Navid Mostoufi, Alkis Constantinides

Applied Numerical Methods for Chemical Engineers (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 504
• Erscheinungstermin: 22. Mai 2022
• Englisch
• ISBN-13: 9780128229347
• Artikelnr.: 64084355

Autorenporträt

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 Canada's 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 Tehran's International Award, 2015. He is also the University of Tehran's distinguished researcher, 2013. Prof. Mostoufi is now the secretary of the Iranian Association of Chemical Engineers.

Inhaltsangabe

1. Nonlinear Equations
2. Simultaneous Linear Algebraic Equations
3. Finite Difference Methods and Interpolation
4. Differentiation and Integration
5. Ordinary Differential Equations: Initial Value Problems
6. Ordinary Differential Equations: Boundary Value Problems
7. Partial Differential Equations
8. Linear and Nonlinear Regression Analysis