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Step-Growth Polymerization Process Modeling and Product Design.

Author: Kevin Seavey; Y A Liu
Publisher: Hoboken : John Wiley & Sons, 2009.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Understand quantitative model step-growth polymerization plans and how to predict properties of the product polymer with the essential information in Step-Growth Polymerization Process Modeling and Product Design . If you want to learn how to simulate step-growth polymerization processes using commercial software and seek an in-depth, quantitative understanding of how to develop, use, and deploy these simulations,  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Kevin Seavey; Y A Liu
ISBN: 9780470292495 0470292490 1282112457 9781282112452 0470238232 9780470238233
OCLC Number: 609835730
Description: 1 online resource (746 pages)
Contents: Step-growth polymerization process modeling and product design; contents; foreword; preface; software selection; acknowledgments; about the authors; 1 introduction; part i: fundamentals and applications of step-growth polymerization process modeling and product design; part ii: modeling step-growth polymerization processes and properties using polymers plus and aspen custom modeler; part iii: advanced topics in step-growth polymerization process modeling and product design; appendix: listing of computer files; glossary; index.
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Abstract:

Understand quantitative model step-growth polymerization plans and how to predict properties of the product polymer with the essential information in Step-Growth Polymerization Process Modeling and Product Design . If you want to learn how to simulate step-growth polymerization processes using commercial software and seek an in-depth, quantitative understanding of how to develop, use, and deploy these simulations, consult this must-have guide. The book focuses on quantitative relationships between key process input variables (KPIVs) and key process output variables (KPOVs), and the integrated.

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