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Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.]
| Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.] |
| Autore | Agachi Paul Șerban |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2006 |
| Descrizione fisica | 1 online resource (291 p.) |
| Disciplina | 660.2815 |
| Altri autori (Persone) | AgachiPaul Serban |
| Soggetto topico |
Chemical engineering
Chemical process control Predictive control |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-08795-5
9786611087951 3-527-60947-4 3-527-60922-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Model 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
1.2.1.7 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 2.3.5.2 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) 2.4.4 Sampling Time (T)2.4.5 Weight Matrices (Γ(/)(y) and Γ(/)(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 3.1.3.2 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 3.2.2 Linear Model Identification and LMPC of the Bioreactor |
| Record Nr. | UNINA-9910144003503321 |
Agachi Paul Șerban
|
||
| Weinheim, : Wiley-VCH, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.]
| Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.] |
| Autore | Agachi Paul Șerban |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2006 |
| Descrizione fisica | 1 online resource (291 p.) |
| Disciplina | 660.2815 |
| Altri autori (Persone) | AgachiPaul Serban |
| Soggetto topico |
Chemical engineering
Chemical process control Predictive control |
| ISBN |
1-281-08795-5
9786611087951 3-527-60947-4 3-527-60922-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Model 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
1.2.1.7 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 2.3.5.2 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) 2.4.4 Sampling Time (T)2.4.5 Weight Matrices (Γ(/)(y) and Γ(/)(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 3.1.3.2 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 3.2.2 Linear Model Identification and LMPC of the Bioreactor |
| Record Nr. | UNINA-9910830387603321 |
|
Agachi Paul Șerban
|
||
| Weinheim, : Wiley-VCH, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.]
| Model based control [[electronic resource] ] : case studies in process engineering / / Paul Serban Agachi ... [et al.] |
| Autore | Agachi Paul Șerban |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH, c2006 |
| Descrizione fisica | 1 online resource (291 p.) |
| Disciplina | 660.2815 |
| Altri autori (Persone) | AgachiPaul Serban |
| Soggetto topico |
Chemical engineering
Chemical process control Predictive control |
| ISBN |
1-281-08795-5
9786611087951 3-527-60947-4 3-527-60922-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Model 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
1.2.1.7 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 2.3.5.2 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) 2.4.4 Sampling Time (T)2.4.5 Weight Matrices (Γ(/)(y) and Γ(/)(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 3.1.3.2 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 3.2.2 Linear Model Identification and LMPC of the Bioreactor |
| Record Nr. | UNINA-9910840517903321 |
|
Agachi Paul Șerban
|
||
| Weinheim, : Wiley-VCH, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||