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.

Of particular interest to the petrochemicals industry Constant relation of scientific results to industrial application The book was written in response to a demand for the material that it contains

Klappentext

The work in this book entails the development of non-linear model-based multivariable control algorithms and strategies and their use in an integrated approach to control strategy, which incorporates a process model, an inferential model and a multivariable control algorithm in one framework. This integrated approach has been applied to various refinery processes that exhibit strong non-linearities, process interactions and constraints and has been shown to produce good results by improving closed-loop quality control and maximising the yield of high-value products. The non-linear model-based control structure is further extended to permit the use of inferential models in non-linear multivariable control applications. A wide range of inferential models has been developed, implemented in real-time and integrated with non-linear multivariable control applications. These inferential models demonstrate the improvement in the performance of closed-loop quality control and the dynamic response of the system in reducing long time delays. A comlex multivariable control problem is solved by formulating the non-linear, constrained optimisation strategy for a crude distillation and a semi-regenerative catalytic reforming process. A non-linear constrained optimisation strategy is proposed and applied to a fluid catalytic cracking reactor-regenerator section using a simplified fluid-catalytic-cracking-process model. A dynamic parameter update algorithm is developed and used to reduce the effect of larger modelling errors by updating the selected model parameters regularly.This book was brought about, primarily, in response to industrial interest in the improvement of operating efficiency and profitability using the non-linear model-based technology which it discusses. A second motivation of more academic interest was the implementation of model-based methods in real-time for control of complex processes with strong non-linearities and process interactions and a third, more practical, was the reduction of the gap between theoretical work and the industrisl practice of advanced process control.

Inhalt
1 Introduction.- 1.1 Non-linear Model-based Control.- 1.2 Motivation for this Book.- 1.3 Objectives and Contributions.- 1.4 Scope of the Book.- 1.5 Book Overview.- 2 Literature Review.- 2.1 Introduction.- 2.2 Model-predictive Control.- 2.3 Internal Model Control (IMC).- 2.4 Stability and Robustness of Linear MPC.- 2.5 Non-linear Model-based Control (NMBC).- 2.6 Generic Model Control (GMC).- 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.- 3.3 On-line Applications of Inferential Models.- 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.- 4.3 The Non-linear GMC Control Law.- 4.4 GMC Application to Debutanizer.- 4.5 Model Development.- 4.6 Controller Implementation.- 4.7 Results and Discussions.- 4.8 Cost/Benefit Analysis.- 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.- 5.3 Non-linear Control Algorithm for Fractionator.- 5.4 Model Parameter Update.- 5.5 Model-predictive Control.- 5.6 Results and Discussions.- 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.- 6.4 Application to Catalytic Reforming Process.- 6.5 Real-time Implementation.- 6.6 Conclusions.- 7 Non-linear Multivariable Control of a Fluid Catalytic Cracking Process.- 7.1 Introduction.- 7.2 FCC Process Control Overview.- 7.3 Dynamic Model of FCC Process.- 7.4 Non-linear Control Algorithm for FCC Reactor-Regenerator System.- 7.5 DynamicModel Parameter Update.- 7.6 Model-predictive Control.- 7.7 Real-time Implementation.- 7.8 Plant Results.- 7.9 Conclusions.- 8 Conclusions and Recommendations.- 8.1 Conclusions.- 8.2 Recommendations.- Appendix A.- A.1 Programme for Pressure-compensated Temperature.- A.2 Programme for Naphtha-final-boiling-point Inferential Model.- A.3 Theory Underlying the Pressure-compensated Temperature.- Appendix B.- B.1 S-B GMC Controller Implementation.- Appendix C.- Constrained Multivariable Control System Programme for Shell Heavy Oil Fractionator.- Appendix D.- D.1 Description and Application of Real-time Optimization (RT-Opt.) Software to Catalytic Reforming Reactor Section.- D.1.1 Description.- D.1.2 Mathematical Algorithm.- D.1.3 Application to Catalytic Reforming Reactor Section.- D.2 Implementation Procedure of Real-time Optimization (RT-Opt.).- Appendix E.- Constrained Multivariable Predictive Control for Fluid Catalytic Cracking (FCC) Process.- References.