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Batch Processing Systems Engineering pp 259–273Cite as

Tendency Models for Estimation, Optimization and Control of Batch Processes

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Part of the book series: NATO ASI Series ((NATO ASI F,volume 143))

Abstract

This paper summarizes recent progress in the area of estimation and control of batch processes. The task of designing effective strategies for the estimation of unmeasured variables and for the control of the important outputs of the process is linked to our need to optimize the process and its success is depended upon the availability of a process model. For this reason we will provide a substantial focus on the modeling issues that relate to batch processes. In particular we will focus attention on the approach developed in our group and referred to as “tendency modeling” that can be used for the estimation, optimization and control of batch processes. Several batch reactor example processes will be detailed to illustrate the applicability of the general approach. These relate to organic synthesis reactors and bioreactors. The point that distinguishes tendency modeling from other modeling approaches is that the developed Tendency Models are multivariable, nonlinear, and aim to incorporate all the available fundamental information about the process through the use of material and energy balances. These models are not frozen in time as they are allowed to evolve. Because they are not perfectly accurate they are used in the optimization, estimation and control of the process on a tentative basis as they are updated either between batches or more frequently. This iterative or adaptive modeling strategy also influences the controller design. The controller performance requirements and thus the need of a more accurate model increase as successive optimization steps guide the process operation near its constraints.

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Author information

Authors and Affiliations

  1. Chemical Process Modeling and Control Research Center and Department of Chemical Engineering, Lehigh University, Bethlehem, PA, 19015, USA

    Christos Georgakis

Authors
  1. Christos Georgakis
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Editor information

Editors and Affiliations

  1. School of Chemical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Ginraras V. Reklaitis

  2. Department of Chemical Engineering, College of Engineering University of South Florida, 4202 East Fowler Avenue, ENG 118, Tampa, FL, 33620-5350, USA

    Aydin K. Sunol

  3. Laboratory for Technical Chemistry, Eidgenössische Technische Hochschule (ETH) Zürich, Switzerland

    David W. T. Rippin

  4. Department of Chemical Engineering, Boğaziçi University, TR-80815, Bebek-Istanbul, Turkey

    Öner Hortaçsu

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© 1996 Springer-Verlag Berlin Heidelberg

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Georgakis, C. (1996). Tendency Models for Estimation, Optimization and Control of Batch Processes. In: Reklaitis, G.V., Sunol, A.K., Rippin, D.W.T., Hortaçsu, Ö. (eds) Batch Processing Systems Engineering. NATO ASI Series, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60972-5_12

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  • DOI: https://doi.org/10.1007/978-3-642-60972-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64635-5

  • Online ISBN: 978-3-642-60972-5

  • eBook Packages: Springer Book Archive

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