Introduction to TDLAS Technology
Tunable diode laser absorption spectroscopy (TDLAS) technology has gained much appreciation for its ability to detect greenhouse gases mainly because it offers the opportunities of non-contact and real time measurements. Nevertheless, the problem of discordance in gas absorption spectral interferences has become a major technical challenge, which affected the capability of simultaneous measurements of multi-component gases. Breakthrough in Gas Detection Technology
Neural Network-Based Decoupling Algorithm
In response to this challenge, a research team led by Prof. Gao Xiaoming of the Hefei Institutes of Physical Science at the Chinese Academy of Sciences has designed an alleviated neural network decoupling algorithm for aliased spectra. This innovative solution enables a low cost and, therefore, low complexity way to mitigate the problems caused by TDLAS technology.
Significant Improvement in Gas Detection
This neural network algorithm has made it much easier and more accurate to identify more than one gas at an instance,’ added Prof. Gao. The research has been reported in the ACS Sensors journal, abbreviated as a scientific publication.
Training the Neural Network
According to the findings of the researchers, they calculated the modulation depth under controlled lab environment and collected a large amount of spectra containing aliasing for the neural network model. This is because extensive training of the model helped it to generalize the results across the different conditions. In addition, experimental data was also gathered to retune the proposed model with the aim of proving its utility.
Simplicity and Efficiency
The beauty of this new method lies in its simplicity,” said Gao. “It requires no additional hardware.
Cost-Effective and Accurate Solution
This approach allowed the team to decouple spectral interferences in the existing system with the help of the neural network-based decoupling algorithm, thus simplifying the design and decreasing its cost. Not only did the algorithm separate multi-component gas signals with high accuracy and stability, but transfer learning mechanism also enabled the algorithm to function well in complex environment. It even enabled to detect more than one gas using a single laser which remarkably improved the efficiency of the method.
Implications for Future TDLAS Systems
This work highlights the strong potential for neural networks to differentiate aliased spectra, as demonstrated by the following conclusions for future use of TDLAS gas detection systems in challenging environments.
