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Advancements in Absorbance Recovery for Enhanced TDLAS Measurements
A team of researchers from the Hefei Institutes of Physical Science (HFIPS) at the Chinese Academy of Sciences has pioneered a new method utilizing neural networks to enhance the accuracy of single-path tunable diode laser absorption spectroscopy (TDLAS). This innovative approach is aimed at addressing the common challenges associated with measurement errors in existing TDLAS methodologies.
The findings of their research were published in the journal Fuel, emphasizing the significance of accurate combustion flow field temperature and concentration measurement. Such data are critical for the design, monitoring, and diagnosis of advanced combustion systems, offering insights that go beyond traditional measurement techniques.
TDLAS is praised for its speed, high sensitivity, and resistance to interferences. However, the conventional single-path approach has been hindered by substantial measurement errors, largely due to baseline distortion affecting absorbance readings. Prof. Liu Kun, a key researcher on the project, noted that “the main challenge involves correcting baseline errors that impact absorbance measurements.”
The research team identified that the derivative of absorbance is more responsive to changes in the line shape curvature, particularly near the absorption peak where baseline error-induced variations are minimal. Building on this understanding, the HFIPS researchers, under the guidance of Prof. Gao Xiaoming and Prof. Liu Kun, devised a model capable of extracting the absolute absorbance profile from the derivative data.
This new absorbance recovery method underwent rigorous testing through simulations and practical temperature assessments. It was applied effectively in measuring exhaust temperature and water concentration in a small diesel turbojet engine, yielding impressive results with an error margin of only 0.9% when compared to traditional thermocouple readings.
“Our results present a promising technique for enhancing the accuracy of TDLAS measurements, and it can be seamlessly integrated into existing tomographic absorption spectroscopy systems,” stated Prof. Gao Xiaoming.
More information: Ruifeng Wang et al, Measurement of engine exhaust plume temperature and concentration distributions with tomographic absorption spectroscopy and learning-based absorbance recovery, Fuel (2024). DOI: 10.1016/j.fuel.2024.132775
Citation: Neural network improves tunable diode laser absorption spectroscopy quantification accuracy (2024, September 12) retrieved 12 September 2024 from https://phys.org/news/2024-09-neural-network-tunable-diode-laser.html
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