Towards Integrated Dynamic Real-Time Optimization and Control of Industrial Processes Two-level strategy Vertical decomposition Optimal process operation Objectives: Maximize profit On-spec production Feasible operation profiles Implications Complex dynamic optimization and control problem Involves repetitive decision making D-RTO MPC Constraints: Changing market conditions Process disturbances Operational & safety constraints Vertical decomposition approach D-RTO D-RTO trigger Estimation D-RTO time-scale MPC time-scale MPC Decomposition based on objectives  economic optimization (D-RTO) & tracking (MPC) subproblems Different models, derived from a first principle model, different set of constraints at each level Re-optimization may not be necessary at each sampling time Plant (model) (incl. base control) Sensitivity analysis for D-RTO Interplay between D-RTO and MPC D-RTO MPC updated ....... D-RTO trigger: sensitivity analysis Lagrange function sensitivities w.r.t. all estimated disturbances Optimal solution sensitivities w.r.t. all estimated disturbances Economic objective only at D-RTO level and disturbance rejection at MPC level soft constraints can be moved from MPC to D-RTO Consistent process models ( , ) at each level a LTV model along reference trajectories by linearization of D-RTO model at MPC level Longer time horizon for D-RTO to ensure feasibility compute compute and changed active constraint set One sensitivity integration of process model at each sampling time using previous D-RTO results (and active constraint set) at is required Compute change in sensitivities ( ) and Lagrange function ( ) can be then calculated Solution to QP problem: using second order information (Hessian of Lagrange function)  optimal sensitivities using first order information  feasible only sensitivities updates as D-RTO trigger for a potential re-optimization and quick feasible updates of based on disturbance sensitivity analysis of optimal solution a re-optimization is triggered only if the detected persistent disturbances have high sensitivities and causes active constraint set change uref, yref If and are larger than a threshold value Sth and changed active constraint set is predicted, a re-optimization should be done Else linear updates based on optimal solution sensitivities are sufficient Application to an industrial process Problem description Dynamic optimization results and feasible updates due to change in parameter 1 M Polymer Catalyst Reactor Solvent Recycle monomer Separation Monomer Continuous polymerization process with frequent grade changes Optimal grade change problem minimize off-spec polymer production and transition time fresh and recycle monomer feed rate and catalyst feed rate as control variables two uncertain reaction parameters and open-loop unstable operation Process model of about 2000 DAEs Reaction parameters randomly perturbed between their bounds Re-optimization done only when necessary  steer to desired grade Feasible updates only possible up to +4% change in parameter 1 The integrated D-RTO and control with embedded sensitivity analysis is well suited for large-scale industrial process operation A software package with integrated dynamic optimizer, MPC, EKF and OPC data server is developed Nominal solution Reoptimized solution Fesible updates