Gas Concentration Estimation Using Sequential Filtering
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Solution Overview
Problem
Solid-state gas sensors face challenges in accurately measuring hydrogen gas concentrations in mixed atmospheres due to influences from non-target gases, temperature, and delayed response, requiring effective mitigation strategies to improve measurement reliability and flexibility across varying operating conditions.
Innovation Solution
A system comprising multiple sensors, including resistive and capacitive sensors, and a processor executing a sequential estimation filter, such as a Kalman filter, to dynamically adjust state values and account for hydrogen and oxygen concentrations, temperature, and other influencing factors, enabling precise estimation of gas concentrations in mixed atmospheres.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If solid-state gas sensors are used to detect hydrogen gas concentration, then the sensor can provide measurements of hydrogen pressure, but non-target gases and temperature variations can influence the measurements and reduce accuracy
Solution Approach 1:
The patent segments the sensor response into multiple independent states (hydrogen pressure, oxygen pressure, temperature) and processes each separately through the sequential estimation filter. This allows the system to distinguish and measure hydrogen concentration independently from interfering gases and temperature effects, resolving the contradiction between measurement precision and environmental interference.
Solution Approach 2:
The sequential estimation filter acts as an intermediary between the raw sensor measurements and the final hydrogen concentration calculation. It uses the filter to compensate for temperature effects and non-target gas interference by processing measurements through a mathematical model that separates hydrogen signal from environmental noise, thereby improving measurement accuracy despite harmful factors.
2Speed
If the sensor response time is reduced for faster detection, then the system can respond quicker to gas changes, but the sensor takes tenths to thousandths of seconds to reach steady-state response
Solution Approach 1:
The sequential estimation filter performs preliminary processing of sensor measurements by continuously updating state estimates before the sensor fully reaches steady-state. This allows the system to infer gas concentration changes during the transient response period, effectively reducing the perceived response time while the physical sensor is still settling.
Solution Approach 2:
The filter uses feedback from continuous measurements to dynamically adjust state estimates. By comparing current measurements with predicted values from the model and updating the hydrogen pressure estimate accordingly, the system can track gas concentration changes in real-time during the transient response, reducing the effective response time while maintaining accuracy.
3Measurement precision
If the gas sensing system is calibrated for a specific operating environment, then the sensor response can be optimized for that condition, but the system loses flexibility when operating under varying conditions
Solution Approach 1:
The patent transitions from static calibration to dynamic adaptation by using the sequential estimation filter to continuously update hydrogen pressure estimates based on real-time measurements. The filter dynamically adjusts its state estimates and compensation factors according to changing operating conditions, allowing the system to maintain measurement precision across varying environments without requiring re-calibration.
Solution Approach 2:
The system changes its operational parameters dynamically by updating the filter's state estimates and compensation factors in real-time. Instead of using fixed calibration values, the system continuously adjusts its internal parameters based on measured temperature, oxygen pressure, and sensor response characteristics, enabling it to adapt to varying operating conditions while maintaining accuracy.
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 provides accurate and flexible gas concentration estimation by effectively mitigating the impacts of non-target gases and temperature, and reducing sensor response delays, thereby enhancing measurement reliability and adaptability across a broader range of operating conditions.
Implementation Method 1
at least one of the sensors sensitive to a concentration of hydrogen internal to said at least one of the sensors
Implementation Method 2
the plurality of sensors comprises a resistive sensor
Implementation Method 3
the plurality of sensors comprises a capacitive sensor
Implementation Method 4
a processor for receiving the set of measurements, and for executing a sequential estimation filter comprising a plurality of states
Data Source
AI summary
A system for estimating gas concentrations in a mixed atmosphere includes (a) a plurality of sensors for providing a set of measurements, at least one of the sensors sensitive to an internal concentration of hydrogen; and (b) a processor for receiving the set of measurements and for executing a sequential estimation filter that includes a plurality of states having a corresponding set of values. The processor responsively adjusts at least a portion of the set of values in response to the set of measurements. The plurality of sensors can include a resistive sensor and a capacitive gas sensor, both of which are sensitive to hydrogen concentration. The plurality of states can include states representative of hydrogen pressure in the mixed atmosphere, hydrogen concentration in a bulk material of at least one of the sensors, and hydrogen concentration in an interface layer of at least one of the sensors.