Numerical Methods for Chemical Engineering: A Practical Guide

Chemical engineering problems often involve complex differential equations and non-linear systems that cannot be solved by hand. Understanding how to model and solve these systems numerically is a vital skill for modern process and design engineers. In this text-only course, you will transition from manual calculations to constructing robust numerical solutions. You will learn to formulate chemical engineering problems mathematically and solve them using modern computational techniques and standard Python libraries. What you'll learn: - Understand the fundamental principles of numerical approximation and error analysis in engineering models. - Solve non-linear algebraic equations representing chemical equilibrium and equation-of-state calculations. - Apply numerical integration techniques to determine reactor volumes and mass transfer rates. - Solve ordinary differential equations describing transient heat transfer and chemical kinetics. - Implement modern Python libraries like NumPy and SciPy to automate complex engineering computations. - Analyze boundary value problems and partial differential equations for transport phenomena. The course begins with core definitions, mathematical formulation principles, and error analysis before guiding you through step-by-step written explanations and code implementations for algebraic, differential, and transport equations. This course is designed for chemistry students, process engineers, and beginners looking to build a strong foundation in numerical modeling without needing advanced prior programming experience. Start reading today to master the computational tools that drive modern chemical engineering.