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Stochastic Modelling in Process Technology

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Title: Stochastic Modelling in Process Technology
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 a stochastic model may be formulated for a case, which cannot be described by formulating and solving differential balance equations. - 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
Authors: Herold G. Dehling, Timo Gottschalk, Alex C. Hoffmann
Resource Type: eBook.
Subjects: Stochastic models, Manufacturing processes--Mathematical models
Categories: MATHEMATICS / Differential Equations / Ordinary, MATHEMATICS / Applied
Database: eBook Collection (EBSCOhost)
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An: 203383
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  Data: Stochastic Modelling in Process Technology
– Name: Abstract
  Label: Description
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  Data: 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. - 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
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  Data: <searchLink fieldCode="AR" term="%22Herold+G%2E+Dehling%22">Herold G. Dehling</searchLink><br /><searchLink fieldCode="AR" term="%22Timo+Gottschalk%22">Timo Gottschalk</searchLink><br /><searchLink fieldCode="AR" term="%22Alex+C%2E+Hoffmann%22">Alex C. Hoffmann</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+processes--Mathematical+models%22">Manufacturing processes--Mathematical models</searchLink>
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RecordInfo BibRecord:
  BibEntity:
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      – Code: 670.15118
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Stochastic models
        Type: general
      – SubjectFull: Manufacturing processes--Mathematical models
        Type: general
    Titles:
      – TitleFull: Stochastic Modelling in Process Technology
        Type: main
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          Name:
            NameFull: Herold G. Dehling
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            NameFull: Timo Gottschalk
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            NameFull: Alex C. Hoffmann
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            NameFull: Herold G. Dehling
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            NameFull: Timo Gottschalk
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            NameFull: Alex C. Hoffmann
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2007
            – D: 04
              M: 02
              Type: profile
              Y: 2014
          Identifiers:
            – Type: isbn-print
              Value: 9780444520265
            – Type: isbn-electronic
              Value: 9780080548975
          Numbering:
            – Type: volume
              Value: 00211
          Titles:
            – TitleFull: Stochastic Modelling in Process Technology
              Type: main
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