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Stochastic modelling in process technology /

There is an ever increasing need for modelling complex processes reliably. Computational modelling techniques, such as CFD and MD may be used as tools to study specific systems, but their emergence has not decreased the need for generic, analytical process models. Multiphase and multicomponent syste...

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Bibliographic Details
Main Authors: Dehling, Herold. (Author)
Corporate Authors: Elsevier Science & Technology.
Group Author: Gottschalk, Timo.; Hoffmann, Alex C.
Published: Elsevier,
Publisher Address: Amsterdam ; Boston ; London :
Publication Dates: 2007.
Literature type: eBook
Language: English
Edition: First edition.
Series: Mathematics in science and engineering ; v. 211
Subjects:
Manufacturing processes > Mathematical models.
Stochastic models.
Stochastic processes.
TECHNOLOGY & ENGINEERING > Industrial Technology.
TECHNOLOGY & ENGINEERING > Technical & Manufacturing Industries & Trades.
TECHNOLOGY & ENGINEERING > Industrial Engineering.
TECHNOLOGY & ENGINEERING > Manufacturing.
Electronic books.
Online Access: http://www.sciencedirect.com/science/bookseries/00765392/211
Summary: There is an ever increasing need for modelling complex processes reliably. Computational modelling techniques, such as CFD and MD may be used as tools to study specific systems, but their emergence has not decreased the need for generic, analytical process models. Multiphase and multicomponent systems, and high-intensity processes displaying a highly complex behaviour are becoming omnipresent in the processing industry. This book discusses an elegant, but little-known technique for formulating process models in process technology: stochastic process modelling. The technique is based on computi.
Carrier Form: 1 online resource (x, 279 pages) : illustrations.
Bibliography: Includes bibliographical references (pages 263-274) and index.
ISBN: 0080548970
9780080548975
Index Number: TS183
CLC: TH16
Contents: Front Cover; Stochastic Modelling in Process Technology; Copyright Page; Preface; Table of Contents; Chapter 1 Modeling in Process Technology; Chapter 2 Principles of Stochastic Process modeling; Chapter 3 Batch Fluidized Beds; Chapter 4 Continuous Systems and RTD; Chapter 5 RTD in Continuous Fluidized Beds; Chapter 6 Mixing and Reactions; Chapter 7 Particle Size Manipulation; Chapter 8 Multiphase Systems; Chapter 9 Diffusion Limits; Appendix A Equations for RTD in CSTR and DPF; Bibliography; Index; Mathematics in Science and Engineering.

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