1. Decomposed optimization-control problem
A Two-Level Strategy of Integrated Dynamic Optimization and Control of Industrial Processes - A Case Study
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
Decomposed optimization-control problem
Constraints:
Changing market conditions
Process disturbances
Operational & safety constraints
D-RTO
MPC
Vertical decomposition approach
D-RTO
Estimation
D-RTO trigger
MPC
Decomposition based on objectives  economic optimization (D-RTO) & tracking (MPC) subproblems
Different models, derived from a first principle model, at each level
Different set of constraints at each level
Plant (model)
(incl. base control)
Interplay between D-RTO and MPC
Implementation
A Matlab implementation of an EKF for constrained state estimation
Soft constraints can be moved from MPC to D-RTO
Longer time horizon for D-RTO to ensure feasibility
D-RTO trigger for a possible re-optimization
based on disturbance sensitivity analysis of optimal solution
a re-optimization is triggered only if the detected persistent disturbances have high sensitivities
D-RTO
An MPC using linear time variant model
LTVMPC
updated
updated
An MPC using sequential approach dynamic optimization
ADOPTmpc
(MPC)
EKF
(Estimator)
D-RTO trigger
DYNOPC
A simultaneous approach based dynamic optimizer
(in collaboration with CMU, Pittsburgh, USA)
ADOPTrho
(D-RTO)
Scheduler
INCA-OPC server
MPC
Connection to DCS (process plant) possible
a strict operation envelope is computed which is used on the MPC level
An extension of a sequential approach based dynamic optimizer (ADOPT) for real-time applications
gPROMS
(Process model)
Delta-mode MPC computes updates to the control profiles for tracking the process in the strict operation envelope: rejects fast frequency process disturbances
D-RTO optimization problem is initialized with the solution on previous time horizon
MPC optimization problem is initialized with the D-RTO solution
A flexible software architecture for implementation of the two-level strategy
Is being applied to large-scale industrial processes
Case study: Semi-batch reactive distillation column
Problem description
Discussion
Conclusion
Methyle acetate (MA) semi-batch reactive distillation column: gPROMS model with 817 DAEs
Objective: Maximize production of MA for a fixed batch time of 4 hours (optimum by an off-line optimization)
Control variables: reflux ratio R, vapor stream V
Disturbance scenario: 50% drop in side stream feed rate and other nominal process disturbances
Application of two-level strategy, nonlinear MPC (NMPC), delta-mode MPC and open-loop operation
Open-loop operation: the desired product quality (xD) is not met
Delta mode MPC and NMPC only:
rigorous nonlinear model (the best option that can be considered) is used
produce off-spec product (economically infeasible)
not economically viable
Two-level strategy: Real-time dynamic optimization and delta mode MPC
re-optimization triggered by the sensitivity-based approach
new reference trajectories are determined
desired product quality is met in the closed loop operation
economically feasible operation
A different strategy than the traditional MPC approach
Flexible plant operation in changing market and operating conditions can be achieved by two-level strategy
Guaranteed overall (economical and operational) feasibility that might not be achievable by an MPC only
can handle large-scale industrial problems
Future research work:
Rigorous strategy for D-RTO trigger, disturbance forecasting
Relation of process models on different levels
Fast numerical algorithms on different levels, etc…