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Intangles Lab Pvt. Ltd., one of India’s fastest-growing Digital Twin solutions providers, has been granted a patent with the United States Patent and Trademark Office for “Systems and Methods of Sub-Resolution Measurements in Fuel Tanks,” a machine learning algorithm developed with extensive global research and testing. The patent covers the underlying technology in Intangles’ fuel pilferage monitoring system that pinpoints the exact locations and quantities of fuel pilferage. At a time when fuel costs comprise a major chunk of the total cost of operations, fuel pilferage in the form of underfilling and tank drains is a matter of grave concern across diverse geographies and demographics. Intangles' devices are detecting over 200,000 liters of fuel pilferage on a monthly basis, in the process preventing up to a million liters of pilferage every quarter. This is empowering fleet managers and automobile OEMs to track unbiased fuel efficiency in real time and make informed operational decisions.

The algorithm provides a cost-effective solution for fuel pilferage, which has long led to financial losses for commercial vehicle fleets across the globe. Typically, in-built sensors in the fuel tank of commercial vehicles are unable to provide granular data about fuel levels, leading to undetected fuel pilferage. Unlike current market solutions that require expensive and tedious hardware installations to replace or augment these in-built sensors, Intangles’ solution is designed to work with the in-built sensors and amplify their resolutions using machine learning. 

“We are empowering fleet managers and automobile companies to make informed business decisions,” said Anup Patil, CEO and Co-founder at Intangles, adding, “Our technology enables greater accuracy and reliability without having to install additional sensors, many of which can be very expensive and damaging to the fuel tank. Our machine learning models enable the highest levels of precision with sensors inherently integrated with your vehicle.”

Intangles’ algorithm leverages the robustness of OEM-fitted sensors while using machine learning to match and improve upon the accuracy of other after-market solutions. With this proprietary technology, Intangles’ systems can distinguish between fuel sloshing and genuine pilferage with a precision upwards of 99%.  

“We aim to redefine the landscape of fuel management on a global scale. This will be accomplished by reducing losses resulting from underfillings, thefts, and adulteration along with boosting the overall fuel economy of fleets by as much as 10-15%,” said Aman Singh, Co-founder and Head of Analytics at Intangles.

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