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221028 ||| eng |
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|a 0080548970
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|a 9780080548975
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|a TS183
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|a Dehling, Herold
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| 245 |
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|a Stochastic modelling in process technology
|c Herold G. Dehling, Timo Gottschalk, Alex C. Hoffmann
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| 246 |
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|a Stochastic modeling in process technology
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| 250 |
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|a 1st ed
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| 260 |
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|a Amsterdam
|b Elsevier
|c 2007, 2007
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| 300 |
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|a x, 279 pages
|b illustrations
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| 505 |
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|a Includes bibliographical references (pages 263-274) and index
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|a 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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| 653 |
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|a TECHNOLOGY & ENGINEERING / Industrial Technology / bisacsh
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| 653 |
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|a TECHNOLOGY & ENGINEERING / Manufacturing / bisacsh
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| 653 |
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|a Modèles stochastiques
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| 653 |
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|a TECHNOLOGY & ENGINEERING / Technical & Manufacturing Industries & Trades / bisacsh
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| 653 |
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|a Manufacturing processes / Mathematical models / fast / (OCoLC)fst01008179
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| 653 |
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|a Manufacturing processes / Mathematical models
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| 653 |
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|a Fabrication / Modèles mathématiques
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| 653 |
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|a TECHNOLOGY & ENGINEERING / Industrial Engineering / bisacsh
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| 653 |
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|a Stochastic models / http://id.loc.gov/authorities/subjects/sh2005004376
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| 653 |
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|a Stochastic models / fast / (OCoLC)fst01737780
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| 700 |
1 |
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|a Gottschalk, Timo
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| 700 |
1 |
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|a Hoffmann, Alex C.
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b ZDB-1-ELC
|a Elsevier eBook collection Mathematics
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| 490 |
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|a Mathematics in science and engineering
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| 776 |
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|z 0444520260
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| 776 |
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|z 9780444520265
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| 776 |
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|z 9780080548975
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| 776 |
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|z 0080548970
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| 856 |
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|u https://www.sciencedirect.com/science/bookseries/00765392/211
|x Verlag
|3 Volltext
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| 082 |
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|a 670.15118
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| 520 |
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|a 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
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