Numerical Optimal Control for Chemical Engineering

Every efficient chemical process relies on precise control and optimization, yet translating complex physical systems into solvable mathematical models can feel overwhelming. This text-only course guides you step-by-step from the foundational principles of optimal control theory to implementing numerical optimization algorithms for real-world chemical systems. You will learn to bridge the gap between chemical engineering principles and numerical computation. What you'll learn: - Understand the foundational mathematics of optimal control, including state variables, control inputs, and objective functions. - Formulate dynamic optimization problems for chemical reactors, separation units, and heat exchangers. - Apply numerical discretization techniques, such as direct transcription and orthogonal collocation, to solve continuous-time problems. - Implement optimization algorithms capable of handling system constraints, safety boundaries, and operating limits. - Explore the basics of Model Predictive Control (MPC) and its modern applications in industrial process automation. - Practice analyzing and writing clean optimization scripts to simulate and solve control problems. Starting with key terminology and basic system dynamics, the course moves systematically through discretization, numerical solvers, and practical process applications. This course is designed for engineering students, process operators, and beginners to numerical optimization, requiring no advanced prerequisites beyond basic calculus. Start mastering the numerical methods that drive modern process optimization today.