MATLAB Optimization and Cost Functions for Chemical Engineering

In chemical engineering, finding the most cost-effective, efficient, or high-yielding process design requires translating physical constraints into mathematical models. Designing and implementing effective cost functions in MATLAB is the foundation of solving these complex optimization problems. This text-based course guides you through the process of formulating, writing, and executing optimization routines. You will transition from understanding basic mathematical optimization concepts to writing custom cost functions and running solvers to find optimal operating conditions. What you'll learn: - Understand the core principles of numerical optimization and cost function formulation. - Write custom cost functions in MATLAB using standard script and function syntax. - Configure and run MATLAB solvers for both constrained and unconstrained optimization problems. - Apply modern practices like vectorization and parameter tuning to improve solver performance and reliability. - Analyze optimization results to ensure physical feasibility in chemical processes. You will start with foundational optimization concepts and MATLAB syntax, then progress to writing your first simple cost function. From there, you will explore multi-variable problems, constraint handling, and debugging common solver errors through structured written walkthroughs. This course is designed for beginner chemical engineering students, process engineers, and MATLAB learners who want to build a solid foundation in numerical optimization. No prior optimization experience is required. Begin reading today to master the fundamentals of numerical optimization in MATLAB.