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Model Predictive Control Of A Nonlinear Large Scale Process Network Used In The Production Of Vinyl Acetate
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Book Synopsis Model Predictive Control of a Nonlinear Large-scale Process Network Used in the Production of Vinyl Acetate by : TungSheng Tu
Download or read book Model Predictive Control of a Nonlinear Large-scale Process Network Used in the Production of Vinyl Acetate written by TungSheng Tu and published by . This book was released on 2013 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, we focus on the development and application of two Lyapunov-based model predictive control (LMPC) schemes to a large-scale nonlinear chemical process network used in the production of vinyl acetate. The nonlinear dynamic model of the process consists of 179 state variables and 13 control (manipulated) inputs and features a cooled plug-flow reactor, an eight-stage gas-liquid absorber, and both gas and liquid recycle streams. The two control schemes considered are an LMPC scheme which is formulated with a convectional quadratic cost function and a Lyapunov-based economic model predictive control (LEMPC) scheme which is formulated with an economic (non-quadratic) cost measure. The economic cost measure for the entire process network accounts for the reaction selectivity and the product separation quality. In the LMPC and LEMPC control schemes, five inputs, directly affecting the economic cost, are regulated with LMPC/LEMPC and the remaining eight inputs are computed by proportional-integral controllers. Simulations are carried out to study the economic performance of the closed-loop system under LMPC and under LEMPC formulated with the proposed economic measure. A thorough comparison of the two control schemes is provided.
Book Synopsis Distributed Model Predictive Control of Nonlinear and Two-time Scale Process Networks by : Xianzhong Chen
Download or read book Distributed Model Predictive Control of Nonlinear and Two-time Scale Process Networks written by Xianzhong Chen and published by . This book was released on 2012 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale chemical process systems are characterized by highly nonlinear behavior and the coupling of physico-chemical phenomena occurring at disparate time scales. Examples include fluidized catalytic crackers, distillation columns, biochemical reactors as well as chemical process networks in which the individual processes evolve in a fast time-scale and the network dynamics evolve in a slow time-scale. Traditionally, the design of advanced model-based control systems for chemical processes has followed the centralized paradigm in which one control system is used to compute the control actions of all manipulated inputs. While the centralized paradigm to model-based process control has been successful, when the number of the process state variables, manipulated inputs and measurements in a chemical plant becomes large - a common occurrence in modern plants -, the computational time needed for the solution of the centralized control problem may increase significantly and may impede the ability of centralized control systems (particularly when nonlinear constrained optimization-based control systems like model predictive control-MPC are used), to carry out real-time calculations within the limits set by process dynamics and operating conditions. One feasible alternative to overcome this problem is to utilize cooperative, distributed control architectures in which the manipulated inputs are computed by solving more than one control (optimization) problems in separate processors in a coordinated fashion. Motivated by the above considerations, this dissertation presents rigorous, yet practical, methods for the design of distributed model predictive control systems for nonlinear and two-time-scale process networks. Beginning with a review of results on the subject, the first part of this dissertation presents the design of two, sequential and iterative, distributed MPC architectures via Lyapunov-based control techniques for general nonlinear process systems. Key practical issues like the feedback of asynchronous and delayed measurements as well as the utilization of cost functions that explicitly account for economic considerations are explicitly addressed in the formulation and design of the controllers and of their communication strategy. In the second part of the dissertation, we focus on the design of model predictive control systems for nonlinear two-times-scale process networks within the framework of singular perturbations. Both centralized and distributed MPC designs are presented. Throughout the thesis, the applicability, effectiveness and computational efficiency of the control methods are evaluated via simulations using numerous, large-scale chemical process networks.
Author :Panagiotis D. Christofides Publisher :Springer Science & Business Media ISBN 13 :0857295829 Total Pages :253 pages Book Rating :4.8/5 (572 download)
Book Synopsis Networked and Distributed Predictive Control by : Panagiotis D. Christofides
Download or read book Networked and Distributed Predictive Control written by Panagiotis D. Christofides and published by Springer Science & Business Media. This book was released on 2011-04-07 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems – the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized control systems. Key features of this book include: • new techniques for networked and distributed control system design; • insight into issues associated with networked and distributed predictive control and their solution; • detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and • integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control and for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the book’s methods in greater depth.
