Muutke küpsiste eelistusi

E-raamat: Computational Techniques for Process Simulation and Analysis Using MATLAB®

(Indian Institute of Technology, Chennai, India)
  • Formaat: 562 pages
  • Ilmumisaeg: 18-Sep-2017
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781498762120
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 93,59 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Computational Techniques for Process Simulation and Analysis Using MATLAB®Suurem pilt
  • Formaat: 562 pages
  • Ilmumisaeg: 18-Sep-2017
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781498762120
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

MATLAB® has become one of the prominent languages used in research and industry and often described as "the language of technical computing". The focus of this book will be to highlight the use of MATLAB® in technical computing; or more specifically, in solving problems in Process Simulations. This book aims to bring a practical approach to expounding theories: both numerical aspects of stability and convergence, as well as linear and nonlinear analysis of systems.

The book is divided into three parts which are laid out with a "Process Analysis" viewpoint. First part covers system dynamics followed by solution of linear and nonlinear equations, including Differential Algebraic Equations (DAE) while the last part covers function approximation and optimization. Intended to be an advanced level textbook for numerical methods, simulation and analysis of process systems and computational programming lab, it covers following key points

Comprehensive coverage of numerical analyses based on MATLAB for chemical process examples.

Includes analysis of transient behavior of chemical processes.

Discusses coding hygiene, process animation and GUI exclusively.

Treatment of process dynamics, linear stability, nonlinear analysis and function approximation through contemporary examples.

