Numerical Methods for Chemical Engineering with Python

Chemical engineering challenges often lead to complex systems of equations that cannot be solved by hand. To design reactors, model transport phenomena, and optimize processes, you need robust numerical techniques. This course guides you through the foundational mathematical concepts and shows you how to implement them computationally. You will start by mastering the essential terminology of linear algebra and numerical stability. You will learn the mechanics of Gaussian elimination, discover why standard elimination can fail due to round-off errors, and master pivoting techniques to ensure numerical accuracy. To keep your skills current, you will also learn how to implement these algorithms using modern Python libraries and structured arrays. What you'll learn: - Understand the core mathematical principles of Gaussian elimination and matrix operations - Apply partial pivoting techniques to prevent division-by-zero and minimize numerical round-off errors - Analyze chemical engineering systems, such as mass balances, using linear systems of equations - Implement numerical solvers using modern Python standards, including type hints and structured arrays - Evaluate the stability and convergence of numerical algorithms under different process conditions This course begins with foundational mathematical definitions before moving into hands-on algorithmic implementation and practical engineering scenarios. It is designed for undergraduate engineering students and professional chemical engineers who are new to numerical computation and want to build a solid, practical foundation. No prior advanced programming experience is required.