Book Synopsis Nonlinear Model Based Process Control by : Rıdvan Berber
Download or read book Nonlinear Model Based Process Control written by Rıdvan Berber and published by Springer Science & Business Media. This book was released on 1998 with total page 916 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. This text 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. 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.
Book Synopsis Nonlinear Model Predictive Control and Dynamic Real Time Optimization for Large-scale Processes by : Rui Huang
Download or read book Nonlinear Model Predictive Control and Dynamic Real Time Optimization for Large-scale Processes written by Rui Huang and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Model Predictive Control of Nonlinear Processes by : Venkateswarlu Ch
Download or read book Model Predictive Control of Nonlinear Processes written by Venkateswarlu Ch and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Model predictive control (MPC) is known to be a powerful control strategy for a variety of processes. In this study, the capabilities of linear and nonlinear model predictive controllers are explored by designing and applying them to different nonlinear processes. A linear model predictive controller (LMPC) is presented for the control of an ethyl acetate reactive distillation. A generalized predictive control (GPC) and a constrained generalized predictive control (CGPC) are presented for the control of an unstable chemical reactor. Further, a nonlinear model predictive controller (NMPC) based on simulated annealing is presented for the control of a highly complex nonlinear ethyl acetate reactive distillation column. The results of these controllers are evaluated under different disturbance conditions for their servo and regulatory performance and compared with the conventional controllers. From these results, it is observed that though linear model predictive controllers offer better control performance for nonlinear processes over conventional controllers, the nonlinear model predictive controller provides effective control performance for highly complex nonlinear processes.
Book Synopsis Model Predictive Control for Nonlinear Networked Control Systems by : Paolo Varutti
Download or read book Model Predictive Control for Nonlinear Networked Control Systems written by Paolo Varutti and published by . This book was released on 2014 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Bingfeng Gu Publisher :Library and Archives Canada = Bibliothèque et Archives Canada ISBN 13 :9780494019481 Total Pages :220 pages Book Rating :4.0/5 (194 download)
Book Synopsis Model Predictive Control of Nonlinear Processes [microform] by : Bingfeng Gu
Download or read book Model Predictive Control of Nonlinear Processes [microform] written by Bingfeng Gu and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2005 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Quadratic Constraint Approach to Model Predictive Control of Interconnected Systems by : Anthony Tri Tran C.
Download or read book A Quadratic Constraint Approach to Model Predictive Control of Interconnected Systems written by Anthony Tri Tran C. and published by Springer. This book was released on 2018-03-06 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the stabilization and model predictive control of interconnected systems with mixed connection configurations. It introduces the concept of dissipation-based quadratic constraint for developing attractivity assurance methods for interconnected systems. In order to develop these methods, distributed and decentralized architectures are employed, whereby the communication between subsystems is fully connected, partially connected, or completely disconnected. Given that the control inputs are entirely or partially decoupled between subsystems and no additional constraints are imposed on the interactive variables beyond the coupling constraint itself, the proposed approaches can be used with various types of systems and applications. Further, the book describes how the effects of coupling delays and data losses in device networks are resolved. From a practical perspective, the innovations presented are of benefit in applications in a broad range of fields, including the process and manufacturing industries, networked robotics, and network-centric systems such as chemical process systems, power systems, telecommunication networks, transportation networks, and, no less importantly, supply chain automation.