Focus on simulation using MATLAB to solve ODEs and PDEs that are frequently encountered in process systems.
Preface xix
Author xxiii
Chapter 1 Introduction
1(26)
1.1 Overview
1(2)
1.1.1 A General Model
1(1)
1.1.2 A Process Example
2(1)
1.1.3 Analysis of Dynamical Systems
3(1)
1.2 Structure Of A MATLAB® Code
3(9)
1.2.1 Writing Our First MATLAB® Script
5(2)
1.2.2 MATLAB® Functions
7(2)
1.2.3 Using Array Operations in MATLAB®
9(1)
1.2.4 Loops and Execution Control
10(1)
1.2.5 Section Recap
11(1)
1.3 Approximations And Errors In Numerical Methods
12(8)
1.3.1 Machine Precision
12(2)
1.3.2 Round-Off Error
14(1)
1.3.3 Taylor's Series and Truncation Error
15(3)
1.3.4 Trade-Off between Truncation and Round-Off Errors
18(2)
1.4 Error Analysis
20(3)
1.4.1 Convergence and Stability
20(1)
1.4.2 Global Truncation Error
21(2)
1.5 Outlook
23(4)
Section I Dynamic Simulations and Linear Analysis
Chapter 2 Linear Algebra
27(42)
2.1 Introduction
27(3)
2.1.1 Solving a System of Linear Equations
27(1)
2.1.2 Overview
28(2)
2.2 Vector Spaces
30(11)
2.2.1 Definition and Properties
30(2)
2.2.2 Span, Linear Independence, and Subspaces
32(2)
2.2.3 Basis and Coordinate Transformation
34(1)
2.2.3.1 Change of Basis
34(1)
2.2.4 Null (Kernel) and Image Spaces of a Matrix
35(6)
2.2.4.1 Matrix as Linear Operator
35(4)
2.2.4.2 Null and Image Spaces in MATLAB®
39(2)
2.3 Singular Value Decomposition
41(13)
2.3.1 Orthonormal Vectors
41(1)
2.3.2 Singular Value Decomposition
42(5)
2.3.3 Condition Number
47(4)
2.3.3.1 Singular Values, Rank, and Condition Number
47(1)
2.3.3.2 Sensitivity of Solutions to Linear Equations
47(4)
2.3.4 Directionality
51(3)
2.4 Eigenvalues And Eigenvectors
54(11)
2.4.1 Orientation for This Section
54(1)
2.4.2 Brief Recap of Definitions
54(2)
2.4.3 Eigenvalue Decomposition
56(2)
2.4.4 Applications
58(11)
2.4.4.1 Similarity Transform
62(1)
2.4.4.2 Linear Differential Equations
63(1)
2.4.4.3 Linear Difference Equations
64(1)
2.5 Epilogue
65(2)
Exercises
67(2)
Chapter 3 Ordinary Differential Equations: Explicit Methods
69(58)
3.1 General Setup
69(13)
3.1.1 Some Examples
69(3)
3.1.2 Geometric Interpretation
72(2)
3.1.3 Euler's Explicit Method
74(2)
3.1.4 Euler's Implicit Method
76(2)
3.1.5 Stability and Step-Size
78(2)
3.1.5.1 Stability of Euler's Explicit Method
78(1)
3.1.5.2 Error and Stability of Euler's Implicit Method
79(1)
3.1.6 Multivariable ODE
80(2)
3.1.6.1 Nonlinear Case
81(1)
3.2 Second-Order Methods: A Journey Through The Woods
82(11)
3.2.1 Some History
82(1)
3.2.2 Runge-Kutta (RK-2) Methods
83(4)
3.2.2.1 Derivation for RK-2 Methods
83(1)
3.2.2.2 Heun's Method
84(2)
3.2.2.3 Other RK-2 Methods
86(1)
3.2.3 Step-Size Halving: Error Estimate for RK-2
87(2)
3.2.4 Richardson's Extrapolation
89(2)
3.2.5 Other Second-Order Methods (*)
91(2)
3.2.5.1 Trapezoidal Rule: An Implicit Second-Order Method
91(1)
3.2.5.2 Second-Order Adams-Bashforth Methods
92(1)
3.2.5.3 Predictor-Corrector Methods
92(1)
3.2.5.4 Backward Differentiation Formulae
93(1)
3.3 Higher-Order Runge-Kutta Methods
93(10)
3.3.1 Explicit Runge-Kutta Methods: Generalization
93(4)
3.3.2 Error Estimation and Embedded RK Methods
97(4)
3.3.2.1 MATLAB® Solver ode 23
100(1)
3.3.3 The Workhorse: Fourth-Order Runge-Kutta
101(4)
3.3.3.1 Classical RK-4 Method(s)
102(1)
3.3.3.2 Kutta's 3/8th Rule RK-4 Method
103(1)
3.4 MATLAB® ODE45 Solver: Options And Parameterization
103(2)
3.5 Case Studies And Examples
105(20)
3.5.1 An Ideal PFR
106(5)
3.5.1.1 Simulation of PFR as ODE-IVP
106(2)
3.5.1.2 Numerical Integration for PFR Design
108(2)
3.5.1.3 Comparison of ODE-IVP with Integration
110(1)
3.5.2 Multiple Steady States: Nonisothermal CSTR
111(5)
3.5.2.1 Model and Problem Setup
111(2)
3.5.2.2 Simulation of Transient CSTR
113(2)
3.5.2.3 Step Change in Inlet Temperature
115(1)
3.5.3 Hybrid System: Two-Tank with Heater
116(4)
3.5.4 Chemostat: Preview into "Stiff" System
120(5)
3.6 Epilogue
125(1)
Exercises
125(2)
Chapter 4 Partial Differential Equations in Time
