Numerical Methods for Chemical Engineering: Solving IVPs and Parameter Fitting

Translating raw experimental data into predictive mathematical models is a core skill for modern chemical and biological engineers. To design efficient batch reactors and understand enzyme kinetics, you must know how to solve differential equations and fit parameters to real-world data. This text-based course guides you through the foundational principles of numerical methods, showing you how to set up and solve initial value problems (IVPs) and perform parameter estimation for chemical systems. What you'll learn: - Understand the fundamental mathematical concepts behind initial value problems in chemical engineering. - Implement numerical solvers to simulate batch reactor dynamics and enzyme-catalyzed reactions. - Fit experimental data to kinetic models using parameter estimation techniques. - Apply error-handling and code organization practices to ensure numerical stability and solver convergence. - Analyze model sensitivity to understand how parameter variations affect system behavior. You will start with the basic terminology of ordinary differential equations and chemical kinetics before moving on to numerical solving and parameter fitting techniques. Through clear, step-by-step written explanations and practical code snippets, you will learn to build, run, and refine your own simulation scripts. This course is designed for chemical engineering students, biotechnologists, and researchers who are new to numerical modeling. No advanced programming background is required, as we begin with foundational concepts. Start mastering the numerical tools essential for chemical engineering simulation today.