Distributed Process Control Using Inter-Sub-Process Models
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Solution Overview
Problem
Existing process control systems for large industrial processes with multiple sub-processes face challenges in maintaining optimal control, as single centralized models are complex and inflexible, while independent controllers often result in unstable and sub-optimal solutions.
Innovation Solution
A distributed cooperative process control system comprising multiple local control modules that communicate and cooperate to achieve a shared objective function, using sub-process and inter-sub-process models to adjust manipulated variables and account for interactions between sub-processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single centralized model based predictive controller is used to control all sub-processes, then all interactions between variables are accounted for, but the controller becomes difficult to maintain especially when the process is very large or geographically dispersed
Solution Approach 1:
The centralized controller is segmented into multiple distributed control modules, each responsible for controlling a specific sub-process. Each control module contains a local process model for its sub-process and inter-sub-process models for interactions with other sub-processes. This segmentation reduces the complexity of individual controllers while maintaining comprehensive interaction modeling through the inter-sub-process models.
2Ease of operation
If several independent model based predictive controllers are provided for each sub-process, then the controllers are easier to configure and maintain, but the resulting control may be unstable and unable to achieve the global optimum
Solution Approach 1:
The distributed control modules exchange information about their manipulated and controlled variables through inter-sub-process models. This feedback mechanism allows each local controller to adjust its control actions based on the state and actions of other sub-processes, ensuring coordinated control that maintains stability and achieves global optimization while preserving the ease of configuration and maintenance of distributed architecture.
Data Source
AI summary
A process control system for controlling a process including a plurality of sub-processes, the process control system including a plurality of control modules each associated with one of the plurality of sub-processes. At least one of the plurality of control modules includes a model, a communicator, and a controller. The model includes a sub-process model defining a relationship between variables of the associated sub-process, and an inter-sub-process model defining a relationship between a variable of another sub-process and at least one of the variables of the associated sub-process. The communicator communicates with control module associated with the another sub-process to determine an updated value for the variable of the another sub-process. The controller uses the model and the updated value to determine a control signal for adjusting a manipulated variable of the associated sub-process. The process control method is also provided that is performed by the process control system.