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Modeling and control of batch proces...

Mhaskar, Prashant.

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Modeling and control of batch processes = theory and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling and control of batch processes/ by Prashant Mhaskar, Abhinav Garg, Brandon Corbett.
Reminder of title:	theory and applications /
Author:	Mhaskar, Prashant.
other author:	Garg, Abhinav.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xxvi, 335 p. :ill., digital ;24 cm.
[NT 15003449]:	Motivation -- Part I: First-Principles Model Based Control -- Part II: Integrating Multi-Model Dynamics With PLS Based Approaches -- Part III: Subspace Identification Based Modeling Approach for Batch Processes.
Contained By:	Springer eBooks
Subject:	Process control. -
Online resource:	https://doi.org/10.1007/978-3-030-04140-3
ISBN:	9783030041403

Modeling and control of batch processes = theory and applications /
Mhaskar, Prashant.

Modeling and control of batch processestheory and applications /[electronic resource] :by Prashant Mhaskar, Abhinav Garg, Brandon Corbett. - Cham :Springer International Publishing :2019. - xxvi, 335 p. :ill., digital ;24 cm. - Advances in industrial control,1430-9491. - Advances in industrial control..

Motivation -- Part I: First-Principles Model Based Control -- Part II: Integrating Multi-Model Dynamics With PLS Based Approaches -- Part III: Subspace Identification Based Modeling Approach for Batch Processes.

Modeling and Control of Batch Processes presents state-of-the-art techniques ranging from mechanistic to data-driven models. These methods are specifically tailored to handle issues pertinent to batch processes, such as nonlinear dynamics and lack of online quality measurements. In particular, the book proposes: a novel batch control design with well characterized feasibility properties; a modeling approach that unites multi-model and partial least squares techniques; a generalization of the subspace identification approach for batch processes; and applications to several detailed case studies, ranging from a complex simulation test bed to industrial data. The book's proposed methodology employs statistical tools, such as partial least squares and subspace identification, and couples them with notions from state-space-based models to provide solutions to the quality control problem for batch processes. Practical implementation issues are discussed to help readers understand the application of the methods in greater depth. The book includes numerous comments and remarks providing insight and fundamental understanding into the modeling and control of batch processes. Modeling and Control of Batch Processes includes many detailed examples of industrial relevance that can be tailored by process control engineers or researchers to a specific application. The book is also of interest to graduate students studying control systems, as it contains new research topics and references to significant recent work. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

ISBN: 9783030041403

Standard No.: 10.1007/978-3-030-04140-3doiSubjects--Topical Terms:

634598
Process control.

LC Class. No.: TS156.8 / .M43 2019

Dewey Class. No.: 670.427

Modeling and control of batch processes = theory and applications /
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245 1 0 $a Modeling and control of batch processes $h [electronic resource] : $b theory and applications / $c by Prashant Mhaskar, Abhinav Garg, Brandon Corbett.
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490 1 $a Advances in industrial control, $x 1430-9491
505 0 $a Motivation -- Part I: First-Principles Model Based Control -- Part II: Integrating Multi-Model Dynamics With PLS Based Approaches -- Part III: Subspace Identification Based Modeling Approach for Batch Processes.
520 $a Modeling and Control of Batch Processes presents state-of-the-art techniques ranging from mechanistic to data-driven models. These methods are specifically tailored to handle issues pertinent to batch processes, such as nonlinear dynamics and lack of online quality measurements. In particular, the book proposes: a novel batch control design with well characterized feasibility properties; a modeling approach that unites multi-model and partial least squares techniques; a generalization of the subspace identification approach for batch processes; and applications to several detailed case studies, ranging from a complex simulation test bed to industrial data. The book's proposed methodology employs statistical tools, such as partial least squares and subspace identification, and couples them with notions from state-space-based models to provide solutions to the quality control problem for batch processes. Practical implementation issues are discussed to help readers understand the application of the methods in greater depth. The book includes numerous comments and remarks providing insight and fundamental understanding into the modeling and control of batch processes. Modeling and Control of Batch Processes includes many detailed examples of industrial relevance that can be tailored by process control engineers or researchers to a specific application. The book is also of interest to graduate students studying control systems, as it contains new research topics and references to significant recent work. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.
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