The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work…mehr
The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The last decade has seen considerable interest in reviving the fortunes of non linear control. In contrast to the approaches of the 60S, 70S and 80S a very pragmatic agenda for non-linear control is being pursued using the model-based predictive control paradigm. This text by R. Ansari and M. Tade gives an excellent synthesis of this new direction. Two strengths emphasized by the text are: (i) four applications found in refinery processes are used to give the text a firm practical continuity; (ii) a non-linear model-based control architecture is used to give the method a coherent theoretical framework.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
1 Introduction.- 1.1 Non-linear Model-based Control.- 1.2 Motivation for this Book.- 1.3 Objectives and Contributions.- 1.3.1 Objectives.- 1.3.2 Contributions.- 1.3.2.1 Non-linear Control Theory and Development.- 1.3.2.1 Practical Applications in Industries.- 1.4 Scope of the Book.- 1.5 Book Overview.- 2 Literature Review.- 2.1 Introduction.- 2.1.1 Industrial Background.- 2.1.2 Academic Background.- 2.2 Model-predictive Control.- 2.2.1 Dynamic Matrix Control (DMC).- 2.2.2 Limitations of DMC.- 2.2.3 Model Algorithm Control (MAC).- 2.2.4 Difference Between DMC and MAC.- 2.2.5 Principal Component Analysis (PCA).- 2.3 Internal Model Control (IMC).- 2.3.1 IMC Theoretical Background.- 2.3.2 Comparison of IMC with DMC and MAC.- 2.3.3 Extensions and Variants of IMC.- 2.4 Stability and Robustness of Linear MPC.- 2.5 Non-linear Model-based Control (NMBC).- 2.5.1 Introduction.- 2.5.2 Non-linear Model-based Control Architecture.- 2.5.2.1 NMBC-Model.- 2.5.2.2 NMBC-Model Parameter Update.- 2.5.2.3 NMBC- Controller Configuration/Simulation.- 2.5.3 Non-linear Control System Technique.- 2.5.4 Non-linear Programming Methods.- 2.6 Generic Model Control (GMC).- 2.6.1 Introduction.- 2.6.2 GMC and Internal Model Control (IMC).- 2.6.3 GMC and Model-predictive Control (MPC).- 2.6.3.1 Discrete form of GMC.- 2.6.3.2 Relationship between Discrete GMC and MPC.- 2.7 Stability and Robustness of Non-linear System.- 2.8 Conclusions and Discussion.- 3 Inferential Models In Non-linear Multivariable Control Applications.- 3.1 Introduction.- 3.2 Development of Inferential Models.- 3.2.1 Overview of Models.- 3.2.2 Non-linear Inferential Control Model.- 3.2.3 Correlation-Based Model.- 3.2.4 Verification of Correlation-Based Model.- 3.3 On-line Applications of Inferential Models.- 3.3.1 Naphtha Final Boiling Point (FBP) of Crude Distillation.- 3.3.2 Kerosene Flash Point of Crude Distillation.- 3.3.3 Reid Vapour Pressure (RVP) of Debutanizer Bottom.- 3.3.4 Iso-Pentane of Debutanizer Overhead.- 3.3.5 Octane Inferential Model for Catalytic Reforming.- 3.4 Tuning of Inferential Models.- 3.5 Inferential Models in Non-linear Multivariable Control Applications.- 3.6 Benefits of Inferential Models.- 3.7 Conclusions.- 4 Non-linear Model-based Multivariable Control of a Debutanizer.- 4.1 Introduction.- 4.2 The Debutanizer Control Strategy.- 4.2.1 Objective.- 4.2.2 Process Description.- 4.2.3 Control Proposal.- 4.2.4 Hardware Consideration.- 4.3 The Non-linear GMC Control Law.- 4.4 GMC Application to Debutanizer.- 4.5 Model Development.- 4.5.1 Steady-State Model Considerations.- 4.5.2 Development of Inferential Models.- 4.5.2.1 RVP of Platformer Feed.- 4.5.2.2 Iso-pentane of the Debutanizer Overhead.- 4.6 Controller Implementation.- 4.6.1 Controller-Process Interface.- 4.6.2 Non-linear Controller Tuning.- 4.7 Results and Discussions.- 4.8 Cost/Benefit Analysis.- 4.8.1 Benefits Calculations.- 4.9 Conclusions.- 5 Non-linear Model-based Multivariable Control of a Crude Distillation Process.- 5.1 Introduction.- 5.2 Crude Distillation Process Control Overview.- 5.2.1 Process Description.- 5.2.2 Control Objectives and Constraints.- 5.2.2.1 Control Objectives.- 5.2.2.2 Control Constraints.- 5.2.3 Dynamic Model with Uncertainty.- 5.3 Non-linear Control Algorithm for Fractionator.- 5.4 Model Parameter Update.- 5.5 Model-predictive Control.- 5.5.1 Problem Formulation for Linear Control.- 5.5.2 MATLAB®/SIMULINK® Programme.- 5.6 Results and Discussions.- 5.6.1 Simulation Results.- 5.6.2 Integrating Top End Point Inferential Model.- 5.6.3 Real time Implementation Results.- 5.7 Conclusions.- 6 Constrained Non-linear Multivariable Control of a Catalytic Reforming Process.- 6.1 Introduction.- 6.2 Process Constraints Classifications.- 6.3 Constraint Non-linear Multivariable Control.- 6.3.1 Control Theory and Design.- 6.3.2 Selection of Design Parameters.- 6.3.2.1 Selection of K1C and K2C.- 6.3.2.2 Selection of W.- 6.4 Application to Catalytic Reforming Process.- 6.4.1 Dynamic Model of the P