127(52)
4.1 General Setup
127(6)
4.1.1 Classification of PDEs
128(1)
4.1.2 Brief History of Second-Order PDEs
128(1)
4.1.3 Classification of Second-Order PDEs and Practical Implications
129(3)
4.1.3.1 Elliptic PDE
129(1)
4.1.3.2 Hyperbolic PDE
130(1)
4.1.3.3 First-Order Hyperbolic PDEs
131(1)
4.1.3.4 Parabolic PDE
132(1)
4.1.4 Initial and Boundary Conditions
132(1)
4.2 A Brief Overview Of Numerical Methods
133(2)
4.2.1 Finite Difference
133(1)
4.2.2 Method of Lines
134(1)
4.2.3 Finite Volume Methods
134(1)
4.2.4 Finite Element Methods
135(1)
4.3 Hyperbolic PDE: Convective Systems
135(15)
4.3.1 Finite Differences in Space and Time
136(4)
4.3.1.1 Upwind Difference in Space
136(2)
4.3.1.2 Forward in Time Central in Space (FTCS) Differencing
138(1)
4.3.1.3 Lax-Friedrichs Scheme
139(1)
4.3.1.4 Higher-Order Methods
139(1)
4.3.2 Crank-Nicolson: Second-Order Implicit Method
140(1)
4.3.2.1 Preview of Numerical Solution
141(1)
4.3.3 Solution Using Method of Lines
141(8)
4.3.3.1 MoL with Central Difference in Space
142(3)
4.3.3.2 MoL with Upwind Difference in Space
145(4)
4.3.4 Numerical Diffusion
149(1)
4.4 Parabolic PDE: Diffusive Systems
150(7)
4.4.1 Finite Difference in Space and Time
152(1)
4.4.2 Crank-Nicolson Method
153(1)
4.4.3 Method of Lines Using MATLAB® ODE Solvers
154(3)
4.4.3.1 MoL with Central Difference in Space
154(3)
4.4.4 Methods to Improve Stability
157(1)
4.5 Case Studies And Examples
157(19)
4.5.1 Nonisothermal Plug Flow Reactor
157(7)
4.5.2 Packed Bed Reactor with Multiple Reactions
164(6)
4.5.3 Steady Graetz Problem: Parabolic PDE in Two Spatial Dimensions
170(11)
4.5.3.1 Heat Transfer in Fluid Flowing through a Tube
170(4)
4.5.3.2 Effect of Velocity Profile
174(1)
4.5.3.3 Calculation of Nusselt Number
174(2)
4.6 Epilogue
176(1)
Exercises
177(2)
Chapter 5 Section Wrap-Up: Simulation and Analysis
179(46)
5.1 Binary Distillation Column: Staged Ode Model
181(5)
5.1.1 Model Description
181(2)
5.1.2 Model Equations and Simulation
183(2)
5.1.3 Effect of Parameters: Reflux Ratio and Relative Volatility
185(1)
5.2 Stability Analysis For Linear Systems
186(15)
5.2.1 Motivation: Linear Stability Analysis of a Chemostat
187(4)
5.2.1.1 Phase Portrait at the Steady State
190(1)
5.2.1.2 Trivial Steady State and Analysis
190(1)
5.2.2 Eigenvalues, Stability, and Dynamics
191(7)
5.2.2.1 Dynamics When Eigenvalues Are Real and Distinct
192(5)
5.2.2.2 An Example
197(1)
5.2.2.3 Summary
197(1)
5.2.3 Transient Growth in Stable Linear Systems
198(10)
5.2.3.1 Defining Normal and Nonnormal Matrices
198(1)
5.2.3.2 Analysis of Nonnormal Systems
199(2)
5.3 Combined Parabolic PDE With ODE-IVP: Polymer Curing
201(7)
5.4 Time-Varying Inlet Conditions And Process Disturbances
208(7)
5.4.1 Chemostat with Time-Varying Inlet Flowrate
208(4)
5.4.2 Zero-Order Hold Reconstruction in Digital Control
212(3)
5.5 Simulating System With Boundary Constraints
215(4)
5.5.1 PFR with Temperature Profile Specified
216(3)
5.6 Wrap-Up
219(1)
Exercises
219(6)
Section II Linear and Nonlinear Equations and Bifurcation
Chapter 6 Nonlinear Algebraic Equations
225(48)
6.1 General Setup
225(2)
6.1.1 A Motivating Example: Equation of State
226(1)
6.2 Equations In Single Variable
227(14)
6.2.1 Bisection Method
228(5)
6.2.2 Secant and Related Methods
233(3)
6.2.2.1 Regula-Falsi: Method of False Position
235(1)
6.2.2.2 Brent's Method
235(1)
6.2.3 Fixed Point Iteration
236(2)
6.2.4 Newton-Raphson in Single Variable
238(2)
6.2.5 Comparison of Numerical Methods
240(1)
6.3 Newton-Raphson: Extensions And Multivariate
241(8)
6.3.1 Multivariate Newton-Raphson
241(4)
6.3.2 Modified Secant Method
245(2)
6.3.3 Line Search and Other Methods
247(2)
6.4 MATLAB® Solvers
249(4)
6.4.1 Single Variable Solver: f zero
249(1)
6.4.2 Multiple Variable Solver: f solve
250(3)
6.5 Case Studies And Examples
253(15)
6.5.1 Recap: Equation of State
253(1)
6.5.2 Two-Phase Vapor-Liquid Equilibrium
253(4)
6.5.2.1 Bubble Temperature Calculation
