| Edition |
Fourth edition. |
| Description |
1 online resource |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Note |
Includes index. |
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Online resource; title from PDF title page (EBSCO, viewed September 11, 2017). |
| Summary |
Coulson and Richardson's Chemical Engineering: Volume 3B: Process Control, Fourth Edition, covers reactor design, flow modeling, and gas-liquid and gas-solid reactions and reactors. Converted from textbooks into fully revised reference materialContent ranges from foundational through to technical Added emerging applications, numerical methods and computational tools. |
| Contents |
Machine generated contents note: ch. 1 Introduction -- 1.1. Definition of a Chemical/Biochemical Process -- 1.1.1.A Single Continuous Process -- 1.1.2.A Batch and a Semibatch or a Fed-Batch Process -- 1.2. Process Dynamics -- 1.2.1. Classification of Process Variables -- 1.2.2. Dynamic Modeling -- 1.3. Process Control -- 1.3.1. Types of Control Strategies -- 1.4. Incentives for Process Control -- 1.5. Pictorial Representation of the Control Systems -- 1.6. Problems -- References -- ch. 2 Hardware Requirements for the Implementation of Process Control Systems -- 2.1. Sensor/Transmitter -- 2.1.1. Temperature Transducers -- 2.1.2. Pressure Transducers -- 2.1.3. Liquid or Gas Flow Rate Transducers -- 2.1.4. Liquid Level Transducers -- 2.1.5. Chemical Composition Transducers -- 2.1.6. Instrument or Transducer Accuracy -- 2.1.7. Sources of Instrument Errors -- 2.1.8. Static and Dynamic Characteristics of Transducers -- 2.2. Signal Converters -- 2.3. Transmission Lines -- 2.4. The Final Control Element |
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Note continued: 2.4.1. Control Valves -- 2.5. Feedback Controllers -- 2.5.1. The PID (Proportional-Integral-Derivative) Controllers -- 2.5.2. The PID Controller Law -- 2.5.3. The Discrete Version of a PID Controller -- 2.5.4. Features of the PID Controllers -- 2.6.A Demonstration Unit to Implement A Single-Input, Single-Output PID Controller Using the National Instrument Data Acquisition (NI-DAQ) System and the LabVIEW -- 2.7. Implementation of the Control Laws on the Distributed Control Systems -- 2.8. Problems -- References -- ch. 3 Theoretical Process Dynamic Modeling -- 3.1. Detailed Theoretical Dynamic Modeling -- 3.2. Solving an ODE or a Set of ODEs -- 3.2.1. Solving a Linear or a Nonlinear Differential Equation in MATLAB -- 3.2.2. Solving a Linear or a Nonlinear Differential Equation on Simulink -- 3.3. Examples of Lumped Parameter Systems -- 3.3.1.A Surge Tank With Level Control -- 3.3.2.A Stirred Tank Heater With Level and Temperature Control |
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Note continued: 3.3.3.A Nonisothermal Continuous Stirred Tank Reactor -- 3.3.4.A CSTR With Liquid Phase Endothermic Chemical Reactions -- 3.4. Examples of Stage-Wise Systems -- 3.4.1.A Binary Tray Distillation Column -- 3.5. Examples of Distributed Parameter Systems -- 3.5.1.A Plug Flow Reactor -- 3.6. Problems -- References -- ch. 4 Development of Linear State-Space Models and Transfer Functions for Chemical Processes -- pt. A Theoretical Development of Linear Models -- 4.1. Tools to Develop Continuous Linear State-Space and Transfer Function Dynamic Models -- 4.1.1. Linearization of Nonlinear Differential Equations -- 4.1.2. The Linear State-Space Models -- 4.1.3. Developing Transfer Function Models (T.F.) -- 4.2. The Basic Procedure to Develop the Transfer Function of SISO and MIMO Systems -- 4.3. Steps to Derive the Transfer Function (T.F.) Models -- 4.4. Transfer Function of Linear Systems -- 4.4.1. Simple Functional Forms of the Input Signals |
