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020 _a3527315454 (cased)
020 _a9783527315451 (cased)
039 9 _a201402040106
_bVLOAD
_c201007311028
_dmalmash
_c200812141105
_dmusallam
_y200812140942
_zalawaid
050 0 0 _aTP155.75
_b.M62 2006
245 0 0 _aModel based control :
_bcase studies in process engineering /
_cPaul Serban Agachi ... [et al.].
260 _aWeinheim :
_bWiley-VCH,
_cc2006.
300 _axi, 277 p. :
_bill. ;
_c25 cm.
504 _aIncludes bibliographical references and index.
505 _aPreface. 1 Introduction. 1.1 Introductory Concepts of Process Control. 1.2 Advanced Process Control Techniques. 1.2.1 Key Problems in Advanced Control of Chemical Processes. 1.2.1.1 Nonlinear Dynamic Behavior. 1.2.1.2 Multivariable Interactions between Manipulated and Controlled Variables. 1.2.1.3 Uncertain and Time-Varying Parameters. 1.2.1.4 Deadtime on Inputs and Measurements. 1.2.1.5 Constraints on Manipulated and State Variables. 1.2.1.6 High-Order and Distributed Processes. 1.2.1.7 Unmeasured State Variables and Unmeasured and Frequent Disturbances. 1.2.2 Classification of the Advanced Process Control Techniques. 2 Model Predictive Control. 2.1 Internal Model Control. 2.2 Linear Model Predictive Control. 2.3 Nonlinear Model Predictive Control. 2.3.1 Introduction. 2.3.2 Industrial Model-Based Control: Current Status and Challenges. 2.3.2.1 Challenges in Industrial NMPC. 2.3.3 First Principle (Analytical) Model-Based NMPC. 2.3.4 NMPC with Guaranteed Stability. 2.3.5 Artificial Neural Network (ANN)-Based Nonlinear Model Predictive Control. 2.3.5.1 Introduction. 2.3.5.2 Basics of ANNs. 2.3.5.3 Algorithms for ANN Training. 2.3.5.4 Direct ANN Model-Based NMPC (DANMPC). 2.3.5.5 Stable DANMPC Control Law. 2.3.5.6 Inverse ANN Model-Based NMPC. 2.3.5.7 ANN Model-Based NMPC with Feedback Linearization. 2.3.5.8 ANN Model-Based NMPC with On-Line Linearization. 2.3.6 NMPC Software for Simulation and Practical Implementation. 2.3.6.1 Computational Issues. 2.3.6.2 NMPC Software for Simulation. 2.3.6.3 NMPC Software for Practical Implementation. 2.4 MPC General Tuning Guidelines. 2.4.1 Model Horizon (n). 2.4.2 Prediction Horizon (p). 2.4.3 Control Horizon (m). 2.4.4 Sampling Time (T). 2.4.5 Weight Matrices ( l y and l u). 2.4.6 Feedback Filter. 2.4.7 Dynamic Sensitivity Used for MPC Tuning. 3 Case Studies. 3.1 Productivity Optimization and Nonlin
520 _aFilling 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.
650 0 _aChemical process control
_vCase studies.
_949364
650 0 _aPredictive control
_vCase studies.
_949365
650 0 _aChemical engineering.
_96532
700 1 _aAgachi, Paul Serban.
_949366
942 _2lcc
_n0
_cBK
999 _c23388
_d23388