Numerical Optimization in Chemical Engineering with MATLAB

Finding optimal solutions is a core challenge in chemical engineering, whether you are sizing reactors, fitting experimental data, or determining safe operational bounds. This course introduces you to the foundational mathematics and practical implementation of numerical optimization. You will start by understanding how physical engineering constraints are translated into robust mathematical models, focusing on geometric optimization techniques such as finding the minimum-volume enclosing ellipse for multidimensional data points. Through clear, written explanations and structured code walk-throughs, you will learn to write efficient, clean MATLAB scripts that solve complex engineering problems without relying on black-box software. What you'll learn: - Understand the foundational mathematics of convex optimization and geometric bounding. - Formulate objective functions and constraint equations for chemical engineering systems. - Implement numerical optimization algorithms directly in MATLAB. - Solve minimum-volume enclosing ellipse problems to analyze process data and parameter uncertainty. - Apply modern coding best practices, including vectorized operations and clear code formatting. - Troubleshoot and debug optimization routines when solvers fail to converge. This text-based course guides you step-by-step from core mathematical definitions to complete, working MATLAB scripts. You will analyze the underlying algorithms, write code to solve optimization problems, and interpret the numerical results. This course is designed for engineering students, researchers, and practicing chemical engineers who are new to numerical optimization and want to build a solid, code-first foundation. No advanced optimization background is required, though a basic familiarity with MATLAB syntax and linear algebra will help you get the most out of the material. Start mastering numerical optimization to solve your complex engineering challenges today.