Applied Numerical Optimization for Chemical Engineers in MATLAB

In chemical engineering, designing efficient reactors and optimizing process conditions requires solving complex, non-linear mathematical models that cannot be solved by hand. Understanding how to implement robust numerical optimization algorithms is essential for finding reliable solutions to these real-world engineering challenges. This text-based course guides you through the core concepts of numerical optimization, showing you how to read, write, and execute optimization routines in MATLAB. You will transition from understanding basic mathematical definitions to confidently applying quasi-Newton methods and modification algorithms to chemical processes. What you'll learn: - Understand the foundational mathematical concepts of numerical optimization, Hessians, and gradient descent. - Implement quasi-Newton minimization routines in MATLAB using finite difference estimates. - Apply Levenberg-Marquardt modifications to guarantee descent directions during search operations. - Execute line search techniques using the secant method to locate optimal step sizes. - Analyze optimization convergence behavior and troubleshoot common numerical errors in engineering models. - Structure clean, modern MATLAB code for process optimization and parameter estimation. The course begins with essential mathematical terminology and foundational optimization concepts before moving into step-by-step algorithmic implementations. You will read clear explanations of the underlying theory and study practical MATLAB code examples designed for process engineering applications. This course is designed for engineering students, researchers, and junior professionals new to numerical optimization. No prior advanced programming experience is required, though a basic familiarity with algebra and MATLAB syntax is helpful. Start building reliable optimization routines for your engineering projects today.