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Applied Mathematics and Modeling for Chemical Engineers. Edition No. 3

  • Book

  • 432 Pages
  • April 2023
  • John Wiley and Sons Ltd
  • ID: 5839032
Understand the fundamentals of applied mathematics with this up-to-date introduction

Applied mathematics is the use of mathematical concepts and methods in various applied or practical areas, including engineering, computer science, and more. As engineering science expands, the ability to work from mathematical principles to solve and understand equations has become an ever more critical component of engineering fields. New engineering processes and materials place ever-increasing mathematical demands on new generations of engineers, who are looking more and more to applied mathematics for an expanded toolkit.

Applied Mathematics and Modeling for Chemical Engineers provides this toolkit in a comprehensive and easy-to-understand introduction. Combining classical analysis of modern mathematics with more modern applications, it offers everything required to assess and solve mathematical problems in chemical engineering. Now updated to reflect contemporary best practices and novel applications, this guide promises to situate readers in a 21st century chemical engineering field in which direct knowledge of mathematics is essential.

Readers of the third edition of Applied Mathematics and Modeling for Chemical Engineers will also find: - Detailed treatment of ordinary differential equations (ODEs) and partial differential equations (PDEs) and their solutions - New material concerning approximate solution methods like perturbation techniques and elementary numerical solutions - Two new chapters dealing with Linear Algebra and Applied Statistics

Applied Mathematics and Modeling for Chemical Engineers is ideal for graduate and advanced undergraduate students in chemical engineering and related fields, as well as instructors and researchers seeking a handy reference.

