Numerical Methods and Data Fitting for Chemical Engineering

Analyzing experimental data and modeling chemical processes requires robust mathematical tools. This text-based course introduces you to the essential numerical methods used to solve chemical engineering problems, focusing on linear least squares and data fitting. You will transition from understanding raw experimental data to building accurate mathematical models of chemical kinetics, thermodynamics, and transport phenomena. By learning how to set up and solve regression problems, you will gain the confidence to analyze real-world chemical systems. What you'll learn: - Understand the fundamentals of linear least squares regression and parameter estimation. - Apply numerical methods to solve chemical kinetics and reactor design equations. - Fit experimental data to thermodynamic models and transport equations. - Implement modern data analysis workflows using Python, NumPy, and pandas. - Analyze residuals and evaluate the goodness of fit for chemical models. - Formulate and solve systems of linear equations representing chemical processes. Starting with foundational mathematical concepts and definitions, the course guides you step-by-step through regression analysis, error minimization, and numerical computation, all tailored to chemical engineering scenarios. This course is designed for engineering students, researchers, and junior chemical engineers who want a clear, practical introduction to numerical data fitting without needing advanced programming experience. Start reading today to master the mathematical tools behind chemical process modeling.