Numerical Methods for Chemical Engineering: A Practical Introduction

Chemical engineering problems often involve complex systems of equations, non-linear thermodynamics, and dynamic reactor behaviors that cannot be solved by hand. This course introduces you to the essential numerical methods used to model, simulate, and solve real-world chemical processes. Through clear written explanations and practical code examples, you will transition from manual calculations to automated computational problem-solving. You will learn how to define simulation parameters, set up system equations, and execute numerical routines to analyze chemical engineering systems. What you'll learn: 1. Understand foundational concepts of numerical errors, convergence, and algorithm stability. 2. Solve non-linear algebraic equations for chemical equilibrium and equation-of-state calculations. 3. Model transient systems using ordinary differential equations (ODEs) for chemical reactors. 4. Configure simulation parameters and input data structures for process modeling. 5. Apply modern Python libraries like NumPy and SciPy to automate engineering computations. 6. Practice formulating multi-component mass and energy balances as solvable matrix systems. The course begins with essential mathematical definitions and error analysis before moving step-by-step through linear systems, root-finding, and differential equations. Each concept is paired with written code walkthroughs demonstrating practical application to chemical processes. This course is designed for aspiring chemical engineers, students, and professionals looking for a foundational, text-based introduction to numerical computation. No advanced programming or advanced math background is required. Start mastering the computational tools that drive modern chemical process design today.