Stochastic Modelling in Process Technology
( Volume 211 )
Publication series ：Volume 211
Author： Dehling Herold G.;Gottschalk Timo;Hoffmann Alex C.
Publisher： Elsevier Science
Publication year： 2007
E-ISBN: 9780080548975
P-ISBN(Paperback): 9780444520265
P-ISBN(Hardback): 9780444520265
Subject： O175.1 Ordinary Differential Equations;O29 applied mathematics
Language： ENG
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Description
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
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