Stochastic Modelling In Process Technology

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E-Book Overview

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 computing the probability distribution for a single particle's position in the process vessel, and/or the particle's properties, as a function of time, rather than - as is traditionally done - basing the model on the formulation and solution of differential conservation equations. Using this technique can greatly simplify the formulation of a model, and even make modelling possible for processes so complex that the traditional method is impracticable. Stochastic modelling has sporadically been used in various branches of process technology under various names and guises. This book gives, as the first, an overview of this work, and shows how these techniques are similar in nature, and make use of the same basic mathematical tools and techniques. The book also demonstrates how stochastic modelling may be implemented by describing example cases, and shows how a stochastic model may be formulated for a case, which cannot be described by formulating and solving differential balance equations.Key Features: - Introduction to stochastic process modelling as an alternative modelling technique - Shows how stochastic modelling may be succesful where the traditional technique fails - Overview of stochastic modelling in process technology in the research literature - Illustration of the principle by a wide range of practical examples - In-depth and self-contained discussions - Points the way to both mathematical and technological research in a new, rewarding field - Introduction to stochastic process modelling as an alternative modelling technique- Shows how stochastic modelling may be succesful where the traditional technique fails- Overview of stochastic modelling in process technology in the research literature- Illustration of the principle by a wide range of practical examples- In-depth and self-contained discussions- Points the way to both mathematical and technological research in a new, rewarding field

E-Book Content

Stochastic Modelling in Process Technology This is volume 211 in MATHEMATICS IN SCIENCE AND ENGINEERING Edited by C.K. Chui, Stanford University A list of recent titles in this series appears at the end of this volume. Stochastic Modelling in Process Technology Herold G. Dehling DEPARTMENT OF MATHEMATICS RUHR-UNIVERSITÄT BOCHUM BOCHUM, GERMANY Timo Gottschalk DEPARTMENT OF MATHEMATICS RUHR-UNIVERSITÄT BOCHUM BOCHUM, GERMANY Alex C. Hoffmann DEPARTMENT OF PHYSICS AND TECHNOLOGY UNIVERSITY OF BERGEN BERGEN, NORWAY ELSEVIER Amsterdam – Boston – Heidelberg – London – New York – Oxford Paris – San Diego – San Francisco – Singapore – Sydney – Tokyo Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands Linacre House, Jordan Hill, Oxford OX2 8DP, U.K. First edition 2007 Copyright © 2007 Elsevier B.V. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone (+44) (0) 1865 843830; fax (+44) (0) 1865 853333; email: