one of the leading experts in the field, teaches readers how to program Monte Carlo algorithms to solve their own specific problems in polymer reaction engineering, starting with simple principles and moving on to more complex examples. Additional educational software is available online.
The handbook is aimed at polymer scientists and engineers in academia and industry, but is of equal interest to PhD students.
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Specificații

ISBN-13: 9783527341085
ISBN-10: 3527341080
Pagini: 400
Dimensiuni: 170 x 244 mm
Editura: Wiley-VCH GmbH
Locul publicării:Weinheim, Germany

Public țintă

Polymer Chemists, Chemical Engineers, Ph.D. Students in Material Sciences, Ph.D. Students in Engineering Sciences, Plastics Technologists, Chemical Industry, Materials Scientists, Libraries

Notă biografică

João Soares is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta and has published more than 180 articles in referred journals, written 12 book chapters, and is an author of the book Polyolefin Reaction Engineering published by Wiley-VCH in 2012. He has offered 27 public and 22 in-house industrial short courses all over the world in his areas of expertise. He is the Editor-in-Chief for the Canadian Journal of Chemical Engineering. He is also a member of the Executive Advisory Board of Wiley-VCH Macromolecular journals and responsible for the coordination of Macromolecular Reaction Engineering. Professor Soares is a Fellow of the Chemical Institute of Canada, and a Professional Engineer in the Provinces of Ontario and Alberta. He is the recipient of various scientific awards and consults for several polyolefin-manufacturing companies in Canada, USA, Europe, Asia, and the Middle East.

Mohammed Al-Khayyat is a Research Assistant in the Department of Chemical and Materials Engineering at the University of Alberta, where he obtained his M.Sc. in Chemical Engineering. He has authored several articles in peer-reviewed journals. He specializes in mathematical and computational modeling in chemical engineering, with a particular focus on polymer reaction engineering. His expertise spans Monte Carlo methods, transport phenomena, and computationally efficient polymerization models.
Amanda L. T. Brandão is a Professor in the Department of Chemical and Materials Engineering at the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), where she coordinates the Group of Advanced Applications in Processes (GAAP). She holds a B.Sc. in Chemical Engineering from PUC-Rio and M.Sc. and Ph.D. degrees in Chemical Engineering from COPPE/UFRJ. She has published numerous articles in refereed journals and serves as an Associate Editor of The Canadian Journal of Chemical Engineering. Her research focuses on the modeling, simulation, and optimization of chemical processes, techno-economic analysis, and the integration of data science and machine learning with chemical processes and polymer reaction engineering.
 

Cuprins

1 Preface
 
2 Introduction
2.1 The Uniform Distribution and the Generation of Random Numbers
2.2 Polymerizations are Stochastic Processes
2.3 A Preliminary Monte Carlo Simulation Algorithm
2.4 Chain Length and Molecular Weight Averages
2.5 Final Thoughts
2.6 Extra Readings
 
3 Steady State Monte Carlo Models
3.1 Case Study 3.1: Styrene Free-Radical Polymerization
3.2 Case Study 3.2: Free-Radical Copolymerization of Styrene and Methyl Methacrylate
3.3 Case Study 3.3: Coordination Polymerization with the Formation of Long Chain Branches
3.4 Final Thoughts
3.5 Extra Readings
 
4 Dynamic Monte Carlo Models
4.1 Case Study 4.1 ? Part 1: Step-Growth Polymerization
4.2 Gillespie?s Method: A Stochastic Approach to Dynamic Simulation
4.3 Case Study 4.1 ? Part 2: Gillespie Algorithm for Step-Growth Polymerization
4.4 Case Study 4.1 ? Part 3: Non-Stoichiometric Step-Growth Polymerization
4.5 Reaction Networks and Competing Events
4.6 How to Select the Size of the Control Volume
4.7 Case Study 4.2: Dynamic Styrene Free-Radical Polymerization with Gel Effect
4.8 Case Study 4.3: ATRP Copolymerization of Styrene and n-Butyl Acrylate
4.9 Case Study 4.4: Thermal Degradation of Polystyrene
4.10 Final Thoughts
4.11 Extra Readings
 
5 Optimization of Monte Carlo Methods
5.1 Big O Notation and Computational Complexity
5.2 Compilation versus Interpretation
5.3 Homopolymerization Geometric Sampling
5.4 Copolymerization Geometric Sampling
5.5 Minimizing Random Number Generation Overhead
5.6 Balancing Accuracy and Speed
5.7 Disjoint Set Union for Gelation Tracking
5.8 GPU Parallel Computing
5.9 Final Thoughts
5.10 Extra Readings
 
6 Epilogue: Creating Molecules to Understand Them
6.1 Lessons from Monte Carlo Simulation
6.2 Ten Rules for Writing Monte Carlo Algorithms
6.3 The Expanding Role of Monte Carlo Methods in Polymer Reaction Engineering
6.4 Final Thoughts
 
Appendix A: Instantaneous Chain Length Distribution (CLD) of Polymers Made by Free Radical Polymerization