This book presents Maple solutions to a wide range of problems relevant to chemical engineers and others. Many of these solutions use Maple’s symbolic capability to help bridge the gap between analytical and numerical solutions. The readers are strongly encouraged to refer to the references included in the book for a better understanding of the physics involved, and for the mathematical analysis. This book was written for a senior undergraduate or a first year graduate student course in chemical engineering. Most of the examples in this book were done in Maple 10. However, the codes should run in the most recent version of Maple. We strongly encourage the readers to use the classic worksheet (*. mws) option in Maple as we believe it is more user-friendly and robust. In chapter one you will find an introduction to Maple which includes simple basics as a convenience for the reader such as plotting, solving linear and nonlinear equations, Laplace transformations, matrix operations, ‘do loop,’ and ‘while loop. ’ Chapter two presents linear ordinary differential equations in section 1 to include homogeneous and nonhomogeneous ODEs, solving systems of ODEs using the matrix exponential and Laplace transform method. In section two of chapter two, nonlinear ordinary differential equations are presented and include simultaneous series reactions, solving nonlinear ODEs with Maple’s ‘dsolve’ command, stop conditions, differential algebraic equations, and steady state solutions. Chapter three addresses boundary value problems.

Computational Techniques for Chemical Engineers offers a practical guide to the chemical engineer faced with a problem of computing. The computer is a servant not a master, its value depends on the instructions it is given. This book aims to help the chemical engineer in the right choice of these instructions. The text begins by outlining the principles of operation of digital and analogue computers and then discussing the difficulties which arise in formulating a problem for solution on such a machine. This is followed by separate chapters on digital computers and their programming; the use of digital computers in chemical engineering design work; optimization techniques and their application in the selection of optimum designs; the solution of sets of non-linear algebraic equations via hill-climbing; and determination of equilibrium compositions by minimization of Gibbs free energy. Subsequent chapters discuss the solution of partial or simultaneous differential equations; parameter estimation in differential equations; continuous systems; and analogue computers.

Authors Owen Hanna and Orville Sandall include broad use of convergence acceleration techniques such as Pade approximation for series; Shanks transformation for series; linear and nonlinear systems of algebraic equations; systematic use of global Richardson extrapolation for integrals and ODE systems to monitor the overall error; and discussion of methods for the solution of stiff ODE.

In the recent decades, the emerging new molecular measurement techniques and their subsequent availability in chemical database has allowed easier retrieval of the associated data by the chemical analyst. Before the data revolution, most books focused either on mathematical modeling of chemical processes or exploratory chemometrics. Computational and Statistical Methods for Chemical Engineering aims to combine these two approaches and provide aspiring chemical engineers a single, comprehensive account of computational and statistical methods. The book consists of four parts: Part I discusses the necessary calculus, linear algebra, and probability background that the student may or may not have encountered before. Part II provides an overview on standard computational methods and approximation techniques useful for chemical engineering systems. Part III covers the most important statistical models, starting from simple measurement models, via linear models all the way to multivariate, non-linear stochiometric models. Part IV focuses on the importance of designed experiments and robust analyses. Each chapter is accompanied by an extensive selection of theoretical and practical exercises. The book can be used in combination with any modern computational environment, such as R, Python and MATLAB. Given its easy and free availability, the book includes a bonus chapter giving a simple introduction to R programming. This book is particularly suited for undergraduate students in Chemical Engineering who require a semester course in computational and statistical methods. The background chapters on calculus, linear algebra and probability make the book entirely self-contained. The book takes its examples from the field of chemistry and chemical engineering. In this way, it motivates the student to engage actively with the material and to master the techniques that have become crucial for the modern chemical engineer.

Applications of numerical mathematics and scientific computing to chemical engineering.