How do you design and implement a Model Predictive Control (MPC) system?

MPC implementation involves: process modeling (step response or transfer function models through testing), variable selection (controlled variables, manipulated variables, disturbance variables), constraint definition (valve limits, rate limits, process constraints), tuning (prediction horizon, control horizon, move suppression weights, CV/MV priorities), and real-time optimization integration. Design steps: steady-state analysis, dynamic matrix identification through plant tests, controller configuration, simulation validation, commissioning with constraints active, and performance monitoring. MPC handles multivariable interactions, constraints, and optimization simultaneously, ideal for complex units like distillation columns and reactors.