Table of Contents

Preface to the Third Edition xv

Part I 1

1 Formulation of Physicochemical Problems 3

1.1 Introduction 3

1.2 Illustration of the Formulation Process (Cooling of Fluids) 3

1.2.1 Model I: Plug Flow 3

1.2.2 Model II: Parabolic Velocity 6

1.3 Combining Rate and Equilibrium Concepts (Packed-Bed Adsorber) 7

1.4 Boundary Conditions and Sign Conventions 8

1.5 Summary of the Model Building Process 9

1.6 Model Hierarchy and its Importance in Analysis 10

1.6.1 Level 1 10

1.6.2 Level 2 11

1.6.3 Level 3 13

1.6.4 Level 4 13

Problems 15

References 20

2 Modeling with Linear Algebra and Matrices 21

2.1 Introduction 21

2.2 Basic Concepts of Systems of Linear Equations 21

2.3 Matrix Notation 22

2.3.1 Matrices 22

2.3.2 Vectors 22

2.3.3 Scalars 22

2.3.4 Matrices and Vectors with Special Structure 22

2.4 Matrix Algebra and Calculus Operations 24

2.4.1 Equality 24

2.4.2 Addition and Subtraction 24

2.4.3 Multiplication 24

2.4.4 Division 26

2.4.5 Further Algebraic Properties of Matrices 27

2.4.6 Basic Differential and Integral Relations for Matrices 28

2.5 Problem 1: Solution of N Equations in N Unknowns 29

2.5.1 Analytical Results 29

2.5.2 Computational Approach: Gauss Elimination 30

2.6 Problem 2: The Matrix Eigenvalue Problem 32

2.6.1 Problem Statement and Formal Solution 32

2.6.2 Computing Eigensystems: Basic Procedure 33

2.7 Singular Systems 34

2.7.1 Consistent and Inconsistent Systems 34

2.7.2 Solution Structure for Consistent Systems 35

2.7.3 Formulation and Characteristics of Non-Square Problems 36

2.7.4 Over-Determined Systems: Least-Squares Solution 37

2.7.5 Under-Determined Systems 38

2.8 Computational Linear Algebra 40

2.8.1 The LU Factorization 40

2.8.2 The QR Factorization 40

2.8.3 The SVD Factorization 40

2.8.4 Large-Scale Problems and Iterative Methods 41

Problems 42

References 47

3 Solution Techniques for Models Yielding Ordinary Differential Equations 49

3.1 Geometric Basis and Functionality 49

3.2 Classification of ODE 50

3.3 First-Order Equations 50

3.3.1 Exact Solutions 51

3.3.2 Equations Composed of Homogeneous Functions 52

3.3.3 Bernoulli’s Equation 52

3.3.4 Riccati’s Equation 52

3.3.5 Linear Coefficients 54

3.3.6 First-Order Equations of Second Degree 54

3.4 Solution Methods for Second-Order Nonlinear Equations 55

3.4.1 Derivative Substitution Method 55

3.4.2 Homogeneous Function Method 58

3.5 Linear Equations of Higher Order 59

3.5.1 Second-Order Unforced Equations: Complementary Solutions 60

3.5.2 Particular Solution Methods for Forced Equations 64

3.5.3 Summary of Particular Solution Methods 70

3.6 Coupled Simultaneous ODE 71

3.7 Eigenproblems 74

3.8 Coupled Linear Differential Equations 74

3.9 Summary of Solution Methods for ODE 75

Problems 75

References 87

4 Series Solution Methods and Special Functions 89

4.1 Introduction to Series Methods 89

4.2 Properties of Infinite Series 90

4.3 Method of Frobenius 91

4.3.1 Indicial Equation and Recurrence Relation 91

4.4 Summary of the Frobenius Method 98

4.5 Special Functions 98

4.5.1 Bessel’s Equation 99

4.5.2 Modified Bessel’s Equation 100

4.5.3 Generalized Bessel’s Equation 100

4.5.4 Properties of Bessel Functions 102

4.5.5 Differential, Integral, and Recurrence Relations 103

Problems 105

References 107

5 Integral Functions 109

5.1 Introduction 109

5.2 The Error Function 109

5.2.1 Properties of Error Function 110

5.3 The Gamma and Beta Functions 110

5.3.1 The Gamma Function 110

5.3.2 The Beta Function 111

5.4 The Elliptic Integrals 111

5.5 The Exponential and Trigonometric Integrals 113

Problems 113

References 116

6 Staged-Process Models: The Calculus of Finite Differences 117

6.1 Introduction 117

6.1.1 Modeling Multiple Stages 117

6.2 Solution Methods for Linear Finite Difference Equations 118

6.2.1 Complementary Solutions 118

6.3 Particular Solution Methods 121

6.3.1 Method of Undetermined Coefficients 121

6.3.2 Inverse Operator Method 122

6.4 Nonlinear Equations (Riccati Equation) 122

Problems 124

References 126

7 Probability and Statistical Modeling 127

7.1 Concepts and Results From Probability Theory 127

7.1.1 Experiments and Random Variables 127

7.1.2 Probabilities and Distribution Functions 128

7.1.3 Characteristics of Distributions Functions 131

7.1.4 The Cumulative Distribution Function 132

7.2 Concepts and Results From Mathematical Statistics 134

7.2.1 Populations, Samples, and Sampling 134

7.2.2 Sample Statistics and Sampling Distributions 134

7.3 Statistical Analysis and Modeling 137

7.3.1 Confidence Interval for the Mean of a Population 137

7.3.2 Hypothesis Tests for the Population Mean 138

7.3.3 Hypothesis Tests: Comparing Multiple Means 140

7.3.4 Linear Models and Linear Regression 143

Problems 150

References 154

8 Approximate Solution Methods for ODE: Perturbation Methods 155

8.1 Perturbation Methods 155

8.1.1 Introduction 155

8.2 The Basic Concepts 157

8.2.1 Gauge Functions 157

8.2.2 Order Symbols 158

8.2.3 Asymptotic Expansions and Sequences 158

8.2.4 Sources of Nonuniformity 159

8.3 The Method of Matched Asymptotic Expansion 160

8.3.1 Outer Solutions 160

8.3.2 Inner Solutions 160

8.3.3 Matching 161

8.3.4 Composite Solutions 161

8.3.5 General Matching Principle 162

8.3.6 Composite Solution of Higher Order 162

8.4 Matched Asymptotic Expansions for Coupled Equations 163

8.4.1 Outer Expansion 163

8.4.2 Inner Expansion 164

8.4.3 Matching 164

Problems 165

References 173

