1 edition of Nonlinear Model-based Process Control found in the catalog.
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.
|Statement||by Rashid M. Ansari, Moses O. Tadé|
|Series||Advances in Industrial Control, Advances in industrial control|
|Contributions||Tadé, Moses O.|
|The Physical Object|
|Format||[electronic resource] :|
|Pagination||1 online resource (xxiii, 232p. 83 illus.)|
|Number of Pages||232|
|ISBN 10||1447111923, 144710739X|
|ISBN 10||9781447111924, 9781447107392|
Nonlinear modelling and neural networks for industrial applications - process modelling, process development, materials development, process control, process simulation, process optimisation A site with frames may be visible here. Nonlinear Solutions Oy has provided solutions based on nonlinear models (particularly neural network based) since. Nonlinear Process Control: Michael A. Henson, Dale E. Seborg: Books - at: Hardcover. Nonlinear control theory is the area of control theory which deals with systems that are nonlinear, time-variant, or l theory is an interdisciplinary branch of engineering and mathematics that is concerned with the behavior of dynamical systems with inputs, and how to modify the output by changes in the input using feedback, feedforward, or signal filtering.
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Out of 5 stars Nonlinear model-based Process Control Reviewed in the United States on It is an excellent book which provides model-based process control applications to important refinery by: The ASI on Nonlinear Model Based Process Control (August~ Antalya - Turkey) convened as a continuation of a previous ASI which was held in August in Antalya on Methods of Model Based Process Control in a more general : Paperback.
The ASI on Nonlinear Model Based Process Control (August~ Antalya - Turkey) convened as a continuation of a previous ASI which was held in August in Antalya on Methods of Model Based Process Control in a more general context.
In. 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. The ASI on Nonlinear Model Based Process Control (August~ Antalya - Turkey) convened as a continuation of a previous ASI which was held in August in Antalya on Methods of Model Based Process Control in a more general context.
Nonlinear Process Control reflects the contributions of eleven leading researchers in the field. It is an ideal textbook for graduate courses Nonlinear Model-based Process Control book process control, as well as a concise, up-to-date reference for control by: Nonlinear Model-based Process Control: Applications in Petroleum Refining Rashid M.
Ansari PhD, Moses O. Tadé PhD (auth.) 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.
Nonlinear Model Based Process Control B. Bequette (auth.), Ridvan Berber, Costas Kravaris (eds.) The ASI on Nonlinear Model Based Process Control (August~ Antalya - Turkey) convened as a continuation of a previous ASI which was held in August in Antalya on Methods of Model Based Process Control in a more general context.
The nonlinear model set is based on a convex combination of two bounding linear models. An optimal control sequence is computed for each of the two bounding models. The proposed control algorithm. Nonlinear Process Control will be of particular interest to industrial practitioners.
It provides a tutorial introduction to Generic Model Control and assists them in applying modern control methods to their processes. simple, reasonably general, nonlinear system theory could be developed. Hand in hand with this viewpoint was the feeling that many of the approaches useful for linear systems ought to be extensible to the nonlinear theory.
This is a key point if the theory is to. Nonlinear model-based process control [Book Review] Article in IEEE Control Systems Magazine 21(6) January with 1 Reads How we measure 'reads'. Model-based control. In literature some approaches for complete model based control of the process exist.
For example, Gevelber et al. propose a model-based multi-loop control system. While a first loop is responsible for diameter tracking a second one is intended to compensate for identifiable by: ISBN: OCLC Number: Notes: "Published in cooperation with NATO Scientific Affairs Division." Proceedings of the NATO Advanced Study Institute on Nonlinear Model Based Process Control, Antalya, Turkey, August"--Title page verso.
Filling a gap in the literature for a practical approach to the topic, this book is unique in including a whole section of case studies presenting a wide range of applications from polymerization reactors and bioreactors, to distillation column and complex fluid catalytic cracking units.
Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints. This tutorial will present the concepts behind a MISO nonlinear process-model based controller that can be implemented in-house.
Process-Model Based Control (PMBC) uses a simple dynamic model of the process, a single tuning parameter, tracking of process characteristics for process condition monitoring, and no integral Size: 1MB.
Soroush, M. and Kravaris, C. () A continuous-time formulation of nonlinear model-predictive control, Proceedings American Control Conference, Chicago, IL, – Google Scholar Cited by: Nonlinear Model Based 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. Book Description.
Model-Based Control of Nonlinear Systems presents model-based control techniques for nonlinear, constrained systems. It covers constructive control design methods with an emphasis on modeling constrained systems, generating dynamic control models, and designing tracking control algorithms for the models.
It is an excellent book which provides model-based process control applications to important refinery processes. Since, mathematcs used in this book is easy to understand, it makes the real-time applications more attractive as reader does not need to spent his time on solving the difficult equations.
A very practical book.5/5. Analysis and Control of Nonlinear Process Systems overcomes these barriers. Features: • The necessary mathematical preliminaries for readers from a process engineering background.
• Constant reference to the widely-known finite-dimensional linear time-invariant continuous case as a basis for extension to the nonlinear situation. This contribution continues an article series, about the nonlinear model-based control of the Czochralski crystal growth process.
