Numerical Methods in Chemical Engineering: Balancing Models and Data

Chemical engineering relies on mathematical models to predict reactor behavior, transport phenomena, and thermodynamic properties, but these models are only as good as the data that validates them. This text-based course bridges the gap between theoretical chemical engineering equations and real-world experimental data using numerical methods. You will learn how to formulate, solve, and analyze engineering models by applying robust computational techniques to practical problems. Through clear written explanations, structured derivations, and step-by-step code examples, you will build a solid foundation in engineering computation. You will learn how to fit complex models to experimental data, analyze parameter uncertainty, and solve the algebraic and differential equations that govern chemical systems. What you'll learn: - Understand the foundational theory of numerical approximation and error analysis in chemical processes - Fit mathematical models to experimental data using linear and non-linear regression techniques - Solve systems of algebraic equations representing steady-state chemical processes - Apply numerical integration and differentiation to model transient chemical reactors and transport phenomena - Analyze parameter sensitivity and uncertainty to evaluate the reliability of your engineering models - Implement modern scientific computing practices, including structured code design and vectorization, to solve engineering problems The course begins with an introduction to key modeling concepts and error analysis, then moves systematically through data fitting, optimization, and solving differential equations. Each section combines theoretical principles with written step-by-step computational exercises. This course is designed for beginner to intermediate learners in chemical, environmental, or process engineering who want to master numerical computation. No advanced programming or numerical analysis experience is required. Start translating your chemical engineering data into predictive mathematical models today.