Hierarchical MPC for Plant-Wide Continuous and Batch Optimization
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Solution Overview
Problem
Industrial process control systems face challenges in optimizing both continuous and batch operations, particularly in integrating intermediate components produced under different time frames, leading to inefficiencies and manual adjustments in production planning.
Innovation Solution
A cascaded Model Predictive Control (MPC) system that includes a master MPC controller and slave MPC controllers, using a planning model to perform plant-wide optimization by honoring constraints and adjusting process variables within the system, allowing for real-time optimization and automatic production planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a centralized optimization system is implemented to integrate continuous and batch operations, then production efficiency and coordination are improved, but system complexity and computational burden increase
Solution Approach 1:
The system divides the plant-wide optimization problem into multiple time-scale components: continuous process optimization (short-term) and batch production planning (long-term). The master MPC handles continuous operations while slave MPCs manage batch operations, allowing independent optimization of each segment while maintaining overall coordination through the hierarchical structure.
Solution Approach 2:
The patent introduces a hierarchical time-scale dimension to resolve the contradiction. By operating at multiple time scales (continuous vs. batch), the system can optimize productivity without requiring a single monolithic complex system. The master-slave MPC architecture adds a hierarchical dimension that separates computational complexity across different control levels.
2Adaptability or versatility
If real-time optimization is implemented for continuous processes, then responsiveness and adaptability are improved, but integration with batch operations becomes more difficult
Solution Approach 1:
The master MPC controller acts as an intermediary between continuous and batch operations. It receives constraints and information from slave MPCs managing batch operations and translates them into actionable optimization parameters for continuous processes. This intermediary layer enables real-time optimization while maintaining compatibility with batch operation schedules and constraints.
Solution Approach 2:
The system implements dynamic adaptation across time scales. The master MPC continuously adjusts continuous process parameters in real-time based on current conditions, while slave MPCs plan batch operations over longer horizons. This dynamic multi-scale approach allows real-time responsiveness without requiring complete re-optimization of the entire system at every moment.
3Reliability
If multiple constraints are enforced to ensure safe and feasible operations, then reliability and constraint satisfaction are improved, but flexibility in optimization is reduced
Solution Approach 1:
Slave MPCs perform preliminary action by pre-calculating batch operation schedules and constraints before execution. These pre-computed constraints are then passed to the master MPC, which uses them as fixed boundaries for real-time continuous optimization. This preliminary planning ensures reliability and constraint satisfaction while allowing the master MPC flexibility within the predefined boundaries.
Solution Approach 2:
Different levels of the hierarchical system apply different constraint enforcement strategies appropriate to their time scales. Slave MPCs enforce hard constraints for batch operations where reliability is critical, while the master MPC allows more flexibility in continuous parameters where adaptability is needed. This local differentiation of constraint quality maintains reliability where needed while preserving optimization flexibility elsewhere.
Data Source
AI summary
Constraints are received on initial components and intermediate components. Information is received on the products to be produced including a quantity of each of the products to be produced and a specification that specifies how the intermediate components are to be combined to form each of the products. An optimization is performed that includes the continuous conversion of initial components into the intermediate components as well as subsequent production of the products, subject to the constraints on each of the initial components, the constraints on each of the intermediate components, and the quantity of each of the products to be produced.