Chemo-resistive Gas Sensor with Temperature Modulation and Machine Learning
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Solution Overview
Problem
Chemo-resistive gas sensors are affected by environmental variations, leading to inaccuracies in gas identification and concentration prediction, and require compensation for ambient humidity and temperature changes.
Innovation Solution
A gas sensing device employing chemo-resistive sensors with temperature modulation and machine learning algorithms to estimate humidity and gas concentrations, eliminating the need for additional sensors by leveraging dynamic sensor responses and feature extraction to improve accuracy and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chemo-resistive gas sensors are used for gas detection, then gas sensing capability is achieved, but measurement precision deteriorates due to environmental variations and long-term drift
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the operating temperature of chemo-resistive gas sensors based on environmental conditions. The system monitors temperature and humidity parameters and modifies sensor operating parameters accordingly to compensate for environmental variations, thereby maintaining measurement precision while preserving gas detection reliability.
Solution Approach 2:
The patent implements feedback mechanisms where sensor outputs are continuously monitored and compared against reference values. The system uses feedback loops to detect drift and environmental variations, automatically adjusting measurement parameters and applying correction factors to maintain precision in gas concentration measurements over time.
2Measurement precision
If additional sensors are added to compensate for environmental variations, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies universality by designing a multi-functional processing system that handles multiple tasks using a single integrated platform. The processor simultaneously performs gas concentration measurement, environmental parameter monitoring, drift compensation, and real-time calibration, eliminating the need for separate dedicated sensors for each function while maintaining high measurement precision.
Solution Approach 2:
The system implements self-service capabilities where the gas sensing device automatically compensates for environmental variations and performs real-time calibration without requiring additional external sensors. The integrated processor uses the sensor's own output signals and stored reference data to autonomously adjust measurements, reducing device complexity while preserving measurement precision.
3Measurement precision
If real-time compensation for environmental variations is implemented, then gas prediction accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating compensation factors and storing reference environmental data in lookup tables during system initialization. When real-time measurements are taken, the processor quickly retrieves pre-computed values from memory rather than performing complex calculations in real-time, thereby maintaining high gas prediction accuracy while minimizing processing time delays.
Solution Approach 2:
The patent implements partial action by applying compensation only for the most significant environmental factors (temperature and humidity) rather than attempting to compensate for all possible environmental variations. This selective approach maintains sufficient gas prediction accuracy while reducing the computational burden and processing time required for real-time compensation.
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 solution reduces long-term drift, enhances gas prediction accuracy, and provides humidity values without dedicated sensors, resulting in lower costs, reduced complexity, and improved performance in real-world scenarios.
Implementation Method 1
one or more heat sources for heating the gas sensors according to one or more first temperature profiles during the recovery phases and according to one or more second temperature profiles during the sense phases
Implementation Method 2
one or more chemo-resistive gas sensors, wherein each of the gas sensors is configured for generating signal samples corresponding to a concentration of one of the one or more gases in the mixture of gases
Data Source
AI summary
A gas sensing device includes gas sensors for generating signal samples corresponding to a concentration of a gas; a heat source for heating the gas sensors according to a first temperature profile during recovery phases and according to a second temperature profile during sense phases, a preprocessing processor for preprocessing the received signal samples; a feature extraction processor for extracting feature values from the preprocessed signal samples; a humidity processor for estimating a humidity value of the mixture of gases, including a first trained model based algorithm processor, and wherein the humidity value is based on an output of the first algorithm processor; a gas concentration processor for creating sensing results, wherein the gas concentration processor comprises a second trained model based algorithm processor, wherein the sensing results are based on output values of the second algorithm processor, and wherein the sensing results depend on the humidity value.