Numerical Methods for Chemical Engineering with Python

Chemical engineering problems often involve complex systems of algebraic equations, differential equations, and data-fitting challenges that cannot be solved by hand. This comprehensive written text teaches you how to bridge the gap between chemical engineering theory and numerical computation. You will learn to translate physical transport, thermodynamic, and reaction kinetics problems into robust computational models. By starting with foundational mathematical definitions and progressing to modern implementation practices, you will gain the confidence to analyze and solve real-world engineering scenarios. What you'll learn: Understand the core mathematical theory behind root-finding, numerical integration, and differential equations; Apply modern Python libraries and type-hinted code to solve systems of non-linear equations; Formulate and solve ordinary differential equations describing chemical reactors and transport phenomena; Implement linear and non-linear regression techniques to fit experimental kinetic data; Structure clean, readable, and testable engineering scripts using virtual environments and pytest. The course begins with essential mathematical concepts and numerical definitions before guiding you through structured, step-by-step written tutorials that apply these methods to realistic chemical engineering systems. This course is designed specifically for undergraduate chemical engineering students, practicing engineers, and science professionals who want to build a solid foundation in computational methods without needing prior programming experience. Start translating your chemical engineering challenges into reliable numerical solutions today.