Adaptive control systems use real-time data from sensors to make adjustments. The process typically involves:
Monitoring: Sensors collect data on key parameters such as temperature, pressure, and concentration. Modeling: The data is used to update a mathematical model of the system, predicting the outcome of changes. Control: Based on the model, the system makes adjustments to the reaction conditions to maintain optimal performance.
Advanced techniques like model predictive control (MPC) and machine learning algorithms are often employed to enhance the accuracy and responsiveness of adaptive control systems.
