Nonlinear Model Based Process Control

The ASI on Nonlinear Model Based Process Control (August 10-20, 1997~ Antalya - Turkey) convened as a continuation of a previous ASI which was held in August 1994 in Antalya on Methods of Model Based Process Control in a more general context. In 1994, the contributions and discussions convincingly showed that industrial process control would increasingly rely on nonlinear model based control systems. Therefore, the idea for organizing this ASI was motivated by the success of the first one, the enthusiasm expressed by the scientific community for continuing contact, and the growing incentive for on-line control algorithms for nonlinear processes. This is due to tighter constraints and constantly changing performance objectives that now force the processes to be operated over a wider range of conditions compared to the past, and the fact that many of industrial operations are nonlinear in nature. The ASI intended to review in depth and in a global way the state-of-the-art in nonlinear model based control. The list of lecturers consisted of 12 eminent scientists leading the principal developments in the area, as well as industrial specialists experienced in the application of these techniques. Selected out of a large number of applications, there was a high quality, active audience composed of 59 students from 20 countries. Including family members accompanying the participants, the group formed a large body of92 persons. Out of the 71 participants, 11 were from industry. Front Matter....Pages i-xiv Front Matter....Pages 1-1 Practical Approaches to Nonlinear Control....Pages 3-32 Multiple Model Adaptive Control (MMAC)....Pages 33-57 Self-Scheduling MPC Using LPV Models....Pages 59-84 Front Matter....Pages 85-85 Insights into the Relationships Between Linear and Nonlinear Model Based Control and Issues for Further Research....Pages 87-114 Nonlinear Model-Based Control of Nonminimum-Phase Processes....Pages 115-141 Nonlinear Model-Algorithmic Control: A Review and New Developments....Pages 143-171 Windup and Directionality Compensation in Nonlinear Model-Based Control....Pages 173-208 Internally Stable Linear and Nonlinear Algorithmic Internal Model Control of Unstable Systems....Pages 209-233 Approximate I/O-Linearization of Nonlinear Systems....Pages 235-274 Elementary Nonlinear Decoupling (END), A General Approach to Model Based Control of Nonlinear Multivariable Processes....Pages 275-310 Control of Nonlinear Differential Algebraic Equation Systems : An Overview....Pages 311-344 Promises and Limitations of Functional Expansions in Nonlinear Model-Based Control....Pages 345-369 Nonlinear Feedback Control of Parabolic PDE Systems....Pages 371-399 Front Matter....Pages 401-401 Contractive Model Predictive Control with Local Linearization for Nonlinear Systems....Pages 403-431 Feedback Linearization + Contractive MPC: Stability Analysis / Application to a Polymerization Process....Pages 433-464 Nonlinear Model Predictive Control Schemes with Guaranteed Stability....Pages 465-494 A Computationally Efficient Nonlinear Model Predictive Control Algorithm with Guaranteed Stability....Pages 495-511 Optimization Approaches to Control-Integrated Design of Industrial Batch Reactors....Pages 513-551 Front Matter....Pages 553-553 Software Sensors and Adaptive Linearizing Control of Bioreactors....Pages 555-597 Input Sequences for Nonlinear Modeling....Pages 599-621 Front Matter....Pages 553-553 Towards Multiscale Dynamic Data Reconciliation....Pages 623-665 Multi-Scale Aspects in Linear and Nonlinear Estimation and Control....Pages 667-734 Front Matter....Pages 735-735 Nonlinear Control with Linear Controllers: Transformations, Calculated Gains and Model Scheduling....Pages 737-748 Control of a Steam Boiler by Elementary Nonlinear Decoupling (END)....Pages 749-780 Reduction of PVC Batch Time by Optimal Control of Free Radical Concentration....Pages 781-803 Kappa Number Profile Control for Continuous Digesters....Pages 805-829 Artificial Neural Networks for Nonlinear Control of Industrial Processes....Pages 831-869 Back Matter....Pages 871-896 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 The increasingly competitive environment within which modern industry has to work means that processes have to be operated over a wider range of conditions in order to meet constantly changing performance targets. Add to this the fact that many industrial operations are nonlinear, and the need for on-line control algorithms for nonlinear processes becomes clear. Major progress has been booked in constrained model-based control and important issues of nonlinear process control have been solved. The present book surveys the state of the art in nonlinear model-based control technology, by writers who have actually created the scientific profile. A broad range of issues are covered in depth, from traditional nonlinear approaches to nonlinear model predictive control, from nonlinear process identification and state estimation to control-integrated design. Recent advances in the control of inverse response and unstable processes are presented. Comparisons with linear control are given, and case studies are used for illustration. The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ...