Muutke küpsiste eelistusi

Applied Numerical Methods for Chemical Engineers [Pehme köide]

(Rutgers, The State University of New Jersey (deceased)), (Professor, School of Chemical Engineering, University of Tehran, Iran)
  • Formaat: Paperback / softback, 504 pages, kõrgus x laius: 235x191 mm, kaal: 820 g
  • Ilmumisaeg: 18-Aug-2022
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0128229616
  • ISBN-13: 9780128229613
Teised raamatud teemal:
Applied Numerical Methods for Chemical EngineersSuurem pilt
  • Formaat: Paperback / softback, 504 pages, kõrgus x laius: 235x191 mm, kaal: 820 g
  • Ilmumisaeg: 18-Aug-2022
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0128229616
  • ISBN-13: 9780128229613
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128229613.html
Märksõnad:
Applied Numerical Methods for Chemical Engineers emphasizes the derivation of a variety of numerical methods and their application to the solution of engineering problems, with special attention to problems in the chemical engineering field. These algorithms encompass linear and nonlinear algebraic equations, eigenvalue problems, finite difference methods, interpolation, differentiation and integration, ordinary differential equations, boundary value problems, partial differential equations, and linear and nonlinear regression analysis. MATLAB is adopted as the calculation environment throughout the book because of its ability to perform all the calculations in matrix form, its large library of built-in functions, its strong structural language, and its rich graphical visualization tools. Through this book, students and other users will learn about the basic features, advantages and disadvantages of various numerical methods, learn and practice many useful m-files developed for different numerical methods in addition to the MATLAB built-in solvers, develop and set up mathematical models for problems commonly encountered in chemical engineering, and solve chemical engineering related problems through examples and after-chapter problems with MATLAB by creating application m-files.
  • Clearly and concisely develops a variety of numerical methods and applies them to the solution of chemical engineering problems. These algorithms encompass linear and nonlinear algebraic equations, eigenvalue problems, finite difference methods, interpolation, linear and nonlinear regression analysis, differentiation and integration, ordinary differential equations, boundary value problems, and partial differential equations
  • Includes systematic development of the calculus of finite differences and its application to the integration of differential equations, and a detailed discussion of nonlinear regression analysis, with powerful programs for implementing multivariable nonlinear regression and statistical analysis of the results
  • Makes extensive use of MATLAB and Excel, with most of the methods discussed implemented into general MATLAB functions. All the MATLAB-language scripts developed are listed in the text and included in the book’s companion website
  • Includes numerous real-world examples and homework problems drawn from the field of chemical and biochemical engineering
Chapter 1 Nonlinear Equations
Chapter Outline
Motivation
1(1)
1.1 Introduction
2(1)
1.2 Types of roots and their approximation
3(4)
1.3 The method of successive substitution
7(3)
1.4 The Wegstein method
10(4)
1.5 The bisection method
14(1)
1.6 The method of linear interpolation
15(6)
1.7 The Newton-Raphson method
21(11)
1.8 Synthetic division algorithm
32(1)
1.9 The eigenvalue method
33(6)
1.10 Newton's method for solving system of nonlinear equations
39(4)
1.11 Homotopy method
43(1)
1.12 Using the built-in MATLAB and Excel functions
44(3)
1.13 Summary
47(16)
Problems
47(14)
References
61(2)
Chapter 2 Simultaneous Linear Algebraic Equations
Chapter Outline
Motivation
63(1)
2.1 Introduction
64(4)
2.2 Review of selected matrix and vector operations
68(10)
2.2.1 Matrices and determinants
68(7)
2.2.2 Matrix transformations
75(1)
2.2.3 Matrix polynomials and power series
76(1)
2.2.4 Vector operations
77(1)
2.3 Consistency of equations and existence of solutions
78(1)
2.4 Cramer's rule
79(1)
2.5 Gauss elimination method
80(11)
2.5.1 Gauss elimination in formula form
81(6)
2.5.2 Gauss elimination in matrix form
87(1)
Calculation of determinants by the Gauss method
88(3)
2.6 Gauss-Jordan Reduction Method
91(8)
2.6.1 Gauss-Jordan reduction in formula form
92(5)
2.6.2 Gauss-Jordan reduction in matrix form
97(1)
2.6.3 Gauss-Jordan reduction with matrix inversion
98(1)
2.7 Gauss-Seidel substitution method
99(6)
2.8 Jacobi method
105(8)
2.9 Homogeneous algebraic equations and the characteristic-value problem
113(11)
2.9.1 The Faddeev-Leverrier method
116(1)
2.9.2 Elementary similarity transformations
117(2)
2.9.3 The QR algorithm of successive factorization
119(5)
2.10 Using built-in MATLAB® and Excel functions
124(1)
2.11 Summary
125(12)
Problems
125(9)
References
134(3)
Chapter 3 Finite Difference Methods And Interpolation
Chapter Outline
Motivation
137(1)
3.1 Introduction
138(1)
3.2 Symbolic operators
138(4)
3.3 Backward finite differences
142(3)
3.4 Forward finite differences
145(4)
3.5 Central finite differences
149(5)
3.6 Interpolating polynomials
154(3)
3.7 Interpolation of equally spaced points
157(7)
3.7.1 Gregory-Newton interpolation
157(5)
3.7.2 Stirling's interpolation
162(2)
3.8 Interpolation of unequally spaced points
164(7)
3.8.1 Lagrange polynomials
164(3)
3.8.2 Spline interpolation
167(4)
3.9 Using built-in MATLAB® functions
171(2)
3.10 Summary
173(6)
Problems
173(5)
References
178(1)
Chapter 4 Differentiation And Integration
Chapter Outline
Motivation
179(1)
4.1 Introduction
180(2)
4.2 Differentiation by backward finite differences
182(3)
4.2.1 First derivative
182(2)
4.2.2 Second derivative
184(1)
4.3 Differentiation by forward finite differences
185(3)
4.3.1 First derivative
185(1)
4.3.2 Second derivative
186(2)
4.4 Differentiation by central finite differences
188(8)
4.4.1 First derivative
188(1)
4.4.2 Second derivative
189(7)
4.5 Spline differentiation
196(1)
4.6 Integration formulas
196(1)
4.7 Newton-Cotes formulas of integration
197(12)
4.7.1 The trapezoidal rule
198(3)
4.7.2 Simpson's 1/3 rule
201(1)
4.7.3 Simpson's 3/8 rule
202(3)
4.7.4 Summary of Newton-Cotes integration
205(4)
4.8 Gauss quadrature
209(6)
4.8.1 Two-point Gauss-Legendre quadrature
209(2)
4.8.2 Higher-point Gauss-Legendre formulas
211(4)
4.9 Spline integration
215(1)
4.10 Multiple integrals
216(2)
4.11 Using built-in MATLAB® functions
218(1)
4.12 Summary
219(6)
Problems
219(5)
References
224(1)
Chapter 5 Ordinary Differential Equations: Initial Value Problems
Chapter Outline
Motivation
225(1)
5.1 Introduction
226(3)
5.2 Classifications of ordinary differential equations
229(1)
5.3 Transformation to canonical form
230(5)
5.4 Linear ordinary differential equations
235(6)
5.5 Nonlinear ordinary differential equations
241(22)
5.5.1 The Euler and modified Euler methods
242(6)
5.5.2 The Runge-Kutta methods
248(8)
5.5.3 The Adams and Adams-Moulton methods
256(2)
5.5.4 Simultaneous Differential Equations
258(5)
5.6 Using built-in MATLAB® functions
263(1)
5.7 Difference equations and their solutions
263(4)
5.8 Propagation, stability, and convergence
267(9)
5.8.1 Stability and Error Propagation of Euler Methods