Part II 175

9 Numerical Solution Methods (Initial Value Problems) 177

9.1 Introduction 177

9.2 Type of Method 179

9.3 Stability 180

9.4 Stiffness 185

9.5 Interpolation and Quadrature 186

9.6 Explicit Integration Methods 187

9.7 Implicit Integration Methods 188

9.8 Predictor-Corrector Methods and Runge-Kutta Methods 189

9.8.1 Predictor-Corrector Methods 189

9.9 Runge-Kutta Methods 189

9.10 Extrapolation 191

9.11 Step Size Control 192

9.12 Higher-Order Integration Methods 192

Problems 192

References 195

10 Approximate Methods for Boundary Value Problems: Weighted Residuals 197

10.1 The Method of Weighted Residuals 197

10.1.1 Variations on a Theme of Weighted Residuals 198

10.2 Jacobi Polynomials 205

10.2.1 Rodrigues Formula 205

10.2.2 Orthogonality Conditions 205

10.3 Lagrange Interpolation Polynomials 206

10.4 Orthogonal Collocation Method 206

10.4.1 Differentiation of a Lagrange Interpolation Polynomial 206

10.4.2 Gauss-Jacobi Quadrature 207

10.4.3 Radau and Lobatto Quadrature 208

10.5 Linear Boundary Value Problem: Dirichlet Boundary Condition 209

10.6 Linear Boundary Value Problem: Robin Boundary Condition 211

10.7 Nonlinear Boundary Value Problem: Dirichlet Boundary Condition 213

10.8 One-Point Collocation 215

10.9 Summary of Collocation Methods 215

10.10 Concluding Remarks 216

Problems 217

References 225

11 Introduction to Complex Variables and Laplace Transforms 227

11.1 Introduction 227

11.2 Elements of Complex Variables 227

11.3 Elementary Functions of Complex Variables 228

11.4 Multivalued Functions 229

11.5 Continuity Properties for Complex Variables: Analyticity 230

11.5.1 Exploiting Singularities 231

11.6 Integration: Cauchy’s Theorem 232

11.7 Cauchy’s Theory of Residues 233

11.7.1 Practical Evaluation of Residues 234

11.7.2 Residues at Multiple Poles 235

11.8 Inversion of Laplace Transforms by Contour Integration 235

11.8.1 Summary of Inversion Theorem for Pole Singularities 237

11.9 Laplace Transformations: Building Blocks 237

11.9.1 Taking the Transform 237

11.9.2 Transforms of Derivatives and Integrals 238

11.9.3 The Shifting Theorem 240

11.9.4 Transform of Distribution Functions 240

11.10 Practical Inversion Methods 242

11.10.1 Partial Fractions 242

11.10.2 Convolution Theorem 243

11.11 Applications of Laplace Transforms for Solutions of ODE 243

11.12 Inversion Theory for Multivalued Functions: The Second Bromwich Path 248

11.12.1 Inversion When Poles and Branch Points Exist 250

11.13 Numerical Inversion Techniques 250

11.13.1 The Zakian Method 250

11.13.2 The Fourier Series Approximation 252

Problems 253

References 257

12 Solution Techniques for Models Producing PDEs 259

12.1 Introduction 259

12.1.1 Classification and Characteristics of Linear Equations 261

12.2 Particular Solutions for PDEs 263

12.2.1 Boundary and Initial Conditions 263

12.3 Combination of Variables Method 264

12.4 Separation of Variables Method 269

12.4.1 Coated Wall Reactor 269

12.5 Orthogonal Functions and Sturm-Liouville Conditions 272

12.5.1 The Sturm-Liouville Equation 272

12.6 Inhomogeneous Equations 275

12.7 Applications of Laplace Transforms for Solutions of PDEs 279

Problems 285

References 302

13 Transform Methods for Linear PDEs 305

13.1 Introduction 305

13.2 Transforms in Finite Domain: Sturm-Liouville Transforms 305

13.2.1 Development of Integral Transform Pairs 306

13.2.2 The Eigenvalue Problem and the Orthogonality Condition 309

13.2.3 Inhomogeneous Boundary Conditions 313

13.2.4 Inhomogeneous Equations 316

13.2.5 Time-Dependent Boundary Conditions 317

13.2.6 Elliptic Partial Differential Equations 317

13.3 Generalized Sturm-Liouville Integral Transform 320

13.3.1 Introduction 320

13.3.2 The Batch Adsorber Problem 320

Problems 327

References 331

14 Approximate and Numerical Solution Methods for PDEs 333

14.1 Polynomial Approximation 333

14.2 Singular Perturbation 338

14.3 Finite Difference 343

14.3.1 Notations 343

14.3.2 Essence of the Method 344

14.3.3 Tridiagonal Matrix and the Thomas Algorithm 345

14.3.4 Linear Parabolic Partial Differential Equations 345

14.3.5 Nonlinear Parabolic Partial Differential Equations 349

14.4 Orthogonal Collocation for Solving PDEs 350

14.4.1 Elliptic PDE 350

14.4.2 Parabolic PDE: Example 1 353

14.4.3 Coupled Parabolic PDE: Example 2 354

Problems 355

References 362

Appendix A: Review of Methods for Nonlinear Algebraic Equations 363

A.1 The Bisection Algorithm 363

A.2 The Successive Substitution Method 364

A.3 The Newton-Raphson Method 366

A.4 Rate of Convergence 367

A.4.1 Definition of Speed of Convergence 367

A.5 Multiplicity 368

A.5.1 Multiplicity 368

A.6 Accelerating Convergence 369

References 369

Appendix B: Derivation of the Fourier-Mellin Inversion Theorem 371

References 374

Appendix C: Table of Laplace Transforms 375

Appendix D: Numerical Integration 381

D.1 Basic Idea of Numerical Integration 381

D.2 Newton Forward Difference Polynomial 381

D.3 Basic Integration Procedure 382

D.3.1 Trapezoid Rule 382

D.3.2 Simpson’s Rule 383

D.4 Error Control and Extrapolation 384

D.5 Gaussian Quadrature 384

D.6 Radau Quadrature 386

D.7 Lobatto Quadrature 388

D.8 Concluding Remarks 389

References 389

Appendix E: Nomenclature 391

Appendix F: Statistical Tables 395

Postface 399

Index 401

Authors

Richard G. Rice Louisiana State University. Duong D. Do University of Queensland. James E. Maneval Bucknell University, Lewisburg, Pennsylvania, USA.