Plant-Wide MPC Optimization for Continuous and Batch Production
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Solution Overview
Problem
Industrial process control and automation 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 Controller (MPC) architecture is implemented, where a master MPC controller uses a planning model to perform plant-wide optimization, honoring constraints from slave MPC controllers, and employs proxy limits to merge multiscale models, enabling real-time closed-loop optimization of both continuous and batch processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a master MPC controller performs plant-wide optimization integrating continuous and batch operations, then production efficiency and profitability are improved, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The control system is segmented into a hierarchical structure with a master MPC controller for plant-wide optimization and multiple slave MPC controllers for specific process units. This segmentation allows the complex optimization problem to be divided into manageable parts while maintaining overall integration, thus improving productivity without overwhelming system complexity.
Solution Approach 2:
The master MPC controller acts as an intermediary that coordinates between continuous production processes and batch operations. It receives constraints and information from slave controllers and performs integrated optimization, serving as a mediator that harmonizes different production modes and time frames.
2Manufacturing precision
If the master MPC controller integrates constraints from multiple slave MPC controllers, then the optimization accuracy and product quality are improved, but the computational burden and processing time increase
Solution Approach 1:
Slave MPC controllers pre-process and prepare their constraints and process information before the master controller performs optimization. This preliminary action allows the master controller to receive pre-validated data, reducing computational burden and processing time while maintaining optimization accuracy.
Solution Approach 2:
The system dynamically adjusts the optimization horizon and constraint integration based on process conditions and batch schedules. By making the optimization process adaptive rather than static, the system balances computational accuracy with processing time requirements.
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.