Book Synopsis Distributed and economic model predictive control: beyond setpoint stabilization by : Matthias A. Müller
Download or read book Distributed and economic model predictive control: beyond setpoint stabilization written by Matthias A. Müller and published by Logos Verlag Berlin GmbH. This book was released on 2014 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we study model predictive control (MPC) schemes for control tasks which go beyond the classical objective of setpoint stabilization. In particular, we consider two classes of such control problems, namely distributed MPC for cooperative control in networks of multiple interconnected systems, and economic MPC, where the main focus is on the optimization of some general performance criterion which is possibly related to the economics of a system. The contributions of this thesis are to analyze various systems theoretic properties occurring in these type of control problems, and to develop distributed and economic MPC schemes with certain desired (closed-loop) guarantees. To be more precise, in the field of distributed MPC we propose different algorithms which are suitable for general cooperative control tasks in networks of interacting systems. We show that the developed distributed MPC frameworks are such that the desired cooperative goal is achieved, while coupling constraints between the systems are satisfied. Furthermore, we discuss implementation and scalability issues for the derived algorithms, as well as the necessary communication requirements between the systems. In the field of economic MPC, the contributions of this thesis are threefold. Firstly, we analyze a crucial dissipativity condition, in particular its necessity for optimal steady-state operation of a system and its robustness with respect to parameter changes. Secondly, we develop economic MPC schemes which also take average constraints into account. Thirdly, we propose an economic MPC framework with self-tuning terminal cost and a generalized terminal constraint, and we show how self-tuning update rules for the terminal weight can be derived such that desirable closed-loop performance bounds can be established.
Book Synopsis Robust Model Predictive Control for Large-Scale Manufacturing Systems subject to Uncertainties by : Jens Tonne
Download or read book Robust Model Predictive Control for Large-Scale Manufacturing Systems subject to Uncertainties written by Jens Tonne and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Distributed Model Predictive Control of Large-scale Process Systems by : Mehmet Mercangöz
Download or read book Distributed Model Predictive Control of Large-scale Process Systems written by Mehmet Mercangöz and published by ProQuest. This book was released on 2007 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: The former contribution includes the extension of a two layer control system design for a pulp mill benchmark problem to a full-scale real-time optimization and control network. This extension was achieved through the application of a multi-step methodology involving problem formulation, model development, optimization, sensitivity analysis, and real-time application. A complementary industrial study is conducted to evaluate the applicability of the proposed methodology.
Book Synopsis A Nonlinear Dynamic Model for the Vinyl Acetate Monomer Process by : Rong Chen
Download or read book A Nonlinear Dynamic Model for the Vinyl Acetate Monomer Process written by Rong Chen and published by . This book was released on 2001 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Controller Performance of P,PI and Neral Network Control in Vinyl Acetate Monomer Process by : Faizal Nazareth Ibrahim
Download or read book Controller Performance of P,PI and Neral Network Control in Vinyl Acetate Monomer Process written by Faizal Nazareth Ibrahim and published by . This book was released on 2008 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research is about investigating the controller performance between P, PI and Neural Network control in Vinyl Acetate Monomer (VAC) Process. The manufacturing process is about vapor-phase reaction converting ethylene (C2H4), oxygen (O2) and acetic acid (HAc) into vinyl acetate (VAc) with water (H2O) and carbon dioxide (CO2) as byproducts. The data from the process are successfully generated and the simulation of the dynamic response is done with further analysis of P, PI control and Neural Network control. The study is focusing on the column section process as the clear view of the control performance is observed. The Proportional (P) and Proportional Integral (PI) control are type of controller that used in the process. The Neural Network control then is a control mechanism that has the similar system of human neurons for processing information data. It consists of network of neurons that have weight in each network and built generally in layers. As the analysis result of P and PI control showed some unsatisfying results, Neural Network Control is then developed to see the changes. In Neural Network control, the data has been trained and validate to get the better response before applied again to the process to see the improvement. At the end, Neural Network has visualized the better control performance as the unsatisfying responses of P and PI control have been improvised.-Author.
Book Synopsis Nonlinear Model Predictive Control for Industrial Manufacturing Processes with Reconfigurable Machine Tools by : Qiang Zhang
Download or read book Nonlinear Model Predictive Control for Industrial Manufacturing Processes with Reconfigurable Machine Tools written by Qiang Zhang and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Model Predictive Control Based on Nonlinear Autoregressive and Neural Network Models by : Thomas Pröll
Download or read book Model Predictive Control Based on Nonlinear Autoregressive and Neural Network Models written by Thomas Pröll and published by . This book was released on 1993 with total page 710 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Networked and Distributed Predictive Control by : Panagiotis D. Christofides
Download or read book Networked and Distributed Predictive Control written by Panagiotis D. Christofides and published by . This book was released on 2011-04-01 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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