2nd Edition

A Numerical Primer for the Chemical Engineer, Second Edition

By Edwin Zondervan Copyright 2020
    208 Pages 64 B/W Illustrations
    by CRC Press

    208 Pages 64 B/W Illustrations
    by CRC Press

    Designed as an introduction to numerical methods for students, this book combines mathematical correctness with numerical performance, and concentrates on numerical methods and problem solving. It applies actual numerical solution strategies to formulated process models to help identify and solve chemical engineering problems. Second edition comes with additional chapter on numerical integration and section on boundary value problems in the relevant chapter. Additional material on general modelling principles, mass/energy balances and separate section on DAE’s is also included. Case study section has been extended with additional examples.

    1 The role of models in chemical engineering
    1.1 Introduction
    1.2 The idea of a model
    1.4 Model analysis
    1.5 Model solution strategies
    1.6 Summary
    1.7 Exercises

    2 Errors in computer simulations
    2.1 Introduction
    2.2 Significant digits
    2.3 Round-off and truncation errors
    2.4 Break errors
    2.5 Loss of digits
    2.6 Ill-conditioned problems
    2.7 (Un-)stable methods
    2.8 Summary
    2.9 Exercises

    3 Linear equations
    3.1 Introduction
    3.2 MATLAB
    3.3 Linear systems
    3.4 The inverse of a matrix
    3.5 The determinant of a matrix
    3.6 Useful properties
    3.7 Matrix ranking
    3.8 Eigenvalues and eigenvectors
    3.9 Spectral decomposition
    3.10 Summary
    3.11 Exercises

    4 Elimination methods
    4.1 Introduction
    4.2 MATLAB
    4.3 Gaussian elimination
    4.4 LU factorization
    4.5 Summary
    4.6 Exercises

    5 Iterative methods
    5.1 Introduction
    5.2 Laplace’s equation
    5.3 LU factorization
    5.5 The Jacobi method
    5.6 Example for the Jacobi method
    5.7 Summary
    5.8 Exercises

    6 Nonlinear equations
    6.1 Introduction
    6.2 Newton method 1D
    6.3 Newton method 2D
    6.4 Reduced Newton step method
    6.5 Quasi-Newton method
    6.6 Summary
    6.7 Exercises

    7 Ordinary differential equations
    7.1 Introduction
    7.2 Euler’s method
    7.3 Accuracy and stability of Euler’s method
    7.4 The implicit Euler method
    7.5 Stability of the implicit Euler method
    7.6 Systems of ODEs
    7.7 Stability of ODE systems
    7.8 Stiffness of ODE systems
    7.9 Higher-order methods
    7.10 Summary
    7.11 Exercises

    8 Numerical integration
    8.1 Introduction
    8.2 Euler’s method
    8.3 The trapezoid method
    8.4 Simpson’s method
    8.5 Estimation of errors using numerical integration
    8.6 The Richardson correction
    8.7 Summary
    8.8 Exercises

    9 Partial differential equations
    9.1 Introduction
    9.2 Transport PDEs
    9.3 Finite volumes
    9.4 Discretizing the control volumes
    9.5 Transfer of heat to fluid in a pipe
    9.6 Simulation of the heat PDE
    9.7 Summary
    9.8 Exercises

    10 Data regression and curve fitting
    10.1 Introduction
    10.2 The least squares method
    10.3 Residual analysis
    10.4 ANOVA analysis
    10.5 Confidence limits
    10.6 Summary
    10.7 Exercises

    11 Optimization
    11.1 Introduction
    11.2 Linear programming
    11.3 Nonlinear programming
    11.4 Integer programming
    11.5 Summary
    11.6 Exercises

    12 Basics of MATLAB
    12.1 Introduction
    12.2 The MATLAB user interface
    12.3 The array structure
    12.4 Basic calculations
    12.5 Plotting
    12.6 Reading and writing data
    12.7 Functions and m-files
    12.8 Repetitive operations

    13 Numerical methods in Excel
    13.1 Introduction
    13.2 Basic functions in Excel
    13.3 The Excel solver
    13.4 Solving nonlinear equations in Excel
    13.5 Differentiation in Excel
    13.6 Curve fitting in Excel

    14 Case studies
    14.1 Introduction
    14.2 Modeling a separation system
    14.3 Modeling a chemical reactor system
    14.4 PVT behavior of pure substances
    14.5 Dynamic modeling of a distillation column
    14.6 Dynamic modeling of an extraction cascade (ODEs)
    14.7 Distributed parameter models for a tubular reactor
    14.8 Modeling of an extraction column
    14.9 Fitting of kinetic data
    14.10 Fitting of NRTL model parameters
    14.11 Optimizing a crude oil refinery
    14.12 Planning in a manufacturing line
    Bibliography
    Index

    Biography

    Edwin Zondervan was born and raised in Leeuwarden, the Netherlands. After finishing his bachelor with a specialization in process automation in Leeuwarden he continued in Groningen with a M.Sc. in chemical engineering. Then he moved To Enschede and pursued a Ph.D. on modeling, optimization and control of dead-end membrane filtration of surface water. He defended his doctorate at Groningen in 2007. He worked from 2007 to 2015 at Eindhoven University of Technology. He has been working as associate researcher at the laboratories of Technical University of Catalonia, Carnegie Mellon University, Denmark Technical University and Imperial College . Besides research Edwin Zondervan has been very active in the educational gremials where he trained many generations of students in process design, process control and numerical methods. For the latter one Edwin published a textbook that was released in 2014: “A numerical primer for the chemical engineer”. Recently Edwin Zondervan joined the Institute for environmental science and Technology of Bremen University, where he obtained a professorship in “Process Systems Engineering”. The newly established Laboratory of Process Systems Engineering (PSE) at Bremen University (which was established within Bremen’s Excellence Initiative) will conduct research in the field of sustainable and flexible system design of energy networks. The main objective of the laboratory of PSE is to develop network modeling techniques and dynamic optimization tools and to apply them to the design and operation of complex energy/process systems. The PSE group distinguishes two working areas: i) Novel energy technologies and devices and ii) Energy Efficient production. Where the challenges lie in 1) decisionmaking under uncertainty, 2) sustainable design and 3) managing complexity. In addition the PSE group will be active in the development of an Energy Systems Institute at Bremen University and setup a specialized course program in this field.