Kinetic Modeling and Parameter Estimation in Chemical Engineering

Translating experimental chemical data into reliable kinetic models requires a solid grasp of numerical methods and parameter estimation. This text-only course guides you through the process of modeling chemical reaction kinetics and estimating parameters with confidence. You will learn how to formulate kinetic equations, apply numerical solver techniques, and use statistical methods to fit models to experimental data, transitioning from raw data to robust predictive models. What you'll learn: - Understand the foundational principles of chemical kinetics and numerical modeling - Formulate and solve ordinary differential equations representing chemical reaction systems - Apply parameter estimation techniques to fit kinetic models to experimental datasets - Implement Bayesian statistics concepts to quantify uncertainty and evaluate parameter reliability - Analyze model residuals and perform statistical diagnostic tests to validate your results - Translate mathematical formulations into clean, structured MATLAB code for simulation The course begins with core terminology and the fundamentals of chemical reaction rates before moving into numerical differential equation solvers. You will then progress to optimization algorithms, parameter estimation workflows, and statistical validation techniques. This course is designed for beginner chemical engineering students, researchers, and professionals who want to master numerical modeling without needing advanced prior programming experience. Start building and validating your own chemical kinetic models today.