LEADER 05262nam 2200637Ia 450 001 9910830387603321 005 20170810192842.0 010 $a1-281-08795-5 010 $a9786611087951 010 $a3-527-60947-4 010 $a3-527-60922-9 035 $a(CKB)1000000000376062 035 $a(EBL)481299 035 $a(SSID)ssj0000203895 035 $a(PQKBManifestationID)11188362 035 $a(PQKBTitleCode)TC0000203895 035 $a(PQKBWorkID)10152153 035 $a(PQKB)11353014 035 $a(MiAaPQ)EBC481299 035 $a(OCoLC)86175779 035 $a(EXLCZ)991000000000376062 100 $a20060721d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModel based control$b[electronic resource] $ecase studies in process engineering /$fPaul Serban Agachi ... [et al.] 210 $aWeinheim $cWiley-VCH$dc2006 215 $a1 online resource (291 p.) 300 $aDescription based upon print version of record. 311 $a3-527-31545-4 320 $aIncludes bibliographical references and index. 327 $aModel Based Control; Table of Contents; Preface; 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 327 $a1.2.1.7 Unmeasured State Variables and Unmeasured and Frequent Disturbances1.2.2 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 327 $a2.3.5.2 Basics of ANNs2.3.5.3 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) 327 $a2.4.4 Sampling Time (T)2.4.5 Weight Matrices (?(/)(y) and ?(/)(u)); 2.4.6 Feedback Filter; 2.4.7 Dynamic Sensitivity Used for MPC Tuning; 3 Case Studies; 3.1 Productivity Optimization and Nonlinear Model Predictive Control (NMPC) of a PVC Batch Reactor; 3.1.1 Introduction; 3.1.2 Dynamic Model of the PVC Batch Reactor; 3.1.2.1 The Complex Analytical Model of the PVC Reactor; 3.1.2.2 Morphological Model; 3.1.2.3 The Simplified Dynamic Analytical Model of the PVC Reactor; 3.1.3 Productivity Optimization of the PVC Batch Reactor; 3.1.3.1 The Basic Elements of GAs 327 $a3.1.3.2 Optimization of the PVC Reactor Productivity through the Initial Concentration of Initiators3.1.3.3 Optimization of PVC Reactor Productivity by obtaining an Optimal Temperature Policy; 3.1.4 NMPC of the PVC Batch Reactor; 3.1.4.1 Multiple On-Line Linearization-Based NMPC of the PVC Batch Reactor; 3.1.4.2 Sequential NMPC of the PVC Batch Reactor; 3.1.5 Conclusions; 3.1.6 Nomenclature; 3.2 Modeling, Simulation, and Control of a Yeast Fermentation Bioreactor; 3.2.1 First Principle Model of the Continuous Fermentation Bioreactor 327 $a3.2.2 Linear Model Identification and LMPC of the Bioreactor 330 $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 606 $aChemical engineering 606 $aChemical process control$vCase studies 606 $aPredictive control$vCase studies 615 0$aChemical engineering. 615 0$aChemical process control 615 0$aPredictive control 676 $a660.2815 700 $aAgachi$b Paul S?erban$0883805 701 $aAgachi$b Paul Serban$0883805 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830387603321 996 $aModel based control$93965180 997 $aUNINA