1 Introduction.- 1.1 Non-linear Model-based Control.- 1.2 Motivation for this Book.- 1.3 Objectives and Contributions.- 1.3.1 Objectives.- 1.3.2 Contributions.- 1.3.2.1 Non-linear Control Theory and Development.- 1.3.2.1 Practical Applications in Industries.- 1.4 Scope of the Book.- 1.5 Book Overview.- 2 Literature Review.- 2.1 Introduction.- 2.1.1 Industrial Background.- 2.1.2 Academic Background.- 2.2 Model-predictive Control.- 2.2.1 Dynamic Matrix Control (DMC).- 2.2.2 Limitations of DMC.- 2.2.3 Model Algorithm Control (MAC).- 2.2.4 Difference Between DMC and MAC.- 2.2.5 Principal Component Analysis (PCA).- 2.3 Internal Model Control (IMC).- 2.3.1 IMC Theoretical Background.- 2.3.2 Comparison of IMC with DMC and MAC.- 2.3.3 Extensions and Variants of IMC.- 2.4 Stability and Robustness of Linear MPC.- 2.5 Non-linear Model-based Control (NMBC).- 2.5.1 Introduction.- 2.5.2 Non-linear Model-based Control Architecture.- 2.5.2.1 NMBC-Model.- 2.5.2.2 NMBC-Model Parameter Update.- 2.5.2.3 NMBC- Controller Configuration/Simulation.- 2.5.3 Non-linear Control System Technique.- 2.5.4 Non-linear Programming Methods.- 2.6 Generic Model Control (GMC).- 2.6.1 Introduction.- 2.6.2 GMC and Internal Model Control (IMC).- 2.6.3 GMC and Model-predictive Control (MPC).- 2.6.3.1 Discrete form of GMC.- 2.6.3.2 Relationship between Discrete GMC and MPC.- 2.7 Stability and Robustness of Non-linear System.- 2.8 Conclusions and Discussion.- 3 Inferential Models In Non-linear Multivariable Control Applications.- 3.1 Introduction.- 3.2 Development of Inferential Models.- 3.2.1 Overview of Models.- 3.2.2 Non-linear Inferential Control Model.- 3.2.3 Correlation-Based Model.- 3.2.4 Verification of Correlation-Based Model.- 3.3 On-line Applications of Inferential Models.- 3.3.1 Naphtha Final Boiling Point (FBP) of Crude Distillation.- 3.3.2 Kerosene Flash Point of Crude Distillation.- 3.3.3 Reid Vapour Pressure (RVP) of Debutanizer Bottom.- 3.3.4 Iso-Pentane of Debutanizer Overhead.- 3.3.5 Octane Inferential Model for Catalytic Reforming.- 3.4 Tuning of Inferential Models.- 3.5 Inferential Models in Non-linear Multivariable Control Applications.- 3.6 Benefits of Inferential Models.- 3.7 Conclusions.- 4 Non-linear Model-based Multivariable Control of a Debutanizer.- 4.1 Introduction.- 4.2 The Debutanizer Control Strategy.- 4.2.1 Objective.- 4.2.2 Process Description.- 4.2.3 Control Proposal.- 4.2.4 Hardware Consideration.- 4.3 The Non-linear GMC Control Law.- 4.4 GMC Application to Debutanizer.- 4.5 Model Development.- 4.5.1 Steady-State Model Considerations.- 4.5.2 Development of Inferential Models.- 4.5.2.1 RVP of Platformer Feed.- 4.5.2.2 Iso-pentane of the Debutanizer Overhead.- 4.6 Controller Implementation.- 4.6.1 Controller-Process Interface.- 4.6.2 Non-linear Controller Tuning.- 4.7 Results and Discussions.- 4.8 Cost/Benefit Analysis.- 4.8.1 Benefits Calculations.- 4.9 Conclusions.- 5 Non-linear Model-based Multivariable Control of a Crude Distillation Process.- 5.1 Introduction.- 5.2 Crude Distillation Process Control Overview.- 5.2.1 Process Description.- 5.2.2 Control Objectives and Constraints.- 5.2.2.1 Control Objectives.- 5.2.2.2 Control Constraints.- 5.2.3 Dynamic Model with Uncertainty.- 5.3 Non-linear Control Algorithm for Fractionator.- 5.4 Model Parameter Update.- 5.5 Model-predictive Control.- 5.5.1 Problem Formulation for Linear Control.- 5.5.2 MATLAB®/SIMULINK® Programme.- 5.6 Results and Discussions.- 5.6.1 Simulation Results.- 5.6.2 Integrating Top End Point Inferential Model.- 5.6.3 Real time Implementation Results.- 5.7 Conclusions.- 6 Constrained Non-linear Multivariable Control of a Catalytic Reforming Process.- 6.1 Introduction.- 6.2 Process Constraints Classifications.- 6.3 Constraint Non-linear Multivariable Control.- 6.3.1 Control Theory and Design.- 6.3.2 Selection of Design Parameters.- 6.3.2.1 Selection of K1C and K2C.- 6.3.2.2 Selection of W.- 6.4 Application to Catalytic Reforming Process.- 6.4.1 Dynamic Model of the P
Rezensionen
"The book covers many topics. ... The book is largely self-contained. It may be useful for the academic control community; also it can serve as a concise reference for technicians interested in the application of nonlinear process control theory related to the petroleum refining industry." (I.Randvee, zbMATH 0953.93006, 2022)
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