Introduction to Process Control

2. New Auflage 2012
ISBN: 978-1-4398-5486-0
Verlag: Taylor & Francis Inc

Buch, Englisch, 643 Seiten, Format (B × H): 162 mm x 237 mm, Gewicht: 1004 g

Reihe: Chemical Industries

Introduction to Process Control, Second Edition provides a bridge between the traditional view of process control and the current, expanded role by blending conventional topics with a broader perspective of more integrated process operation, control, and information systems. Updating and expanding the content of its predecessor, this second edition addresses issues in today’s teaching of process control.
Teaching & Learning Principles

Presents a concept first followed by an example, allowing students to grasp theoretical concepts in a practical manner
Uses the same problem in each chapter, culminating in a complete control design strategy
Includes 50 percent more exercises

Content

Defines the traditional and expanded roles of process control in modern manufacturing
Introduces the link between process optimization and process control (optimizing control), including the effect of disturbances on the optimal plant operation, the concepts of steady-state and dynamic backoff as ways to quantify the economic benefits of control, and how to determine an optimal transition policy during a planned production change
Incorporates an introduction to the modern architectures of industrial computer control systems with real case studies and applications to pilot-scale operations
Discusses the expanded role of process control in modern manufacturing, including model-centric technologies and integrated control systems
Integrates data processing/reconciliation and intelligent monitoring in the overall control system architecture

Web Resource The book’s website offers a user-friendly software environment for interactively studying the examples in the text. The site contains the MATLAB® toolboxes for process control education as well as the main simulation examples from the book. Access the site through the authors’ websites at www.pseonline.net and www.chms.ucdavis.edu/research/web/pse/ahmet/

Drawing on the authors’ combined 50 years of teaching experiences, this classroom-tested text is designed for chemical engineering students but is also suitable for industrial practitioners who need to understand key concepts of process control and how to implement them. The authors help readers see how traditional process control has evolved into an integrated operational environment used to run modern manufacturing facilities.

Zielgruppe

Chemical, industrial, process control, manufacturing, and systems engineers and students in these areas as well as any general engineering curriculum.

Autoren/Hrsg.

Romagnoli, Jose A.

Palazoglu, Ahmet

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik, Automatisierung
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Chemische Verfahrenstechnik
• Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik

Inhaltsverzeichnis

INTRODUCTIONWhy Process Control?Historical BackgroundRole of Control in Process IndustriesObjectives of ControlSummaryContinuing ProblemReferences

Definitions and TerminologyConcepts and DefinitionsControl Design ProblemControl System DesignControl Design ProjectSummaryContinuing ProblemReferences

MODELING FOR CONTROLBasic Concepts in ModelingWhy Is Process Modeling Necessary?Classification of ModelsTypes of ModelsDegrees of FreedomModels and ControlSummaryReferences

Development of Models from Fundamental LawsPrinciples of ModelingModels Based on Fundamental LawsModeling of Processes Involving Chemical ReactionsModeling of Complex SystemsDistributed Parameter SystemsNumerical Solution of Model EquationsSummaryContinuing ProblemReferences

Input–Output Models: The Transfer FunctionLinear (Linearized) ModelConcept of Transfer FunctionTransfer Functions of Single-Input Single-Output ProcessesProperties of Transfer FunctionsNonrational Transfer FunctionsSummaryContinuing Problem

Models from Process DataDevelopment of Empirical ModelsModel StructuresProcess Reaction Curve MethodRegression in ModelingSummaryContinuing ProblemReferences

PROCESS ANALYSISStabilityStability of Linear SystemsInput–Output StabilityRouth’s CriterionRoot-Locus MethodDirect Substitution MethodSummaryReferences

Dynamic PerformanceInput TypesFirst-Order ProcessesSecond-Order ProcessesMulticapacity ProcessesEffect of ZerosEffect of Time DelaysSummaryContinuing Problem

Frequency ResponseWhat Is Frequency Response?Complex Numbers in Polar CoordinatesConstruction of Frequency ResponseEvaluation of Frequency ResponseFrequency Response of Common SystemsBode DiagramsNyquist DiagramsSystems in SeriesSummaryContinuing Problem

