Machine Learning Fugitive Leak Prediction Model
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Solution Overview
Problem
Current methods for monitoring fugitive leaks in operational systems are inefficient, as they rely on infrequent LDAR inspections and physically installed sensors, which are costly and unable to detect leaks in real-time, leading to delayed detection and maintenance.
Innovation Solution
A system using a machine learning-based fugitive leak prediction model that receives current and historical operating conditions data to predict fugitive leaks, generating alerts and predictions without the need for physical sensors, by correlating sensed operating conditions with historical emissions data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physically installed sensors are used for leak detection, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of the physical sensor system by training a machine learning model on historical sensor data and operational parameters. This digital twin replicates leak detection functionality without requiring additional physical sensors, thereby maintaining measurement precision while reducing device complexity
Solution Approach 2:
The patent replaces the mechanical/physical sensor installation system with a computational machine learning model. Instead of physically installing sensors throughout the operational system, the model processes existing operational data to predict leaks, substituting physical infrastructure with information processing
2Device complexity
If infrequent LDAR inspections are used, then device complexity is reduced, but detection speed and reliability deteriorate
Solution Approach 1:
The patent implements continuous leak detection by processing operational data in real-time through the machine learning model. Instead of periodic inspections, the system continuously monitors operational parameters and predicts leaks as they develop, maintaining low complexity while dramatically improving detection reliability and timeliness
Solution Approach 2:
The model performs preliminary detection by identifying early signs of potential leaks before they become actual failures. By analyzing trends in operational data, the system predicts impending leaks and enables preventive maintenance, improving reliability by catching issues before they manifest as detectable failures
3Speed
If physical sensors are installed for real-time monitoring, then detection speed is improved, but cost and device complexity increase
Solution Approach 1:
The patent creates a virtual monitoring system that replicates real-time sensor functionality through machine learning. The model processes existing operational data streams to provide real-time leak predictions without requiring additional physical sensors, achieving fast detection while avoiding the complexity and cost of extensive sensor installations
Solution Approach 2:
The machine learning model serves multiple functions simultaneously: it processes various operational parameters (temperature, pressure, flow rates), performs anomaly detection, predicts leaks, and generates maintenance alerts. This multi-functionality achieves real-time monitoring capabilities without requiring separate specialized systems for each function, reducing overall complexity
Data Source
AI summary
Methods, apparatuses, and computer program products for predicting fugitive leaks are provided. For example, a computer-implemented method may include receiving current operating conditions data associated with current operation of one or more operational systems and generating, using a fugitive leak prediction model, fugitive leak predictions corresponding to the current operation of the one or more operational systems. The fugitive leak prediction model may be a machine learning model trained based at least in part on historical operating conditions data associated with past operation of the one or more operational systems and historical fugitive emissions data associated with the past operation of the one or more operational systems, and the fugitive leak prediction model may be configured to generate the fugitive leak predictions based at least in part on the current operating conditions data