Practical Numerical Methods for Chemical Engineers: Using Excel with VBA, 2nd Edition

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9781482070125: Practical Numerical Methods for Chemical Engineers: Using Excel with VBA, 2nd Edition
Vom Verlag:

This revised and expanded 2nd edition introduces even more computational techniques with over 100 VBA macros for extending Excel's power. Engineers and scientists will find the coverage of computational tools applicable to a variety of problems in their own disciplines. ** The selection of software reflects Excel's current status as the de facto computational tool used by practicing engineers. Engineers and scientists should become proficient at extending Excel’s capabilities with Visual Basic for Applications (VBA) programs to boost their Excel worksheets with time saving enhancements and powerful numerical techniques. ** Topics include an introduction to modeling, documentation, basic Excel and VBA programming, root-finding for linear and nonlinear systems of equations, multivariate optimization, experimental uncertainty propagation and analysis, linear and non-linear least-squares regression and model validation, interpolation, integration, and ordinary and partial differential equations. ** A companion web site has links to digital files for downloading over 170 illustrations and examples and the PNM2Suite Excel workbook with VBA user-defined functions, macros, and user forms for advanced numerical techniques. Practice problems are also available from the web site (http://www.d.umn.edu/~rdavis/PNM/PNMExcelVBA2/). Example files and macros may be modified by the user to solve new problems. ** Chapter 1 includes a brief introduction to chemical reaction engineering that provides some background needed for problems involving mass & energy balances with reactions. ** The next two chapters introduce frequently overlooked features of Excel and VBA for engineering programming to apply numerical methods in Excel, as well as document results (versions Excel 2007, 2010 & higher). The remaining chapters present powerful numerical techniques using Excel & VBA, including: ** General Methods: Pseudo-random number generation, Sorting, Formula graphing & evaluation, Random Sampling, User forms ** Linear Equations: Gaussian Elimination, Crout Reduction, Thomas for tri-diagonal matrices, Cholesky for symmetric matrices, worksheet Matrix functions, Jacobi & Gauss-Seidel Iteration, Wegstein & Steffenson's Aitkin Delta Square methods ** Nonlinear Equations: Ordinary Fixed-Point Iteration (Successive Substitution), Bisection, Secant, Regula Falsi, Newton & Quasi-Newton, Continuation (homotopy), Goal Seek, Solver, Bairstow's method for polynomials ** Derivative Approximation: Finite Difference, Richardson's extrapolation, Jacobian, Sensitivity Analysis, Lagrange polynomials, piecewise polynomial splines ** Uncertainty Analysis: Jitter method for the Law of Propagation of Uncertainty, Monte Carlo with Latin-Hypercube sampling, Jackknife for regression parameter uncertainty ** Optimization: Graphical, Quadratic with acceleration, Powell, Golden Section, Luus-Jaakola, Solver (for linear and nonlinear programming), Parameter Scaling ** Least-squares Regression: multivariate linear models, Gauss-Newton and Levenberg-Marquardt for nonlinear models, Solver, Optimization methods, Rational Least Squares ** Interpolation: Linear, Newton Divided Difference, La Grange, Rational, Stineman, Cubic Spline, Constrained Splines, Data Smoothing ** Integration: Trapezoid, Improper, Midpoint, Romberg, Simpson, Adaptive Gauss-Kronrod, Splines, multiple integrals with Simpson, Kronrod, & Monte Carlo methods ** Initial-Value ODEs: Taylor Series, improved Euler, implicit Trapezoid for stiff problems, Modified Euler, fixed & variable step Four/Fifth-order Runge-Kutta, Cash-Karp & Dormand-Prince, Adams-Bashforth-Moulton multi-step methods ** Boundary Value ODEs and PDEs: Shooting, Finite Difference, Collocation on Finite Elements, Method of Lines, semi-implicit Crank-Nicholson methods ** Tables for quick reference of Excel, VBA, and custom functions & macros for numerical methods. ** Tables of unit conversions and ideal gas constants.

Vom Verlag:

This is the 2nd edition. A newer 3rd edition is now available with updated and expanded coverage of Practical Numerical Methods Using Excel with VBA. For more information about the latest edition, visit the companion web site (www.d.umn.edu/~rdavis/PNM). ** Engineers and scientists will find the coverage of computational tools applicable to a wider variety of problems in their own disciplines. ** The selection of software reflects Excel's status as the de facto computational tool used by practicing engineers. Engineers & scientists should become proficient at extending Excel’s capabilities with VBA programming to boost their worksheets with time saving enhancements and powerful numerical techniques. ** Topics include an introduction to modeling, Excel & VBA programming, root-finding for systems of equations, optimization, experimental uncertainty propagation and analysis, least-squares regression & model validation, interpolation, integration, & ordinary and partial differential equations. ** A companion web site has links to digital files for downloading 200 illustrations, examples & the PNM2Suite workbook with 100 VBA user-defined functions, macros, & user forms for advanced numerical techniques. Practice problems are also available from the web site (www.d.umn.edu/~rdavis/PNM/PNMExcelVBA2). Example files & macros are ready to be modified by users for their own needs. ** Chapter 1 includes a brief introduction to chemical reaction engineering that provides some background needed for problems involving mass & energy balances with reactions. ** The next two chapters introduce frequently overlooked features of Excel and VBA for engineering programming to apply numerical methods in Excel, as well as document results. The remaining chapters present powerful numerical techniques using Excel & VBA, including: ** General Methods: Sub & User-defined Function Procedures, User Forms, Pseudo-random Number Generation, Sorting, Formula Graphing & Evaluation, Random Sampling ** Linear Equations: Gaussian Elimination, Crout Reduction, Thomas algorithm for tri-diagonal & Cholesky's method for symmetric matrices, Matrix functions, Jacobi & Gauss-Seidel Iteration, Wegstein & Steffenson's version of Aitkin's Delta Square methods, ** Nonlinear Equations: Ordinary Fixed-Point Iteration, Bisection, Secant, Regula Falsi, Newton & Quasi-Newton, Continuation (Homotopy), Goal Seek, Solver, Bairstow's method for polynomial roots ** Derivative Approximation: Finite Difference, Richardson's extrapolation, Sensitivity Analysis, Lagrange polynomials, splines ** Uncertainty Analysis: Jitter method for the Law of Propagation of Uncertainty, Monte Carlo with Latin-Hypercube sampling, Jack knife for regression parameter uncertainty ** Optimization: Graphical, Quadratic with acceleration, Powell, Golden Section, Luus-Jaakola, Solver (for linear and nonlinear programming), Parameter Scaling ** Least-squares Regression: multivariable linear and nonlinear models, Gauss-Newton, Levenberg-Marquardt, and Monte Carlo methods with parameter uncertainty, Weighting ** Interpolation: Linear, Newton Divided Difference, Lagrange, Rational, Cubic Spline, Constrained Splines, Data Smoothing ** Integration: Trapezoid, Midpoint, Simpson's, Romberg, Adaptive Gauss-Kronrod, Splines, multiple integrals with Kronrod, & Monte Carlo methods ** Initial-Value ODEs: Taylor Series, improved & modified Euler, implicit Trapezoidal for stiff problems, fixed & variable single step 4-5 order Runge-Kutta, Cash-Karp & Dormand-Prince, Adams-Bashforth-Moulton multi-step methods ** Boundary Value ODEs and PDEs: Shooting, Finite Difference, Collocation on Finite Elements, Method of Lines, semi-implicit Crank-Nicholson methods

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