FEEDBACK CONTROLBasic Elements of Feedback ControlFeedback Control ProblemControl LawClosed-Loop Transfer FunctionsAnalysis of Individual Terms in PID ControllersPractical Issues in PID DesignSummaryContinuing ProblemReference

Stability Analysis of Closed-Loop ProcessesClosed-Loop StabilityRouth’s CriterionRoot-Locus MethodModeling ErrorsFrequency Response MethodsSummaryContinuing Problem

Feedback Control DesignDesign ObjectivesController Tuning TechniquesComparing the MethodsSummaryContinuing ProblemReferences

MODEL-BASED CONTROLModel-Based ControlFeedforward ControlDelay Compensation (Smith Predictor)Internal Model ControlSummaryContinuing ProblemReferences

Model Uncertainty and RobustnessIMC Structure with Model UncertaintyDescription of Model UncertaintyIMC Design under Model UncertaintySummaryReferences

Model Predictive Control General PrinciplesDynamic Matrix ControlProcess ConstraintsState-Space Formulation of MPCSummaryContinuing ProblemReferences

MULTIVARIABLE CONTROLMultivariable Systems: Special CasesCascade ControlRatio ControlSplit-Range ControlOverride ControlSummaryContinuing ProblemReferences

Multivariable SystemsCharacteristics of Multivariable ProcessesModeling of Multivariable ProcessesTransfer Functions of Multivariable ProcessesMultivariable Feedback Control StructureSummaryContinuing ProblemReferences

Design of Multivariable ControllersMultiple-Input–Multiple-Output Feedback AnalysisRGA Interaction MeasureMultiloop Controller DesignDesign of Noninteracting Control Loops: DecouplersSummaryContinuing ProblemReferences

CONTROL IN MODERN MANUFACTURING Practical Control of Nonlinear Processes Operating Regime Modeling ApproachGain-Scheduling ControllerMultimodel Controller DesignSummaryReferences

Process Optimization and ControlProcess OptimizationOptimizing Control of DisturbancesDynamic Optimization and Transition PlanningSummaryReferences

Industrial Control TechnologyEvolution of Industrial Control TechnologyGeneric Industrial Control Systems ArchitectureSummaryContinuing ProblemReferences

Role of Process Control in Modern ManufacturingExpanded Role of Control in Modern ManufacturingModel-Centric TechnologiesIntegrated Control SystemsSummaryReferences

Data Processing and ReconciliationDealing with Missing PointsOutliersCharacterizing Process DataModeling Process DataData ReconciliationIssues in Data Reconciliation

Process MonitoringProcess MonitoringStatistical Process ControlPrincipal Component AnalysisMultivariate Performance MonitoringFault Diagnosis and ClassificationController Performance MonitoringSummaryReferences

Appendix A: LinearizationAppendix B: Laplace TransformationAppendix C: Matrix OperationsAppendix D: Basic Statistics

Index

Additional Reading and Exercises appear at the end of each section.

Über Autor(innen)

Jose A. Romagnoli holds the Cain Chair in Process Systems Engineering in the Department of Chemical Engineering and is the director of the Laboratory for Process Systems Engineering at Louisiana State University. He earned a PhD in chemical engineering from the University of Minnesota. Dr. Romagnoli has authored more than 300 international publications and was awarded the Centenary Medal of Australia for his contributions to chemical engineering. His research covers all aspects of process systems engineering, including data processing and reconciliation, modeling of complex systems, advanced model-based control, intelligent process monitoring and supervision, and plant-wide optimization.
Ahmet Palazoglu is a professor of chemical engineering and materials science at the University of California, Davis. He earned a PhD in chemical engineering from Rensselaer Polytechnic Institute. Dr. Palazoglu has authored more than 150 publications and has taught short courses to academic and industrial audiences on process monitoring applications. His research interests include process control, nonlinear dynamics, process monitoring, and statistical modeling.