Coulson and Richardson's chemical engineering. Volume 3B, Process control /

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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 technic...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rohani, Sohrab (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Kidlington, Oxford : Butterworth-Heinemann, 2017.
Edición:Fourth edition.
Temas:
Chemical engineering.
Chemical Engineering
G�enie chimique.
chemical engineering.
SCIENCE > Chemistry > Industrial & Technical.
TECHNOLOGY & ENGINEERING > Chemical & Biochemical.
Chemical engineering
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.

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