Digital Twin AI Control for Plant Shutdown Mitigation
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Solution Overview
Problem
Industrial plants face process upsets and shutdowns that current mitigating strategies, reliant on operator knowledge and experience, often take hours to days to stabilize, leading to potential hazards and inefficiencies.
Innovation Solution
A machine-learned model using reinforcement learning is trained within a digital twin environment to simulate plant operations, evaluating initial and subsequent shutdown states and rewarding actions that reduce shutdown likelihood, enabling quick determination and implementation of optimal stabilizing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If current mitigating strategies relying on operator knowledge and experience are used, then plant stabilizing decisions can be made with human judgment, but it takes hours to days to determine and implement optimal strategies, leading to prolonged shutdowns and hazards
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the industrial plant that replicates the physical plant's behavior and processes. This digital replica allows operators to test and evaluate stabilizing strategies in a virtual environment before implementing them in the actual plant, significantly reducing the time required to determine optimal strategies while maintaining safety through virtual experimentation.
Solution Approach 2:
The system performs preliminary analysis and strategy development in the digital twin environment before actual plant intervention is needed. By pre-evaluating multiple stabilizing strategies in the virtual model and preparing optimal action plans in advance, the system enables rapid implementation when real plant upsets occur, reducing both response time and potential hazards.
2Speed
If plant operators are required to act immediately to stabilize the plant without time to determine optimal mitigating strategy, then rapid response to hazards is achieved, but the lack of optimal strategy determination incurs additional hazards
Solution Approach 1:
The digital twin serves as a safe virtual testing ground where multiple stabilizing strategies can be rapidly evaluated and compared without risking the actual plant. Operators can immediately test various actions in the digital replica and identify the most effective strategy, enabling fast response with minimized hazards through virtual pre-validation.
Solution Approach 2:
The system provides real-time feedback from the digital twin about the potential outcomes of different stabilizing actions. This feedback mechanism allows operators to see the predicted effects of various strategies before implementation, enabling immediate informed decisions that reduce hazards while maintaining rapid response capability.
3Reliability
If more comprehensive analysis of plant operations is performed to ensure safety and stability, then plant reliability is improved, but the time required for analysis and decision-making increases
Solution Approach 1:
By performing comprehensive analysis in the digital twin rather than directly on the physical plant, the system can conduct thorough evaluations of multiple strategies without time penalties. The virtual environment allows parallel processing of numerous scenarios and comprehensive data analysis, achieving both thoroughness and speed that cannot be obtained through traditional single-plant analysis.
Solution Approach 2:
The system evaluates multiple stabilizing strategies beyond what a single operator would typically consider, testing a broader range of actions in the digital twin. This excessive analysis in the virtual environment ensures optimal reliability by considering more possibilities, while the comprehensive nature of the digital model allows this expanded analysis to be completed efficiently.
Data Source
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AI summary
A machine-learned method and system for use in operating an industrial plant. A digital twin of the industrial plant is configured to simulate plant operations based on operating variables from a data store. A machine-learned model comprises a stabilizing agent and a disrupting agent. The stabilizing agent modifies the operating variables within the digital twin to perform a stabilizing action for limiting a degree of shutdown and the disrupting agent modifies the operating variables within the digital twin to perform a disruptive action for increasing the degree of shutdown. A composite action reward is configured to reward the machine-learned model for reducing the degree of shutdown from an initial state of the digital twin to a post-action-state of the digital twin.