254(1)
6.5.2.2 Dew Temperature Calculation
254(1)
6.5.2.3 Generating the T-x-y Diagram
255(2)
6.5.3 Steady State Multiplicity in CSTR
257(4)
6.5.4 Recap: Chemostat
261(1)
6.5.5 Integral Equations: Conversion from a PFR
262(11)
6.5.5.1 First-Order Kinetics
263(3)
6.5.5.2 Complex Kinetics
266(2)
6.6 Epilogue
268(3)
Exercises
271(2)
Chapter 7 Special Methods for Linear and Nonlinear Equations
273(40)
7.1 General Setup
273(2)
7.1.1 Ordinary Differential Equation-Boundary Value Problems
274(1)
7.1.2 Elliptic PDEs
274(1)
7.1.3 Outlook of This
Chapter
275(1)
7.2 Tridiagonal And Banded Systems
275(15)
7.2.1 What Is a Banded System?
275(1)
7.2.1.1 Tridiagonal Matrix
276(1)
7.2.2 Thomas Algorithm a.k.a TDMA
276(9)
7.2.2.1 Heat Conduction Problem
277(4)
7.2.2.2 Thomas Algorithm
281(4)
7.2.3 ODE-BVP with Flux Specified at Boundary
285(3)
7.2.4 Extension to Banded Systems
288(1)
7.2.5 Elliptic PDEs in Two Dimensions
289(1)
7.3 Iterative Methods
290(6)
7.3.1 Gauss-Siedel Method
291(4)
7.3.2 Iterative Method with Under-Relaxation
295(1)
7.4 Nonlinear Banded Systems
296(8)
7.4.1 Nonlinear ODE-BVP Example
296(2)
7.4.1.1 Heat Conduction with Radiative Heat Loss
297(1)
7.4.2 Modified Successive Linearization-Based Approach
298(4)
7.4.3 Gauss-Siedel with Linearization of Source Term
302(2)
7.4.4 Using f solve with Sparse Systems
304(1)
7.5 Examples
304(7)
7.5.1 Heat Conduction with Convective or Radiative Losses
304(1)
7.5.2 Diffusion and Reaction in a Catalyst Pellet
305(8)
7.5.2.1 Linear System and Thiele Modulus
305(3)
7.5.2.2 Langmuir-Hinshelwood Kinetics in a Pellet
308(3)
7.6 Epilogue
311(1)
Exercises
311(2)
Chapter 8 Implicit Methods: Differential and Differential Algebraic Systems
313(50)
8.1 General Setup
313(4)
8.1.1 Stiff System of Equation
313(3)
8.1.1.1 Stiff ODE in Single Variable
315(1)
8.1.2 Implicit Methods for Distributed Parameter Systems
316(1)
8.1.3 Differential Algebraic Equations
316(1)
8.2 Multistep Methods For Differential Equations
317(10)
8.2.1 Implicit Adams-Moulton Methods
318(1)
8.2.2 Higher-Order Adams-Moulton Method
319(1)
8.2.3 Explicit Adams-Bashforth Method
320(2)
8.2.4 Backward Difference Formula
322(3)
8.2.5 Stability and MATLAB® Solvers
325(2)
8.2.5.1 Explicit Adams-Bashforth Methods
325(1)
8.2.5.2 Implicit Euler and Trapezoidal Methods
325(1)
8.2.5.3 Implicit Adams-Moulton Methods of Higher Order
325(1)
8.2.5.4 BDF/NDF Methods
325(1)
8.2.5.5 MATLAB® Nonstiff Solvers
326(1)
8.2.5.6 MATLAB® Stiff Solvers
326(1)
8.3 Implicit Solutions For Differential Equations
327(10)
8.3.1 Trapezoidal Method for Stiff ODE
327(4)
8.3.1.1 Adaptive Step-Sizing
329(1)
8.3.1.2 Multivariable Example
330(1)
8.3.2 Crank-Nicolson Method for Hyperbolic PDEs
331(6)
8.3.2.1 Exploiting Sparse Structure for Efficient Simulation
337(1)
8.4 Differential Algebraic Equations
337(14)
8.4.1 An Introductory Example
338(2)
8.4.1.1 Direct Substitution
338(1)
8.4.1.2 Formulating and Solving a DAE
339(1)
8.4.2 Index of a DAE and More Examples
340(2)
8.4.2.1 Example 2: Pendulum in Cartesian Coordinate System
341(1)
8.4.2.2 Example 3: Heterogeneous Catalytic Reactor
341(1)
8.4.3 Solution Methodology: Overview
342(6)
8.4.3.1 Solving Algebraic Equation within ODE
343(2)
8.4.3.2 Combined Approach
345(3)
8.4.4 Solving Semiexplicit DAEs Using ode 15 s in MATLAB®
348(3)
8.5 Case Studies And Examples
351(8)
8.5.1 Heterogeneous Catalytic Reactor: Single Complex Reaction
351(2)
8.5.2 Flash Separation/Batch Distillation
353(6)
8.6 Epilogue
359(1)
Exercises
360(3)
Chapter 9 Section Wrap-Up: Nonlinear Analysis
363(46)
9.1 Nonlinear Analysis Of Chemostat: "Transcritical" Bifurcation
364(8)
9.1.1 Steady State Multiplicity and Stability
364(1)
9.1.2 Phase-Plane Analysis
365(1)
9.1.3 Bifurcation with Variation in Dilution Rate
366(2)
9.1.4 Transcritical Bifurcation
368(4)
9.2 Nonisothermal CSTR: "Turning-Point" Bifurcation
372(7)
9.2.1 Steady States: Graphical Approach
372(2)
9.2.2 Stability Analysis at Steady States