269(5)
5.8.2 Stability and error propagation of Runge-Kutta methods
274(1)
5.8.3 Stability and error propagation of multistep methods
275(1)
5.9 Step size control
276(1)
5.10 Stiff differential equations
277(2)
5.11 Summary
279(14)
Problems
279(12)
References
291(2)
Chapter 6 Ordinary Differential Equations: Boundary Value Problems
Chapter Outline
Motivation
293(1)
6.1 Introduction
293(2)
6.2 The shooting method
295(10)
6.3 The finite-difference method
305(6)
6.4 Collocation methods
311(10)
6.5 Using built-in MATLAB® functions
321(1)
6.6 Summary
322(5)
Problems
322(3)
References
325(2)
Chapter 7 Partial Differential Equations
Chapter Outline
Motivation
327(1)
7.1 Introduction
328(1)
7.2 Classification of partial differential equations
329(1)
7.3 Initial and boundary conditions
330(2)
7.4 Solution of partial differential equations using finite differences
332(55)
7.4.1 Elliptic partial differential equations
335(17)
7.4.2 Parabolic partial differential equations
352(25)
7.4.3 Hyperbolic partial differential equations
377(5)
7.4.4 Irregular boundaries and polar coordinate systems
382(5)
7.5 Using built-in MATLAB® functions
387(1)
7.6 Stability analysis
387(3)
7.7 Summary
390(13)
Problems
391(9)
References
400(3)
Chapter 8 Linear And Nonlinear Regression Analysis
Chapter Outline
Motivation
403(1)
8.1 Process analysis, mathematical modeling, and regression analysis
404(3)
8.2 Review of statistical terminology used in regression analysis
407(20)
8.2.1 Population and sample statistics
407(8)
8.2.2 Probability density functions and probability distributions
415(8)
8.2.3 Confidence intervals and hypothesis testing
423(4)
8.3 Linear regression analysis
427(10)
8.3.1 The least squares method
429(1)
8.3.2 Properties of the estimated vector of parameters
430(7)
8.4 Nonlinear regression analysis
437(10)
8.4.1 The method of steepest descent
439(1)
8.4.2 The Gauss-Newton method
440(3)
8.4.3 Newton's method
443(1)
8.4.4 The Marquardt method
444(1)
8.4.5 Multiple nonlinear regression
445(2)
8.5 Analysis of variance and other statistical tests of the regression results
447(18)
8.6 Using built-in MATLAB® and Excel functions
465(1)
8.7 Summary
466(9)
Problems
468(7)
References 475
Navid Mostoufi is Professor of Chemical Engineering at the University of Tehran. He has taught advanced mathematics and fluid mechanics courses for over 16 years. His research interests include process modeling, simulation and optimization, and fluidization. He holds a Ph.D. in Fluidization from Canadas Ecole Polytechnique de Montréal. Professor Mostoufi has more than 270 publications in major international journals and conferences and is co-author of the textbook Numerical Methods for Chemical Engineers with MATLAB Applications”, published by Pearson/Prentice Hall in 1999 and Coupled CFD-DEM Modeling: Formulation, Implementation and Application to Multiphase Flows”, published by Wiley in 2016. He is the Founder and Editor-in-Chief of Chemical Product and Process Modeling (http://www.degruyter.com/view/j/cppm) published by Walter de Gruyter GmbH, Germany and winner of University of Tehrans International Award, 2015. He is also the University of Tehrans distinguished researcher, 2013. Prof. Mostoufi is now the secretary of the Iranian Association of Chemical Engineers. Alkis Constantinides was Emeritus Professor of Chemical and Biochemical Engineering at Rutgers University, with nearly forty years of academic and industrial experience. He was the author of the textbook Applied Numerical Methods with Personal Computers and the co-author of the textbook Numerical Methods for Chemical Engineers with MATLAB Applications.