Welcome to the "Numerical Methods in Chemical Engineering" repository! This collection of resources is designed to support undergraduate chemical engineering students in their journey to grasp fundamental concepts and methods in numerical methods. The repository includes course lectures, supplementary tutorials, and MATLAB and Python code samples to demonstrate the practical implementation of these methods in engineering calculations.
Our primary goal in this course is to equip you with essential skills in numerical methods, enabling you to apply them effectively to real-world engineering problems. To facilitate your learning, we've organized the course materials into the following sections:
- Lecture 1: Errors in Numerical Methods (Jupyter Notebook, PDF).
- Lecture 2: Solving Non-linear Equations - Part 1 (Jupyter Notebook, PDF).
- Lecture 3: Solving Non-linear Equations - Part 2 (Jupyter Notebook, PDF).
- Lecture 4: Solving a System of Linear Equations - Part 1 (Jupyter Notebook, PDF).
- Lecture 5: Solving a System of Linear Equations - Part 2 (Jupyter Notebook, PDF).
- Lecture 6: Curve Fitting (Jupyter Notebook, PDF).
- Lecture 7: Differentiation and Integration (Jupyter Notebook, PDF).
- Lecture 8: Solving Initial Value Problems (ODEs) (Jupyter Notebook, PDF).
- Lecture 9: Solving Boundary Value Problems (Jupyter Notebook, PDF).
- Getting Started with Python: A general introduction to Python programming (Jupyter Notebook).
- Getting Started with NumPy: Essential features of NumPy for this course (Jupyter Notebook).
For further reading and reference, you may find the following books helpful:
- Steven C. Chapra, "Applied Numerical Methods with MATLAB for Engineers and Scientists," 3rd edition, McGraw-Hill (2012).
- Amos Gilat and Vish Subramanian, "Numerical Methods for Engineers and Scientists," 3rd edition, Wiley (2014).
We encourage you to explore these resources and engage with the course materials to build a solid foundation in numerical methods for chemical engineering. Happy learning!