Gas Detection Model Training With Digital Twin Leak Simulation
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Solution Overview
Problem
Current gas leak detection systems in industrial sites face limitations in accuracy due to stochastic atmospheric variables and require extensive, labor-intensive training with controlled gas discharges, which are detrimental or impractical.
Innovation Solution
A system and method using a digital twin to simulate gas leaks and weather conditions to train a machine learning model, generating simulated time-series sensor responses for training, followed by continuous retraining with actual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If controlled gas discharges are used to train the machine learning model, then the model training accuracy is improved, but it causes harm to the plant, employees, or surrounding community
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the industrial site including virtual gas sensors, weather conditions, and gas dispersion physics. This virtual environment generates synthetic training data that replicates real-world scenarios without physical gas releases, thereby maintaining training accuracy while eliminating harmful effects to actual plant and personnel
2Adaptability or versatility
If field measurements from manual inspections are used to train the model, then the model can be trained in operational state, but the process becomes very labor intensive requiring thousands of data points
Solution Approach 1:
The digital twin generates synthetic training data in advance through virtual simulations of gas leaks under various conditions. This preliminary generation of comprehensive training datasets eliminates the need for labor-intensive manual data collection during operational phases, while the model remains adaptable to real-world scenarios through the realism of the simulated data
Data Source
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AI summary
A system and method for training a machine learning gas detection model that monitors gas sensors located at an industrial site. The system and method uses a digital twin arranged to execute simulations of gas leaks using a virtual representation of the physical industrial site and varying simulated wind patterns, gas leak locations and leak rates. The simulations executed by the digital twin train a machine learning gas detection model with time-series gas sensor responses for the simulated gas leaks executed by the digital twin. The trained gas detection model is used in a gas detection system to monitor for gas leaks at the physical industrial site.