Multiple-input, multiple-output (MIMO) processes are a feature of almost all chemical plants. The design of robust control strategies is critical for maintaining consistent product quality, ensuring safe operations, minimizing downtime, and generating profit. The design process typically involves the comparative evaluation of alternative control loop configurations for interacting process units, applying domain expertise, and using techniques such as relative gain array and decouplers. How about using reinforcement learning (RL)?

This video shows an example that introduces the elements of RL. It also provides an overview that describes a MIMO process control design problem and demonstrates how you can use RL to generate a design solution. See how the RL results compare with those derived from a traditional design approach and discover further possibilities for using RL in broader applications.