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Note continued: 4.4.2. First-Order Transfer Function Models -- 4.4.3.A Pure Capacitive or An Integrating Process -- 4.4.4. Processes With Second-Order Dynamics -- 4.4.5. Significance of the Transfer Function Poles and Zeros -- 4.4.6. Transfer Functions of More Complicated Processes -- An Inverse Response (A Nonminimum Phase Process), A Higher Order Process and Processes With Time Delays -- 4.4.7. Processes With Nth-Order Dynamics -- 4.4.8. Transfer Function of Distributed Parameter Systems -- 4.4.9. Processes With Significant Time Delays -- pt. B The Empirical Approach to Develop Approximate Transfer Functions for Existing Processes -- 4.5. The Graphical Methods for Process Identification -- 4.5.1. Approximation of the Unknown Process Dynamics by a First-Order Transfer Function With or Without a Time Delay -- 4.5.2. Approximation by a Second-Order Transfer Function With a Time Delay -- 4.6. Process Identification Using Numerical Methods -- 4.6.1. The Least Squares Method |
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Note continued: 4.6.2. Using the "Solver" Function of Excel for the Estimation of the Parameter Vector in System Identification -- 4.6.3.A MATLAB Program for Parameter Estimation -- 4.6.4. Using System Identification Toolbox of MATLAB -- 4.7. Problems -- References -- ch. 5 Dynamic Behavior and Stability of Closed-Loop Control Systems -- Controller Design in the Laplace Domain -- 5.1. The Closed-Loop Transfer Function of a Single-Input, Single-Output (SISO) Feedback Control System -- 5.2. Analysis of a Feedback Control System -- 5.2.1.A Proportional Controller -- 5.2.2.A Proportional-Integral (PI) Controller -- 5.3. The Block Diagram Algebra -- 5.4. The Stability of the Closed-Loop Control Systems -- 5.5. Stability Tests -- 5.5.1. Routh Test -- 5.5.2. Direct Substitution Method -- 5.5.3. The Root Locus Diagram -- 5.6. Design and Tuning of the PID Controllers -- 5.6.1. Controller Design Objectives -- 5.6.2. Choosing the Appropriate Control Law -- 5.6.3. Controller Tuning |
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Note continued: 5.6.4. The Use of Model-Based Controllers to Tune a PID Controller (Theoretical Method) -- 5.6.5. Empirical Approaches to Tune a PID Controller -- 5.7. Enhanced Feedback and Feedforward Controllers -- 5.7.1. Cascade Control -- 5.7.2. Override Control -- 5.7.3. Selective Control -- 5.7.4. Control of Processes With Large Time Delays -- 5.7.5. Control of Nonlinear Processes -- 5.8. The Feedforward Controller (FFC) -- 5.8.1. The Implementation of a Feedforward Controller -- 5.8.2. The Ratio Control -- 5.9. Control of Multiinput, Multioutput (MIMO) Processes -- 5.9.1. The Bristol Relative Gain Array (RGA) Matrix -- 5.9.2. Control of MIMO Processes in the Presence of Interaction Using Decouplers -- 5.10. Problems -- References -- ch. 6 Digital Sampling, Filtering, and Digital Control -- 6.1. Implementation of Digital Control Systems -- 6.2. Mathematical Representation of a Sampled Signal -- 6.3.z-Transform of a Few Simple Functions -- 6.3.1.A Discrete Unit Step Function |
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Note continued: 6.13.4. The Kalman Controller -- 6.13.5. Internal Model Controller (IMC) -- 6.13.6. The Pole Placement Controller -- 6.14. Design of Feedforward Controllers -- 6.15. Control of Multi-Input, Multi-Output (MIMO) Processes -- 6.15.1. Singular Value Decomposition (SVD) and the Condition Number (CN) -- 6.15.2. Design of Multivariate Feedback Controllers for MIMO Plants -- 6.15.3. Dynamic and Steady-State Interaction Compensators (Decouplers) in the z-Domain -- 6.15.4. Multivariable Smith Predictor -- 6.15.5. Multivariable IMC Controller -- Problems -- References -- Further Reading -- ch. 7 Control System Design in the State Space and Frequency Domain -- 7.1. State-Space Representation -- 7.1.1. The Minimal State-Space Realization -- 7.1.2. Canonical Form State-Space Realization -- 7.1.3. Discretization of the Continuous State-Space Formulation -- 7.1.4. Discretization of Continuous Transfer Functions |
