About this course
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.
Course Overview
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. Each lecture contains a set of examples and Python source code to show you have we can use computer to perform basic engineering calculations. To facilitate your learning, we’ve organized the course materials into the following sections:
Main Lectures
1. Lecture 1: Errors in Numerical Methods ([Jupyter Notebook], PDF).
2. Lecture 2: Solving Non-linear Equations – Part 1 ([Jupyter Notebook], PDF).
3. Lecture 3: Solving Non-linear Equations – Part 2 ([Jupyter Notebook], PDF).
4. Lecture 4: Solving a System of Linear Equations – Part 1 ([Jupyter Notebook], PDF).
5. Lecture 5: Solving a System of Linear Equations – Part 2 ([Jupyter Notebook], PDF).
6. Lecture 6: Curve Fitting ([Jupyter Notebook], PDF).
7. Lecture 7: Differentiation and Integration ([Jupyter Notebook], PDF).
8. Lecture 8: Solving Initial Value Problems (ODEs) ([Jupyter Notebook], PDF).
9. Lecture 9: Solving Boundary Value Problems ([Jupyter Notebook], PDF).
Supplementary Lectures
– 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]).
Codes
Source codes of all the numerical methods and other extra codes are listed here. You have access to all these source codes on the GitHub repository.