Digital Twin AI Control for Rapid Plant Upset Stabilization
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Solution Overview
Problem
Industrial plants face process upsets and shutdowns that are challenging to mitigate due to dependence on operator knowledge and experience, leading to potential hazards and prolonged stabilization times.
Innovation Solution
A machine-learned model using reinforcement learning is trained with past process disturbance data and simulated scenarios in a digital twin environment to determine and implement optimal stabilizing strategies, reducing the likelihood of shutdowns and enhancing safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If plant operators determine optimal mitigating strategy manually, then the strategy can be optimized based on knowledge and experience, but it takes hours to days to determine and implement the strategy
Solution Approach 1:
The system performs preliminary actions by pre-training the machine-learned model using reinforcement learning on historical process disturbance data and simulated scenarios in a digital twin environment. This preliminary training enables the model to rapidly determine optimal mitigating strategies during actual plant upsets without requiring manual analysis from operators, thus resolving the contradiction between strategy optimality and response time.
Solution Approach 2:
The patent replaces the mechanical system of manual operator analysis and decision-making with an automated machine-learned model. The model uses reinforcement learning algorithms to process sensor data and generate mitigating strategies, substituting human cognitive processes with computational processes that operate much faster while maintaining or improving strategy optimality.
2Loss of time
If plant operators act immediately to stabilize the plant, then response time is reduced, but optimal mitigating strategy cannot be determined
Solution Approach 1:
The system enables self-service by allowing the machine-learned model to autonomously determine and implement mitigating strategies without requiring human operator intervention for strategy formulation. The model independently processes plant data, evaluates potential actions using reinforcement learning, and generates optimal strategies in real-time, enabling immediate response while maintaining strategy optimality.
Solution Approach 2:
The system implements feedback mechanisms where the machine-learned model continuously monitors plant conditions, evaluates the effectiveness of implemented actions, and adjusts strategies in real-time. This closed-loop feedback enables the system to respond immediately while continuously optimizing the mitigating strategy based on observed outcomes, resolving the contradiction between speed and optimality.
3Ease of operation
If manual operator knowledge and experience are used for stabilization, then human judgment can be applied, but additional hazards may occur due to delayed response
Solution Approach 1:
The patent replaces human operator judgment and decision-making processes with an automated machine-learned model that processes plant data and generates mitigating strategies. This substitution eliminates the delays inherent in human cognitive processing while maintaining high-quality decision-making through reinforcement learning, thereby reducing hazards associated with delayed response without sacrificing the quality of judgment.
Solution Approach 2:
The system introduces an intermediary machine-learned model that acts as a mediator between plant conditions and mitigating actions. This intermediary processes sensor data, applies reinforcement learning principles, and generates optimized strategies, serving as a bridge that combines the speed of automation with the quality of expert judgment, thereby reducing hazards while maintaining operational effectiveness.
Data Source
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.