Nonlinear Parameter Fitting in MATLAB for Chemical Engineers

Accurate chemical engineering models rely on precise parameter estimation from experimental data, but nonlinear physical systems can be notoriously difficult to fit. This text-based course teaches you how to systematically set up, solve, and analyze nonlinear parameter estimation problems using standard MATLAB routines. You will start with key terminology, statistical foundations, and basic optimization concepts before diving into practical implementation. What you'll learn: 1. Understand the mathematical foundations of nonlinear least squares and the Levenberg-Marquardt algorithm. 2. Implement robust parameter fitting workflows using standard MATLAB optimization routines. 3. Analyze measurement errors and compute confidence intervals under normal distribution assumptions. 4. Formulate chemical kinetics and transport models for numerical parameter estimation. 5. Write clean, structured MATLAB code to handle single and multi-response parameter fitting. 6. Evaluate and validate fit results using statistical diagnostics and residual analysis. Through clear written explanations and step-by-step code walkthroughs, you will progress from basic regression concepts to building your own robust parameter estimation scripts. This course is designed for beginners, chemical engineering students, and practicing engineers looking to strengthen their numerical computing skills. No advanced optimization experience is required. Start mastering nonlinear parameter estimation and bring precision to your chemical engineering models today.