What distinguishes model predictive control (MPC) from traditional PID control in advanced process control (APC)?

Model Predictive Control (MPC) distinguishes itself from traditional PID (Proportional-Integral-Derivative) control by using a dynamic model of the process to predict future process behavior and optimize control actions over a time horizon, while PID control reacts only to current and past errors. PID control is a feedback control algorithm that calculates a control action based on the difference between the desired setpoint and the measured process variable. MPC, on the other hand, uses a mathematical model of the process to predict how the process will respond to different control actions over a future time horizon. This allows MPC to anticipate future disturbances and optimize control actions to minimize the impact of these disturbances and keep the process operating at its desired setpoint. Furthermore, MPC can handle multivariable control problems, where multiple process variables are controlled simultaneously, and can take into account constraints on process variables and control actions. PID control is typically used for single-input, single-output control problems and does not explicitly handle constraints. MPC's predictive capability and ability to handle multivariable control and constraints make it a powerful tool for advanced process control in complex petrochemical processes, leading to improved process stability, efficiency, and profitability.