Screen LibraryCoulson and Richardson's chemical engineering. Volume 3B, Process control /
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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| Main Authors: | |
|---|---|
| Corporate Authors: | |
| Published: |
Butterworth-Heinemann,
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| Publisher Address: | Kidlington, Oxford : |
| Publication Dates: | 2017. |
| Literature type: | eBook |
| Language: | English |
| Edition: | Fourth edition. |
| Subjects: | |
| Online Access: |
http://www.sciencedirect.com/science/book/9780081010952 |
| 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. |
| Item Description: | Includes index. |
| Carrier Form: | 1 online resource |
| ISBN: |
9780081012246 0081012241 |
| Index Number: | TP155 |
| CLC: | TQ02 |
| 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 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. |