Laser-based selective BTEX sensing using deep neural networks
Mhanna Mhanna, Mohamed Sy, Ayman Arfaj, José Llamas, Aamir Farooq
Abstract
A mid-infrared absorption-based laser sensor is developed for selective and simultaneous benzene, toluene, ethylbenzene, and xylenes (BTEX) measurements under ambient conditions. The sensor is based on a distributed feedback inter-band cascade laser emitting near 3.3 µm. Wavelength tuning and deep neural networks were employed to differentiate the broadband absorbance of BTEX species. The sensor was validated with gas mixtures and real-time measurements were demonstrated at a temporal resolution of 1 s. Minimum detection limits for BTEX in air are 8, 20, 5, and 46 ppm, respectively. This sensor can be utilized to monitor BTEX emissions in the petrochemical, rubber, and paint industries to avoid hazardous health effects.