How do you design and implement multivariable predictive control (MPC)?

MPC design steps: (1) Define scope - identify controlled and manipulated variables, constraints. (2) Dynamic modeling - step testing to identify process gains and dynamics, typically 1-3 hours per MV-CV pair. (3) Model identification - fit first or second order plus dead time models. (4) Controller configuration - set horizons (control, prediction), move suppression, constraint handling. (5) Commissioning - implement in simulation first, then gradual rollout with close monitoring. (6) Tuning - adjust weights to balance competing objectives. Benefits: handles interactions, constraints, dead time, and optimizes operation. Common applications: distillation (maximize throughput at product specs), furnaces (minimize fuel with tube constraints), compressors (surge avoidance). Challenges: model maintenance, operator acceptance, software costs. Typical payback 6-18 months for well-selected applications.