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Note continued: 7.3.7. Numerical Construction of Bode and Nyquist Plots -- 7.3.8. Applications of the Frequency Response Technique -- 7.4. Problems -- References -- Further Reading -- ch. 8 Modeling and Control of Stochastic Processes -- 8.1. Modeling of Stochastic Processes -- 8.1.1. Process and Noise Models -- 8.1.2. Review of Some Useful Concepts in the Probability Theory -- 8.2. Identification of Stochastic Processes -- 8.2.1. Off-line Process Identification -- 8.2.2. Online Process Identification -- 8.2.3. Test of Convergence of Parameter Vector in the Online Model Identification -- 8.3. Design of Stochastic Controllers -- 8.3.1. The Minimum Variance Controller (MVC) -- 8.3.2. The Generalized Minimum Variance Controllers (GMVC) -- 8.3.3. The Pole Placement Controllers (PPC) -- 8.3.4. The Pole-Placement Minimum Variance Controller (PPMVC) -- 8.3.5. Self-Tuning Regulators (STR) -- 8.4. Problems -- References -- ch. 9 Model Predictive Control of Chemical Processes: A Tutorial |
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Note continued: 9.1. Why MPC? -- 9.2. Formulation of MPC -- 9.2.1. Process Model -- 9.2.2. Objective Function -- 9.2.3. State and Input Constraints -- 9.2.4. Optimal Control Problem -- 9.2.5. Receding-Horizon Implementation -- 9.2.6. Optimization Solution Methods -- 9.3. MPC for Batch and Continuous Chemical Processes -- 9.3.1. NMPC of a Batch Crystallization Process -- 9.3.2. NMPC of a Continuous ABE Fermentation Process -- 9.4. Output-Feedback MPC -- 9.4.1. Luenberger Observer -- 9.4.2. Extended Luenberger Observer -- 9.4.3. NMPC of the Batch Crystallization Process Under Incomplete State Information -- 9.5. Advanced Process Control -- 9.6. Advanced Topics in MPC -- 9.6.1. Stability and Feasibility -- 9.6.2. MPC of Uncertain Systems -- 9.6.3. Distributed MPC -- 9.6.4. MPC With Integrated Model Adaptation -- 9.6.5. Economic MPC -- Appendix -- Batch Crystallization Case Study -- ABE Fermentation Case Study -- Acknowledgments -- References -- ch. 10 Optimal Control -- 10.1. Introduction |
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Note continued: 10.2. Problem Statement -- 10.3. Optimal Control -- 10.3.1. Variational Methods -- 10.3.2. Variation of the Criterion -- 10.3.3. Euler Conditions -- 10.3.4. Weierstrass Condition and Hamiltonian Maximization -- 10.3.5. Hamilton-Jacobi Conditions and Equation -- 10.3.6. Maximum Principle -- 10.3.7. Singular Arcs -- 10.3.8. Numerical Issues -- 10.4. Dynamic Programming -- 10.4.1. Classical Dynamic Programming -- 10.4.2. Hamilton-Jacobi-Bellman Equation -- 10.5. Linear Quadratic Control -- 10.5.1. Continuous-Time Linear Quadratic Control -- 10.5.2. Linear Quadratic Gaussian Control -- 10.5.3. Discrete-Time Linear Quadratic Control -- References -- Further Reading -- ch. 11 Control and Optimization of Batch Chemical Processes -- 11.1. Introduction -- 11.2. Features of Batch Processes -- 11.3. Models of Batch Processes -- 11.3.1. What to Model? -- 11.3.2. Model Types -- 11.3.3. Static View of a Batch Process -- 11.4. Online Control |
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Note continued: 11.4.1. Feedback Control of Run-Time Outputs (Strategy 1) -- 11.4.2. Predictive Control of Run-End Outputs (Strategy 2) -- 11.5. Run-to-Run Control -- 11.5.1. Iterative Learning Control of Run-Time Profiles (Strategy 3) -- 11.5.2. Run-to-Run Control of Run-End Outputs (Strategy 4) -- 11.6. Batch Automation -- 11.6.1. Stand-Alone Controllers -- 11.6.2. Programmable Logic Controllers -- 11.6.3. Distributed Control Systems -- 11.6.4. Personal Computers -- 11.7. Control Applications -- 11.7.1. Control of Temperature and Final Concentrations in a Semibatch Reactor -- 11.7.2. Scale-Up via Feedback Control -- 11.7.3. Control of a Batch Distillation Column -- 11.8. Numerical Optimization -- 11.8.1. Dynamic Optimization -- 11.8.2. Reformulation of a Dynamic Optimization Problem as a Static Optimization Problem -- 11.8.3. Static Optimization -- 11.8.4. Effect of Uncertainty -- 11.9. Real-Time Optimization -- 11.9.1. Repeated Numerical Optimization |
