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STOCHASTIC MODELLING PROCESS TECHNO

Name: STOCHASTIC MODELLING PROCESS TECHNO
Brand: Elsevier
SKU: 9780444520265
Price: 12221.00 INR
Availability: InStock
ISBN: 9780444520265

STOCHASTIC MODELLING IN PROCES

Contributor(s): DEHLING ET AL Author Format: Hardback

Hardback · ISBN 9780444520265

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Key Specifications

ISBN	9780444520265
Subtitle	STOCHASTIC MODELLING IN PROCES
Publisher	Elsevier
Publication date	July 2007
Language	English
MRP	£87.00 GBP
Page count	292
Item weight	129 g

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.

STOCHASTIC MODELLING PROCESS TECHNO

Key Specifications

DEHLING ET AL Author

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