374(2)
9.2.3 Phase-Plane Analysis
376(1)
9.2.4 Turning-Point Bifurcation
377(2)
9.3 Limit Cycle Oscillations
379(8)
9.3.1 Oscillations in Linear Systems
379(2)
9.3.2 Limit Cycles: van der Pol Oscillator
381(2)
9.3.2.1 Relaxation vs. Harmonic Oscillations
382(1)
9.3.3 Oscillating Chemical Reactions
383(4)
9.4 Simulation Of Methanol Synthesis In Tubular Reactor
387(11)
9.4.1 Steady State PFR with Pressure Drop
388(6)
9.4.1.1 Reaction Kinetics
388(1)
9.4.1.2 Input Parameters and Initial Processing
389(2)
9.4.1.3 Steady State PFR Model
391(3)
9.4.2 Transient Model
394(4)
9.5 Trajectory Of A Cricket Ball
398(7)
9.5.1 Solving the ODE for Trajectory
399(1)
9.5.2 Location Where the Ball Hits the Ground
400(3)
9.5.3 Animation
403(2)
9.6 Wrap-Up
405(1)
Exercises
405(4)
Section III Modeling of Data
Chapter 10 Regression and Parameter Estimation
409(42)
10.1 General Setup
409(4)
10.1.1 Orientation
410(1)
10.1.2 Some Statistics
411(2)
10.1.3 Some Other Considerations in Regression
413(1)
10.2 Linear Least Squares Regression
413(10)
10.2.1 Fitting a Straight Line
413(2)
10.2.2 General Matrix Approach
415(3)
10.2.3 Goodness of Fit
418(5)
10.2.3.1 Maximum Likelihood Solution
418(1)
10.2.3.2 Error and Coefficient of Determination
419(4)
10.3 Regression In Multiple Variables
423(6)
10.3.1 General Multilinear Regression
423(1)
10.3.2 Polynomial Regression
424(4)
10.3.3 Singularity and SVD
428(1)
10.4 Nonlinear Estimation
429(8)
10.4.1 Functional Regression by Linearization
429(3)
10.4.2 MATLAB® Solver: Linear Regression
432(2)
10.4.3 Nonlinear Regression Using Optimization Toolbox
434(3)
10.5 Case Studies And Examples
437(11)
10.5.1 Specific Heat: Revisited
437(1)
10.5.2 Antoine's Equation for Vapor Pressure
438(3)
10.5.2.1 Linear Regression for Benzene
439(1)
10.5.2.2 Nonlinear Regression for Ethylbenzene
440(1)
10.5.3 Complex Langmuir-Hinshelwood Kinetic Model
441(4)
10.5.3.1 Case 1: Experiments Performed at Single Concentration of B
442(2)
10.5.3.2 Case 2: Experiments Performed at Different Initial Concentrations of B
444(1)
10.5.4 Reaction Rate: Differential Approach
445(3)
10.6 Epilogue
448(2)
10.6.1 Summary
448(1)
10.6.2 Data Tables
449(1)
Exercises
450(1)
Appendix A: MATLAB® Primer 451(24)
Appendix B: Numerical Differentiation 475(10)
Appendix C: Gauss Elimination For Linear Equations 485(14)
Appendix D: Interpolation 499(12)
Appendix E: Numerical Integration 511(16)
Bibliography 527(2)
Index 529
Dr. Niket Kaisare is currently an Associate Professor in the Department of Chemical Engineering at IIT-Madras. He obtained PhD in Chemical Engineering from Georgia Institute of Technology, working in model-based advanced process control. After a post-doc in the department of chemical engineering at University of Delaware, he joined IIT-Madras as Assistant Professor in 2007. While in IIT-Madras, he taught several courses in process modeling and analysis, computational techniques, process simulation laboratory, and advanced control theory. His courses have consistently gotten good student feedback.

He spent three years, from mid-2011 to 2014, in Industrial R&D. During this stint, he worked on numerous simulation problems related to modeling of vehicle catalytic convertors, cryogenic hydrogen storage, monitoring and control of oil and gas wells, and automation engineering. As a part of R&D team, he used MATLAB extensively and spent important fraction of his time interfacing with engineering and development teams.

He has extensive experience working in MATLAB and FORTRAN as well as simulation software Fluent and Comsol. He also has good working experience with various other simulation tools, such as Aspen-Plus / Unisim, Gaussian and Abacus. His current research program is focused on "multi-scale modeling, analysis and control of catalytic micro-reactors for energy and fuel processing applications."
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97814987621202e.html
Märksõnad: