Advanced Process Control (APC): a Hands-On Introduction to Optimal Control Methods

In today's industrial landscape, traditional control methods often fall short in addressing the complexities and dynamic nature of modern production processes. Optimal control techniques offer a powerful solution by providing a systematic approach to designing advanced process control (APC) strategies that maximize production performance across multiple objectives while adhering to technical and regulatory constraints.

In this workshop, you will learn the fundamentals of optimal process control through hands-on exercises in MATLAB and Simulink, with a specific focus on model predictive control (MPC) and reinforcement learning (RL). You will also hear real-world stories about MathWorks customers who successfully implemented such techniques in various process control applications such as downstream processing, plant operations, and semiconductor manufacturing.

Highlights

• Identifying an MPC prediction model from an existing plant
• Tuning MPC weights and constraints to optimize process response
• Training an RL agent to perform supervisory control tasks