Gas Leak Detection System Using Sensor Arrays and Machine Learning

The patent divides the detection task into multiple specialized sensors, each optimized for detecting specific gases or environmental parameters. Instead of relying on a single metal oxide sensor that responds to all gases indiscriminately, the system segments detection into: natural gas sensors, VOC sensors, humidity sensors, temperature sensors, and pressure sensors. This segmentation allows each sensor to excel at its specific function while the system integrates their data to achieve accurate natural gas detection despite environmental interference.

The patent introduces an intermediary computational layer (machine learning algorithm) that mediates between the raw sensor signals and the final detection decision. This intermediary process analyzes patterns across multiple sensor inputs, distinguishes between signals caused by natural gas versus those caused by environmental factors, and produces a refined detection output. The machine learning model acts as a mediator that separates the harmful environmental interference from the useful natural gas signal.

The patent replaces expensive, fragile, or complex instruments with inexpensive, robust sensors that can be deployed in large numbers. Instead of using costly optical gas imaging cameras or laboratory-grade gas chromatographs, the system employs affordable metal oxide sensors, VOC sensors, and environmental sensors that can be mass-produced and distributed across multiple locations. The low cost enables dense spatial deployment, which compensates for individual sensor limitations through collective data analysis.

The patent substitutes complex mechanical or optical measurement systems with electronic sensor arrays and computational algorithms. Rather than using optical gas imaging cameras that require complex optics, light sources, and image processing hardware, the system uses electronic gas sensors coupled with machine learning software. This substitution reduces mechanical complexity, lowers power requirements, and enables continuous operation without human intervention.

The patent adds temporal and computational dimensions to compensate for limited spatial sampling. Instead of relying solely on having many sensors deployed across the environment, the system uses time-series analysis of data from fewer locations and applies machine learning algorithms to infer leak origin and emission rates. The computational dimension processes patterns over time and across multiple sensor types to extract maximum information from a reduced spatial footprint.

The patent makes each sensor location multi-functional by equipping it with an array of different sensor types (natural gas sensors, VOC sensors, humidity sensors, temperature sensors, pressure sensors) rather than using single-purpose sensors. This universality allows each monitoring point to detect multiple parameters simultaneously, enabling the system to identify leak origins and estimate emission rates from fewer locations by leveraging the diverse information available at each site.

The patent implements feedback mechanisms where sensors continuously monitor environmental conditions (temperature, humidity, pressure) and this information is fed back to the machine learning algorithm. The system uses this feedback to dynamically adjust its interpretation of gas sensor signals, compensating for temperature-driven response changes and humidity effects. The feedback loop enables the system to maintain reliable detection despite varying atmospheric conditions and sensor degradation over time.

The patent explicitly accounts for parameter changes in sensor behavior by measuring temperature, humidity, and pressure alongside gas concentrations. The machine learning model uses these environmental parameters to correct and normalize gas sensor readings, adjusting for the way temperature and humidity affect metal oxide sensor response. This parameter-based correction maintains reliability across different atmospheric conditions and throughout the sensor's operational life.