Numerical Methods for Chemical Engineering: Applications in MATLAB

Suitable for a first year graduate course, this textbook unites the applications of numerical mathematics and scientific computing to the practice of chemical engineering. Written in a pedagogic style, the book describes basic linear and nonlinear algebric systems all the way through to stochastic methods, Bayesian statistics and parameter estimation. These subjects are developed at a level of mathematics suitable for graduate engineering study without the exhaustive level of the theoretical mathematical detail. The implementation of numerical methods in MATLAB is integrated within each chapter and numerous examples in chemical engineering are provided, with a library of corresponding MATLAB programs. This book will provide the graduate student with essential tools required by industry and research alike. Supplementary material includes solutions to homework problems set in the text, MATLAB programs and tutorial, lecture slides, and complicated derivations for the more advanced reader. These are available online at www.cambridge.org/9780521859714.

[1]  V. Arnold Mathematical Methods of Classical Mechanics , 1974 .

[2]  G. Strang Introduction to Linear Algebra , 1993 .

[3]  D. F. Evans,et al.  Fundamentals of Interfacial Engineering , 1996 .

[4]  Eduardo D. Sontag,et al.  Mathematical Control Theory: Deterministic Finite Dimensional Systems , 1990 .

[5]  Uri M. Ascher,et al.  Computer methods for ordinary differential equations and differential-algebraic equations , 1998 .

[6]  P. Flory Principles of polymer chemistry , 1953 .

[7]  T. Lubensky,et al.  Principles of condensed matter physics , 1995 .

[8]  Neil A. Dotson,et al.  Polymerization Process Modeling , 1995 .

[9]  H. S. Fogler,et al.  Elements of Chemical Reaction Engineering , 1986 .

[10]  J. E. Akin,et al.  Finite Elements for Analysis and Design , 1994 .

[11]  D. Chandler,et al.  Introduction To Modern Statistical Mechanics , 1987 .

[12]  W. M. Bolstad Introduction to Bayesian Statistics , 2004 .

[13]  A. Leach Molecular Modelling: Principles and Applications , 1996 .

[14]  G. Odian,et al.  Principles of polymerization , 1981 .

[15]  Hans Christian Öttinger,et al.  Stochastic Processes in Polymeric Fluids , 1996 .

[16]  Edward L Cussler,et al.  Diffusion: Mass Transfer in Fluid Systems , 1984 .

[17]  B. M. Hill,et al.  Theory of Probability , 1990 .

[18]  Elaine S. Oran,et al.  Numerical Simulation of Reactive Flow , 1987 .

[19]  Joel H. Ferziger,et al.  Computational methods for fluid dynamics , 1996 .

[20]  J. Villadsen,et al.  Solution of differential equation models by polynomial approximation , 1978 .

[21]  Bruce A. Finlayson,et al.  Numerical methods for problems with moving fronts , 1992 .

[22]  Gene H. Golub,et al.  Matrix computations , 1983 .

[23]  P. Kloeden,et al.  Numerical Solution of Stochastic Differential Equations , 1992 .

[24]  Berend Smit,et al.  Understanding Molecular Simulation , 2001 .

[25]  J. Stoer,et al.  Introduction to Numerical Analysis , 2002 .

[26]  P. Atkins,et al.  Molecular Quantum Mechanics , 1970 .

[27]  Christian P. Robert,et al.  The Bayesian choice , 1994 .

[28]  Arch W. Naylor,et al.  Linear Operator Theory in Engineering and Science , 1971 .

[29]  R. Bellman Dynamic programming. , 1957, Science.

[30]  H. Jeffreys,et al.  Theory of probability , 1896 .

[31]  Joseph O'Rourke,et al.  Computational Geometry in C. , 1995 .

[32]  W. Deen Analysis Of Transport Phenomena , 1998 .

[33]  Yiannis Aloimonos,et al.  Artificial intelligence - theory and practice , 1995 .

[34]  I. Stakgold Green's Functions and Boundary Value Problems , 1979 .

[35]  William H. Press,et al.  Numerical recipes in C , 2002 .

[36]  Stephen J. Wright,et al.  Numerical Optimization , 2018, Fundamental Statistical Inference.

[37]  Jeff Gill,et al.  What are Bayesian Methods , 2008 .

[38]  Joseph G. Ibrahim,et al.  Monte Carlo Methods in Bayesian Computation , 2000 .