Methods Of Model Based Process Control

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Techniques Of Model Based Control

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Author : Coleman Brosilow
language : en
Publisher: Prentice Hall Professional
Release Date : 2002

Techniques Of Model Based Control written by Coleman Brosilow and has been published by Prentice Hall Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.

Annotation In this book, two of the field's leading experts bring together powerful advances in model-based control for chemical process engineering. From start to finish, Coleman Brosilow and Babu Joseph introduce practical approaches designed to solve real-world problems -- not just theory. The book contains extensive examples and exercises, and an accompanying CD-ROM contains hands-on MATLAB files that supplement the examples and help readers solve the exercises -- a feature found in no other book on the topic.

Methods Of Model Based Process Control

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Author : R. Berber
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Methods Of Model Based Process Control written by R. Berber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.

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 presented by the world's leading scientists and experts from industry. All the important issues that a model based control system has to address are covered in depth, ranging from dynamic simulation and control-relevant identification to information integration. Specific emerging topics are also covered, such as robust control and nonlinear model predictive control. In addition to critical reviews of recent advances, the reader will find new ideas, industrial applications and views of future needs and challenges. Audience: A reference for graduate-level courses and a comprehensive guide for researchers and industrial control engineers in their explorationof the latest trends in the area.

Nonlinear Model Based Process Control

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Author : R. Berber
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Nonlinear Model Based Process Control written by R. Berber and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.

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.

Nonlinear Model Based Control With Application To Polymerization Reactors

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Author : Michael P. Niemiec
language : en
Publisher:
Release Date : 2000

Nonlinear Model Based Control With Application To Polymerization Reactors written by Michael P. Niemiec and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with categories.

Model Based Control

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Author : Paul Serban Agachi
language : en
Publisher: John Wiley & Sons
Release Date : 2007-09-24

Model Based Control written by Paul Serban Agachi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-09-24 with Technology & Engineering categories.

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. A section of general tuning guidelines of MPC is also present.These thus aid readers in facilitating the implementation of MPC in process engineering and automation. At the same time many theoretical, computational and implementation aspects of model-based control are explained, with a look at both linear and nonlinear model predictive control. Each chapter presents details related to the modeling of the process as well as the implementation of different model-based control approaches, and there is also a discussion of both the dynamic behaviour and the economics of industrial processes and plants. 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 control strategies.

Nonlinear Model Based Process Control

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Author : Rashid M. Ansari
language : en
Publisher: Springer
Release Date : 2000-04-12

Nonlinear Model Based Process Control written by Rashid M. Ansari and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-04-12 with Science categories.

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 a process model, an inferential model and a multi-variable control algorithm in one framework.

Proceedings Of The Second International Conference On Foundations Of Computer Aided Process Operations

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Author :
language : en
Publisher:
Release Date : 1994

Proceedings Of The Second International Conference On Foundations Of Computer Aided Process Operations written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Chemical processes categories.

Proceedings Of The Workshop On Neural Network Applications And Tools September 13 14 1993 Liverpool England

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Author : Paulo J. G. Lisboa
language : en
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
Release Date : 1994

Proceedings Of The Workshop On Neural Network Applications And Tools September 13 14 1993 Liverpool England written by Paulo J. G. Lisboa and has been published by Institute of Electrical & Electronics Engineers(IEEE) this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.

Intelligent Engineering Systems Through Artificial Neural Networks

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Author : Cihan H. Dagli
language : en
Publisher: American Society of Mechanical Engineers
Release Date : 1995

Intelligent Engineering Systems Through Artificial Neural Networks written by Cihan H. Dagli and has been published by American Society of Mechanical Engineers this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.

As a follow-up to the previous four volumes of Intelligent Engineering Systems Through Artificial Neural Networks by the same editor, the present volume contains the edited versions of the technical presentations of ANNIE '95, held November, 1995 in St. Louis, Missouri. The 160-some contributions are grouped into six categories: artificial neural network architectures (including subsections on architectures and learning algorithms and training); fuzzy neural networks and systems; evolutionary programming; pattern recognition; adaptive control; and smart engineering system design (including bio-medical engineering systems; signal processing; forecasting; environmental applications; machining and robotics; process control, monitoring, and automated inspection; and general engineering). Includes bandw photographs, diagrams, and charts. Annotation copyright by Book News, Inc., Portland, OR

Process Structure Aware Machine Learning Modeling For State Estimation And Model Predictive Control Of Nonlinear Processes

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Author : Mohammed S. Alhajeri
language : en
Publisher:
Release Date : 2022

Process Structure Aware Machine Learning Modeling For State Estimation And Model Predictive Control Of Nonlinear Processes written by Mohammed S. Alhajeri and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.

Big data is a cornerstone component of the fourth industrial revolution, which calls onengineers and researchers to fully utilize data in order to make smart decisions and enhance the efficiency of industrial processes as well as control systems. In practice, industrial process control systems typically rely on a data-driven model (often linear) with parameters that are determined by industrial/simulation data. However, in some scenarios, such as in profit-critical or quality-critical control loops, first-principles concepts that are based on the underlying physico-chemical phenomena may also need to be employed in the modeling phase to improve data-based process models. Hence, process systems engineers still face significant challenges when it comes to modeling large-scale, complicated nonlinear processes. Modeling will continue to be crucial since process models are essential components of cutting-edge model-based control systems, such as model predictive control (MPC). Machine learning models have a lot of potential based on their success in numerousapplications. Specifically, recurrent neural network (RNN) models, designed to account for every input-output interconnection, have gained popularity in providing approximation of various highly nonlinear chemical processes to a desired accuracy. Although the training error of neural networks that are dense and fully-connected may often be made sufficiently small, their accuracy can be further improved by incorporating prior knowledge in the structure development of such machine learning models. Physics-based recurrent neural networks modeling has yielded more reliable machine learning models than traditional, fully black-box, machine learning modeling methods. Furthermore, the development of systematic and rigorous approaches to integrate such machine learning techniques into nonlinear model-based process control systems is only getting started. In particular, physics-based machine learning modeling techniques can be employed to derive more accurate and well-conditioned dynamic process models to be utilized in advanced control systems such as model predictive control. Along with Lyapunov-based stability constraints, this scheme has the potential to significantly improve process operational performance and dynamics. Hence, investigating the effectiveness of this control scheme under the various long-standing challenges in the field of process systems engineering such as incomplete state measurements, and noise and uncertainty is essential. Also, a theoretical framework for constructing and assessing the generalizability of this type of machine learning models to be utilized in model predictive control systems is lacking. In light of the aforementioned considerations, this dissertation addresses the incorporation ofprior process knowledge into machine learning models for model predictive control of nonlinear chemical processes. The motivation, background and outline of this dissertation are first presented. Then, the use of machine learning modeling techniques to construct two different data-driven state observers to compensate for incomplete process measurements is presented. The closed-loop stability under Lyapunov-based model predictive controllers is then addressed. Next, the development of process-structure-based machine learning models to approximate large, nonlinear chemical processes is presented, with the improvements yielded by this approach demonstrated via open-loop and closed-loop simulations. Subsequently, the reliability of process-structure-based machine learning models is investigated in the presence of different types of industrial noise. Two novel approaches are proposed to enhance the accuracy of machine learning models in the presence of noise. Lastly, a theoretical framework that connects the accuracy of an RNN model to its structure is presented, where an upper bound on a physics-based RNN model's generalization error is established. Nonlinear chemical process examples are numerically simulated or modeled in Aspen Plus Dynamics to illustrate the effectiveness and performance of the proposed control methods throughout the dissertation.