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Multivariable predictive controlappl...

Lahiri, Sandip Kumar, (1970-)

Multivariable predictive controlapplications in industry /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multivariable predictive controlSandip Kumar Lahiri.
Reminder of title:	applications in industry /
Author:	Lahiri, Sandip Kumar,
Published:	Hoboken, NJ :John Wiley & Sons,2017.
Description:	1 online resource (xxxiii, 267 p.)
Subject:	Predictive control.
Online resource:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119243434
ISBN:	9781119243434$q(electronic bk.)

Multivariable predictive controlapplications in industry /
Lahiri, Sandip Kumar,1970-

Multivariable predictive controlapplications in industry /[electronic resource] :Sandip Kumar Lahiri. - 1st ed. - Hoboken, NJ :John Wiley & Sons,2017. - 1 online resource (xxxiii, 267 p.)

Includes bibliographical references and index.

A guide to all practical aspects of building, implementing, managing, and maintaining MPC applications in industrial plants Multivariable Predictive Control: Applications in Industry provides engineers with a thorough understanding of all practical aspects of multivariate predictive control (MPC) applications, as well as expert guidance on how to derive maximum benefit from those systems. Short on theory and long on step-by-step information, it covers everything plant process engineers and control engineers need to know about building, deploying, and managing MPC applications in their companies. MPC has more than proven itself to be one the most important tools for optimising plant operations on an ongoing basis. Companies, worldwide, across a range of industries are successfully using MPC systems to optimise materials and utility consumption, reduce waste, minimise pollution, and maximise production. Unfortunately, due in part to the lack of practical references, plant engineers are often at a loss as to how to manage and maintain MPC systems once the applications have been installed and the consultants and vendors' reps have left the plant. Written by a chemical engineer with two decades of experience in operations and technical services at petrochemical companies, this book fills that regrettable gap in the professional literature. -Provides a cost-benefit analysis of typical MPC projects and reviews commercially available MPC software packages -Details software implementation steps, as well as techniques for successfully evaluating and monitoring software performance once it has been installed -Features case studies and real-world examples from industries, worldwide, illustrating the advantages and common pitfalls of MPC systems -Describes MPC application failures in an array of companies, exposes the root causes of those failures, and offers proven safeguards and corrective measures for avoiding similar failures Multivariable Predictive Control: Applications in Industry is an indispensable resource for plant process engineers and control engineers working in chemical plants, petrochemical companies, and oil refineries in which MPC systems already are operational, or where MPC implementations are being considering.

ISBN: 9781119243434$q(electronic bk.)

LCCN: 2017012540Subjects--Topical Terms:

182316
Predictive control.

LC Class. No.: TJ217.6 / .L34 2017

Dewey Class. No.: 629.8

Multivariable predictive controlapplications in industry /
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245 1 0 $a Multivariable predictive control $h [electronic resource] : $b applications in industry / $c Sandip Kumar Lahiri.
250 $a 1st ed.
260 $a Hoboken, NJ : $b John Wiley & Sons, $c 2017.
300 $a 1 online resource (xxxiii, 267 p.)
504 $a Includes bibliographical references and index.
520 $a A guide to all practical aspects of building, implementing, managing, and maintaining MPC applications in industrial plants Multivariable Predictive Control: Applications in Industry provides engineers with a thorough understanding of all practical aspects of multivariate predictive control (MPC) applications, as well as expert guidance on how to derive maximum benefit from those systems. Short on theory and long on step-by-step information, it covers everything plant process engineers and control engineers need to know about building, deploying, and managing MPC applications in their companies. MPC has more than proven itself to be one the most important tools for optimising plant operations on an ongoing basis. Companies, worldwide, across a range of industries are successfully using MPC systems to optimise materials and utility consumption, reduce waste, minimise pollution, and maximise production. Unfortunately, due in part to the lack of practical references, plant engineers are often at a loss as to how to manage and maintain MPC systems once the applications have been installed and the consultants and vendors' reps have left the plant. Written by a chemical engineer with two decades of experience in operations and technical services at petrochemical companies, this book fills that regrettable gap in the professional literature. -Provides a cost-benefit analysis of typical MPC projects and reviews commercially available MPC software packages -Details software implementation steps, as well as techniques for successfully evaluating and monitoring software performance once it has been installed -Features case studies and real-world examples from industries, worldwide, illustrating the advantages and common pitfalls of MPC systems -Describes MPC application failures in an array of companies, exposes the root causes of those failures, and offers proven safeguards and corrective measures for avoiding similar failures Multivariable Predictive Control: Applications in Industry is an indispensable resource for plant process engineers and control engineers working in chemical plants, petrochemical companies, and oil refineries in which MPC systems already are operational, or where MPC implementations are being considering.
588 $a Description based on online resource; title from digital title page (viewed on August 30, 2017)
650 0 $a Predictive control. $3 182316
650 0 $a Multivariate analysis. $3 181905
856 4 0 $u https://onlinelibrary.wiley.com/doi/book/10.1002/9781119243434