Hydrocarbon Storage Leak Detection with Thermodynamic Sensor Validation
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Solution Overview
Problem
Conventional surveillance systems for hydrocarbon extraction and storage environments are inefficient in monitoring equipment status and do not effectively detect sensor malfunctions, leading to undetected leaks.
Innovation Solution
A leak detection system using a thermodynamic model and machine learning engine to process sensor data, generate feature vectors from video data, and confirm sensor outputs by detecting discrepancies between hydrocarbon inputs and outputs, with a distributed ledger system to verify leak-free conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional surveillance systems are used to monitor hydrocarbon equipment, then the system structure is simple, but the monitoring efficiency is low and sensor malfunctions are not detected
Solution Approach 1:
The patent introduces a thermodynamic model as an intermediary between sensor data and leak detection. This model processes sensor readings to determine expected hydrocarbon quantities, serving as a mediator that enables automated anomaly detection without requiring complex real-time video analysis infrastructure
Solution Approach 2:
The patent replaces the mechanical/manual surveillance system with an automated computational system. Instead of relying on human operators reviewing video footage, the system uses thermodynamic calculations and machine learning algorithms to automatically detect leaks, substituting mechanical monitoring with intelligent automated detection
2Reliability
If conventional surveillance systems are used, then the device complexity is low, but the ability to detect sensor malfunctions is poor
Solution Approach 1:
The patent implements feedback by comparing actual sensor readings against thermodynamic model predictions. The system continuously monitors discrepancies between expected and actual hydrocarbon quantities, providing feedback that enables automated detection of sensor malfunctions and triggers appropriate responses
Solution Approach 2:
The patent performs preliminary actions by pre-processing sensor data through thermodynamic calculations to establish expected baselines before actual leak detection occurs. The system pre-computes what hydrocarbon quantities should be present based on operational parameters, enabling faster and more reliable anomaly detection when discrepancies occur
3Loss of time
If manual video review is used to monitor equipment, then the system cost is low, but the detection speed and timeliness are insufficient
Solution Approach 1:
The patent enables self-service by allowing the system to automatically monitor itself without human intervention. The thermodynamic model continuously evaluates sensor data and the machine learning system autonomously detects leaks, eliminating the need for manual video review and significantly reducing detection time
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system efficiently monitors hydrocarbon storage environments, effectively detects sensor malfunctions, and ensures timely detection of leaks, reducing environmental harm by confirming sensor outputs and generating non-fungible tokens for leak-free verification.
Implementation Method 1
processing the sensor data using a thermodynamic model to determine when a discrepancy exists between the hydrocarbon inputs and the hydrocarbon outputs
Data Source
AI summary
Various embodiments of the present technology relate to solutions for leak detection in hydrocarbon storage environments. In some examples, a leak identification system confirms sensor outputs in a hydrocarbon storage environment. The leak identification system comprises processing circuitry. The processing circuitry obtains sensor data that characterizes hydrocarbon inputs and hydrocarbon outputs in the hydrocarbon storage environment. The processing circuitry processes the sensor data using a thermodynamic model to determine when a discrepancy exists between the hydrocarbon inputs and the hydrocarbon outputs. The processing circuitry generates feature vectors that represent video data that depicts the hydrocarbon storage environment. The processing circuitry provides the feature vectors as input to a machine learning engine trained to detect hydrocarbon leaks in the hydrocarbon storage environment. The processing circuitry receives a machine learning output that indicates when a hydrocarbon leak exists in the hydrocarbon storage environment.