Thermal Fill Level Detection for Petrochemical Storage Vessels
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Solution Overview
Problem
Petrochemical storage systems lack efficient methods to determine fill levels without hazardous manual measurements, and they do not effectively utilize machine learning systems for this purpose.
Innovation Solution
A system utilizing a thermal imaging device and machine learning engine to generate feature vectors from fuel storage equipment images, which are processed to determine fill levels, thereby generating notifications based on machine learning outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Guided Wave Radar (GWR) is used to measure fuel levels, then measurement capability is achieved, but operator safety deteriorates due to exposure to toxic and carcinogenic substances
Solution Approach 1:
The patent introduces an infrared camera as an intermediary device that captures thermal images of the storage tank. The camera detects temperature differences between the liquid-filled portion and the vapor-filled portion of the tank, allowing measurement of fuel levels without direct operator contact with hazardous substances. The infrared camera acts as a mediator between the operator and the toxic environment.
Solution Approach 2:
The patent replaces the mechanical GWR measurement process with an optical/infrared detection system. Instead of using radar waves and physical access to the tank, the system uses infrared thermal imaging to detect fuel levels remotely, eliminating the need for operators to physically access hazardous areas.
2Measurement precision
If GWR measurement process is followed, then fill level data is obtained, but time consumption increases due to manual operations and protective equipment setup
Solution Approach 1:
The system implements automated detection and classification of fuel levels using machine learning algorithms. The infrared camera continuously captures thermal images, and the embedded processing unit automatically analyzes the temperature patterns to determine fill levels without requiring manual intervention. The system serves itself by autonomously performing measurements and generating reports.
Solution Approach 2:
The patent employs pre-trained machine learning models that have been trained beforehand on thermal image data. When a measurement is needed, the system immediately applies the pre-trained classification algorithms to new thermal images, eliminating the need for time-consuming manual analysis or setup during actual measurements.
3Measurement precision
If storage equipment port is opened for GWR measurements, then fuel level can be measured, but environmental harm increases due to vapor release
Solution Approach 1:
The infrared camera serves as a remote intermediary that enables fuel level measurement without opening the storage tank port. By detecting thermal patterns through the tank exterior or via remote imaging, the system eliminates the need to breach the sealed environment, preventing harmful vapor release into the atmosphere.
4Measurement precision
If traditional pressure gauges are used, then fill level monitoring is attempted, but reliability fails due to corrosion from petrochemicals
Solution Approach 1:
The patent replaces contact-based mechanical pressure gauges with non-contact infrared thermal imaging. The infrared camera detects temperature differences on the tank surface or within the fuel vapor space without physical contact with corrosive petrochemicals, eliminating corrosion issues while maintaining measurement capability.
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
This approach allows for efficient and safe determination of fill levels in petrochemical storage equipment, reducing hazardous manual measurements and enhancing operational safety and efficiency.
Implementation Method 1
a thermal image that depicts a storage vessel
Implementation Method 2
The filled and unfilled portions of the storage vessel will have different temperatures
Data Source
AI summary
Various embodiments of the present technology relate to systems and methods to determine fill levels in a fuel extraction and storage environment. In some examples, a system comprises a thermal imaging device, a machine learning interface, and a machine learning engine. The thermal imaging device generates a thermal image that depicts fuel storage equipment. The machine learning interface generates feature vectors based on the thermal image that depicts the fuel storage equipment and feeds the feature vectors to a machine learning engine. The machine learning engine ingests the feature vectors, generates a machine learning output that indicates a fill level for the fuel storage equipment based on the feature vectors, and transfers the machine learning output.