The key idea of the presented approach is to use a sophisticated combination of nonlinear model-based and conventional (linear) PI controllers for Cited by: Nonlinear Control Systems Design This paper deals with the design of output feedback control to stabilize the cutting force of the nonlinear end milling process.
The control design is based on criteria to be satisfied by the geometric conditions of the nonlinear system. joint drives and pneumatic drives are determined and it is.
Nonlinear Model-based Process Control: Applications in Petroleum Refining. [Rashid M Ansari; Moses O Tadé] -- 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.
Find helpful customer reviews and review ratings for Nonlinear Model-based Process Control: Applications in Petroleum Refining (Advances in Industrial Control) at Read honest and unbiased product reviews from our users.5/5(4). Model based control has emerged as an important way to improve plant efficiency in the process industries, while meeting processing and operating policy constraints.
The reader of Methods of Model Based Process Control will find state of the art reports on model based control technology. Summary. Model-Based Control of Nonlinear Systems presents model-based control techniques for nonlinear, constrained systems.
It covers constructive control design methods with an emphasis on modeling constrained systems, generating dynamic control models, and designing tracking control algorithms for the models.
Dynamics and Nonlinear Control of Integrated Process Systems; this book first studies process systems with large material recycle and/or with small purge streams, followed by systems with energy integration.
Nonlinear model-based control of nonminimum-phase processes. In R., Berber and C., Kravaris, eds., Nonlinear Model Based Process Cited by: Stephanopoulos G., Karsligil O., Dyer M.S. () Multi-Scale Aspects in Linear and Nonlinear Estimation and Control.
In: Berber R., Kravaris C. (eds) Nonlinear Model Based Process Control. NATO ASI Series (Series E: Applied Sciences), vol Cited by: 5. Get this from a library. Nonlinear model-based process control: applications in petroleum refining. [Rashid Ansari; Moses O Tadé] -- The work in this text 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.
Design of Model Based Controller for a Non-Linear Process P. Suganthini 1, P. Aravind 2, S. Girirajkumar 3 1 A ssistant P rofe,Dep atm nICE Saranathan C ll gE ieer Tchy mil N du I 2 A ssistant P rofe,Dep atm nICE Saranathan C ll gE ieer Tchy mil N du I 3 P rofes r and Head,Dep atm nICE Saranathan C ll gE ieer Tchy mil N du I Abstract Control of process parameters is one of the Author: P.
Suganthini, P. Aravind, S. Girirajkumar. This paper surveys the major active areas in model-based control, namely inferential control, model predictive control (for linear and nonlinear processes) and nonlinear variable transformations.
There are a variety of algorithms in each of the above areas, and the relative advantages and disadvantage of these techniques are identified. Fuzzy Neural Network Techniques and their Application for Nonlinear Chemical Process Control A. Aoyama, F.J. Doyle III and V. Venkatasubramanian, “Fuzzy Neural Network Techniques and their Application for Nonlinear Chemical Process Control”, in Fuzzy Theory Systems Techniques & Applications, C.T.
Leondes, Editor, Academic Press, Chapter 3 Nonlinear Model Predictive Control In this chapter, we introduce the nonlinear model predictive control algorithm in a rigorous way.
We start by deﬁning a basic NMPC algorithm for constant reference and continue by formalizing state and control constraints.
Viability (or weak forwardFile Size: KB. Model-Based Control of Nonlinear Systems presents model-based control techniques for nonlinear, constrained systems. It covers constructive control design methods with an emphasis on modeling constrained systems, generating dynamic control models, and designing tracking control.
The process can be controlled with nonlinear MPC that uses a nonlinear model directly in the control application.
The nonlinear model may be in the form of an empirical data fit (e.g. artificial neural networks) or a high-fidelity dynamic model based on fundamental mass and energy balances.
The nonlinear model may be linearized to derive a. Zhang T, Feng G, Liu H and Lu J () Piecewise fuzzy anti-windup dynamic output feedback control of nonlinear processes with amplitude and rate actuator saturations, IEEE Transactions on Fuzzy Systems,(), Online publication date: 1-Apr Nonlinear model-based process control—applications in petroleum refining Nonlinear model-based process control—applications in petroleum refining Lewin, Daniel R.
The editors’ foreword of this book describes the motivation for its publication, noting that its main strengths are (a) its focus on four petroleum refinery applications and (b) the non-linear model-based.
The adaptive process planning is used for the following production control considering current state information of the production system. At each time (t e) tasks of process planning as well as production control are executed.A decision about the situation-specific optimal process sequence at time t e requires the availability of up-to-date state information of all involved planning objects.
I would like to study regarding control of linear and nonlinear systems in detail. So, please suggest me some books which can provide in-depth knowledge regarding it.The new 4th edition ofSeborgsProcess Dynamics Controlprovides full topical coverage for process control courses in the chemical engineering curriculum, emphasizing how process control and its related fields of process modeling and optimization are essential to the development of high-value products.A principal objective of this new edition is to describe modern techniques for control .The book is unique in the broad coverage of different model based control strategies and in the variety of applications presented.
A special merit of the book is in the included library of dynamic models of several industrially relevant processes, which can be used by both the industrial and academic community to study and implement advanced.