Laser Sensor Trace Gas Detection Spectroscopy
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Solution Overview
Problem
Current trace gas detection systems face challenges in accurately determining the concentration of species in gaseous samples, particularly in environments with varying aerosol distributions and noise, which affects the sensitivity and reliability of measurements.
Innovation Solution
The use of cavity ring-down spectroscopy (CRDS) systems that collect optical loss data over a range of frequencies, filter out noise, and fit spectral curves to determine species concentration, incorporating techniques such as iterative filtering and three-dimensional probability density mapping to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectroscopy methods are used to detect trace gases, then the detection can be performed with simple equipment, but the measurement precision and reliability are reduced due to aerosol interference and noise
Solution Approach 1:
The patent segments the optical loss data by dividing the frequency spectrum into multiple bins, allowing selective processing of different spectral regions. This segmentation enables the system to isolate and analyze specific absorption features while filtering out aerosol-related noise, thereby improving measurement precision without requiring complex hardware modifications
Solution Approach 2:
The patent introduces a new dimension of analysis by implementing iterative spectral curve fitting with multiple passes. The system performs initial curve fitting, identifies outliers, removes them, and then performs subsequent fitting iterations. This multi-dimensional processing approach in the data analysis domain enhances detection accuracy while maintaining relatively simple physical equipment
2Reliability
If spectral curve fitting is performed on raw optical loss data, then the processing is fast and simple, but the detection reliability is reduced due to noise and outlier values
Solution Approach 1:
The patent applies preliminary action by performing initial spectral curve fitting before final concentration determination. The system first fits a spectral curve to the raw optical loss data, then uses this initial fit to identify and remove outlier values that would otherwise compromise reliability. This preliminary processing step ensures that only clean, reliable data points are used in subsequent analysis
Solution Approach 2:
The patent implements feedback through iterative processing where the output of one fitting cycle feeds into the next. The system performs spectral curve fitting, evaluates the fit quality, removes identified outliers, and then performs another fitting cycle with the cleaned data. This feedback loop continuously improves detection reliability while the iterative nature allows the system to converge efficiently, minimizing time loss
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 precise detection of trace gases like methane and ethane, differentiating between sources, and providing quantitative emission measurements, even in complex aerosol environments, with improved sensitivity and reliability.
Implementation Method 1
collecting optical loss data over a range of frequencies from the sample using a spectroscopy system
Implementation Method 2
the spectroscopy system comprises a cavity ring-down spectroscopy system
Data Source
AI summary
Systems and methods are disclosed to determine the concentration of a species within a sample. An example method may include collecting optical loss data over a range of frequencies from the sample using a spectroscopy system; placing the optical loss data into a plurality of bins, each bin having a defined frequency width; determining an average optical loss data value for the optical loss values within each bin that have an optical loss value less than a threshold value; removing the optical loss data within each bin having a value outside a tolerance range bounding the average optical loss data value for the respective bin; fitting a spectral curve to the remaining optical loss data; and determining the concentration of the species within the sample based on the spectral curve.