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Note continued: 11.9.2. Optimizing Feedback Control -- 11.10. Optimization Applications -- 11.10.1. Semibatch Reactor With Safety and Selectivity Constraints -- 11.10.2. Industrial Batch Polymerization -- 11.11. Conclusions -- 11.11.1. Summary -- 11.11.2. Future Challenges -- Acknowledgments -- References -- ch. 12 Nonlinear Control -- 12.1. Introduction -- 12.2. Some Mathematical Notions Useful in Nonlinear Control -- 12.2.1. Notions of Differential Geometry -- 12.2.2. Relative Degree of a Monovariable Nonlinear System -- 12.2.3. Frobenius Theorem -- 12.2.4. Coordinates Transformation -- 12.2.5. Normal Form -- 12.2.6. Controllability and Observability -- 12.2.7. Principle of Feedback Linearization -- 12.2.8. Exact Input-State Linearization for a System of Relative Degree Equal to n -- 12.2.9. Input-Output Linearization of a System With Relative Degree r Less than or Equal to n -- 12.2.10. Zero Dynamics -- 12.2.11. Asymptotic Stability -- 12.2.12. Tracking of a Reference Trajectory |
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Note continued: 12.2.13. Decoupling With Respect to a Disturbance -- 12.2.14. Case of Nonminimum-Phase Systems -- 12.2.15. Globally Linearizing Control -- 12.2.16. Generic Model Control -- 12.3. Multivariable Nonlinear Control -- 12.3.1. Relative Degree -- 12.3.2. Coordinate Change -- 12.3.3. Normal Form -- 12.3.4. Zero Dynamics -- 12.3.5. Exact Linearization by State Feedback and Diffeomorphism -- 12.3.6. Nonlinear Control Perfectly Decoupled by Static-State Feedback -- 12.3.7. Obtaining a Relative Degree by Dynamic Extension -- 12.3.8. Nonlinear Adaptive Control -- 12.4. Nonlinear Multivariable Control of a Chemical Reactor -- References -- ch. 13 Economic Model Predictive Control of Transport-Reaction Processes -- 13.1. Introduction -- 13.2. EMPC of Parabolic PDE Systems With State and Control Constraints -- 13.2.1. Preliminaries -- 13.2.2. Methodological Framework for Finite-Dimensional EMPC Using APOD -- 13.2.3. Application to a Tubular Reactor Modeled by a Parabolic PDE System |
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Note continued: 13.3. EMPC of Hyperbolic PDE Systems With State and Control Constraints -- 13.3.1. Reactor Description -- 13.3.2. EMPC System Constraints and Objective -- 13.3.3. State Feedback EMPC of Hyperbolic PDE Systems -- 13.3.4. Output Feedback EMPC of Hyperbolic PDE Systems -- 13.4. Conclusion -- References. |
| Bibliography |
Includes bibliographical references and index. |
| Subject |
Chemical engineering.
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Chemical Engineering |
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Génie chimique.
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chemical engineering.
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SCIENCE -- Chemistry -- Industrial & Technical.
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TECHNOLOGY & ENGINEERING -- Chemical & Biochemical.
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Chemical engineering
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| Added Title |
Process control |
| Other Form: |
Print version : 9780081010952 |
| ISBN |
9780081012246 (electronic bk.) |
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0081012241 (electronic bk.) |
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0081010958 |
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9780081010952 |
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9780081010952 |
| Standard No. |
AU@ 000060696310 |
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AU@ 000068133765 |
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CHNEW 001014496 |
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GBVCP 897846990 |
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UKMGB 018430864 |
