Numerical Methods for Chemical Engineering: Models and Parameter Estimation

When designing and optimizing chemical processes, engineers must bridge the gap between theoretical models and real-world experimental data. Understanding how to mathematically fit these models and assess their reliability is a critical skill for modern process engineering. This course guides you through the essential mathematical and computational tools needed to solve complex engineering equations and evaluate parameter uncertainty with confidence. You will start by learning foundational numerical concepts, including root-finding, interpolation, and numerical integration of transport equations. From there, you will explore regression techniques, modern parameter estimation workflows, and how to calculate confidence limits to determine the reliability of your model predictions. What you will learn: Understand foundational numerical methods for solving algebraic and differential chemical engineering equations; Apply linear and non-linear regression techniques to fit chemical process models to experimental data; Calculate confidence intervals and statistical uncertainty limits for estimated parameters; Implement basic data-cleaning and validation practices to ensure robust model performance; Analyze model sensitivity to understand how parameter variations affect process outcomes. This text-only course begins with core mathematical definitions and builds step-by-step toward practical estimation scenarios, using clear explanations and written code examples. This course is designed for beginner-level chemical engineers, process chemistry students, and technical professionals looking to strengthen their computational and data-fitting skills without needing advanced mathematical prerequisites. Start building more reliable and mathematically sound chemical engineering models today.