Numerical Optimization in Chemical Engineering

Chemical engineers constantly face the challenge of maximizing yield, minimizing costs, and optimizing reactor performance. Navigating these complex thermodynamic and kinetic variables requires a structured, mathematical approach to decision-making. This text-based course guides you from the fundamental principles of optimization to solving realistic chemical engineering design and operating problems. You will learn how to translate physical constraints into robust mathematical formulations and solve them using modern numerical algorithms. By the end of this course, you will be able to set up and solve constrained and unconstrained optimization problems, perform sensitivity analyses, and apply these techniques to reactor design, heat exchanger networks, and separation processes. What you will learn: Understand the core mathematical principles of single-variable and multi-variable optimization; Formulate objective functions and constraints for chemical process systems; Apply numerical methods like gradient descent, Newton's method, and linear programming; Solve constrained optimization problems using Lagrange multipliers and modern penalty methods; Perform sensitivity analysis to understand how parameter variations affect your optimal design; Implement optimization algorithms using modern Python libraries and scientific computing packages. This course begins with foundational mathematical concepts and definitions before moving step-by-step through optimization algorithms, practical chemical engineering case studies, and code-based implementation exercises. This course is designed for chemical engineering students, practicing engineers, and process designers who want to build a solid foundation in numerical optimization without needing prior advanced programming experience. Start optimizing your chemical processes today.