Numerical Methods for Chemical Engineering in MATLAB

Translating experimental chemical engineering data into reliable mathematical models is a core skill for any engineer. This text-based course guides you through using MATLAB to solve numerical problems, focusing on linear regression and least squares estimation. You will transition from viewing raw laboratory data to confidently writing MATLAB scripts that fit linear models, analyze dynamic viscosity, and evaluate model accuracy. Through clear written explanations and structured code examples, you will master the foundational numerical methods used in chemical engineering processes. What you'll learn: - Learn the core mathematical concepts of the least squares method and linear regression - Write structured MATLAB scripts to import, clean, and process experimental chemical engineering data - Fit linear models to dynamic viscosity and temperature datasets using numerical techniques - Analyze model fit quality using residuals, R-squared values, and error estimation - Apply vectorized operations in MATLAB to optimize your numerical calculations - Implement robust error-handling and data-validation practices for experimental measurements The course starts with fundamental definitions of dynamic viscosity and linear modeling before moving step-by-step through MATLAB script construction, numerical execution, and statistical analysis of your results. Designed for engineering students and professional chemical engineers new to numerical computation, this course requires no advanced programming background. Start building and analyzing your own chemical engineering models today.