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Int J Fire Sci Eng > Volume 36(1); 2022 > Article
Kim, Park, and Lee: Method for Aerosol Particle and Gas Analyses based on Dual-channel Mid-infrared Sensor

Abstract

Although researchers are actively investigating methods to improve fire detector performance, few studies have investigated fire detectors that detect the type of fire. Fire type detection serves a key role in quickly extinguishing fires and preventing their spread. We present a non-dispersive infrared (NDIR)-based dual-channel mid-infrared (mid-IR) method that can detect and classify aerosol particles and gases. 4.2 μm and 4.7 μm mid-IR light emitting diodes (LEDs) light sources with strong absorption for CO2 and CO are employed. and, and the mid-IR LEDs are modulated with 900 Hz and 1,000 Hz, respectively to increase the signal-to-noise ratio and reduce interference between the light sources. The modulated lights pass through the lenses and sample, and are acquired by a photodetector. The transmittances of the 4.2 μm and 4.7 μm lights are measured to detect the aerosol particles and gases, and the aerosol particles and gases are classified via hierarchical clustering using the measured transmittances and the ratio between the measured transmittances. Various aerosol particles and gases are detected by measuring the transmittance, and the aerosol particles and gases are classified by calculating the distance between clusters. Spectral transmittances analysis of different wavelength bands will enable the detection of various aerosol particles and gases, and further improve the classification accuracy. Furthermore, this method can be applied to fire detection to develop a highly useful technique that can detect and classify fire smoke and rapidly detect the type of fire.

1. Introduction

A NDIR sensor is a simple sensor that can analyze the spectral information of a sample without dispersive elements such as a diffraction grating or prism [1,2]. Owing to its fast response speed, high accuracy, and low cost, it is utilized in various applications such as fire detection [3,4], air quality monitoring [5], and breathing analysis [6]. Particularly in fire detection, researchers have reported studies that combine NDIR sensors with multiple sensors [7], or use a fusion algorithm [8] to reduce nuisance alarms and the response time to fires, distinguish between fires and non-fires, and enhance detection accuracy.
Although many studies for improving the performance of fire detectors have been actively reported [9,10], studies on fire detectors that detect types of fire are insufficient. Detection of fire types is meaningful for rapidly extinguishing a fire and preventing the spread of fire. The fire safety standards for fire extinguishers and automatic fire extinguishing systems (NFSC 101) classify fires into Class A- general fires, Class B- oil fires, Class C- electrical fires, Class D- metal fires, and Class K- kitchen fires, and suggest specific extinguishing methods suitable for each type of fire. For example, foam and powder or an electrical insulating extinguishing agent that can block oxygen is required to extinguish oil and electrical fires. Using an unsuitable extinguishing agent such as water in Class B and C fires will accelerate its spread and cause secondary injuries from electric shock. Accordingly, detecting the type of fire is essential to swiftly extinguish the fire and minimize fire damage. Here, we present a method to detect and classify aerosol particles and gases using the NDIR-based dual-channel mid-IR sensor. To validate this, the spectral transmittances (Tλ) of mid-IR LEDs for aerosol particles and gases were measured and Tλ and a ratio between spectral transmittances (RatioT) were used to classify aerosol particles and gases via hierarchical clustering. Actively applying this technique to fire detectors is expected to enable a rapid detection of the fire type by analyzing fire smoke, thus helping to quickly extinguish fires and prevent their spread.

2. Methods

2.1 Experimental setup

Aerosol particles and gases have a unique light absorption spectra, depending on their molecular bonding structure and molecular vibrational state. The NDIR method measures the light absorbance of a sample, estimates its concentration, and selectively detects a specific sample. In this study, we present a method for detecting and classifying aerosol particles and gases using an NDIR-based dual-channel mid-IR measurement method. Figure 1 depicts the experimental setup. Mid-IR LED 1 (Boston Electronics, LED42Sr) and LED 2 (Boston Electronics, LED47Sr) in the 4.2 μm and 4.7 μm bands were utilized as the light sources, and the spectral transmittances of samples (i.e. aerosol particles and gases) were measured. Through lenses 1 (Thorlabs, Inc., LB5284) and 2 (Thorlabs, Inc., LB5774) with focal lengths of 50 mm and 25.4 mm, respectively, 4.2 μm and 4.7 μm lights are delivered to a photodetector (Thorlabs, Inc., PDA07P2). To reduce interference between the light sources and ambient light and increase the signal-to-noise ratio (SNR), 4.2 μm light and 4.7 μm light were modulated with frequencies of 900 Hz and 1,000 Hz, respectively, and the two modulated lights passing through the sample are measured with the photodetector at a sampling rate of 500 kHz. Water vapor, lighter, smoke detector tester spray (HSI Fire & Safety Group, LLC., SmokeCheck), and CO2 gas were selected as the samples. A chamber with a size of 30 × 30 × 60 mm was customized with a 3D printer and placed in front of the photodetector. In order to minimize the gas flowing in from the outside, a hole was made so that only the mid-infrared LED light source and the sample would pass. and a fan was installed at the sample outlet of the chamber to continuously suck in the sample at a wind speed of 2.5 m/s. The water vapor was sprayed using an ultrasonic humidifier, and a disposable gas lighter using butane gas as fuel was manually ignited using flint. The water vapor, smoke, and gas were sucked in by the fan. The smoke detector tester spray, which was stored in an aluminum can, and the CO2 gas, which comprised pure liquefied carbon dioxide (CO2) and was stored compressed in an aluminum cylinder, were manually sprayed for 5 s at a distance of approximately 3 cm from the sample inlet of the chamber through the nozzle.

2.2 Signal processing

Figure 2 shows the signal processing for calculating the measured optical signal as Tλ and RatioT. The 4.2 μm light and 4.7 μm light modulated with frequencies of 900 Hz and 1,000 Hz, respectively, were acquired for 0.5 second (Figure 2(a)). Fourier transform was employed to obtain the magnitudes of the frequency signals modulated at 900 Hz and 1,000 Hz (Figure 2(b)). Tλ was obtained by dividing the magnitude of the transmitted light by the magnitude of the incident light, and RatioT was expressed as RatioT = T4.2 μm/ T4.7 μm, of which T4.2 μm and T4.7 μm are the transmittances at wavelengths of 4.2 μm and 4.7 μm. Tλ and RatioT over time can be obtained using short-time Fourier transform (STFT), the results of are shown in Figures 2(c) and 2(d). STFT was performed with a window size of 250,000 samples and an overlap of 125,000 samples.

3. Results

To verify the feasibility of the detection and classification of aerosol particles and gases using the NDIR-based dual-channel mid-IR measurement, we performed experiments measuring the transmittance of various aerosol particles and gases, and then classified the aerosol particles and gases using hierarchical cluster analysis. Figure 3 presents the measured transmittance (i.e., T4.2 μm and T4.7 μm) results for the samples. The solid blue and orange lines indicate the measured T4.2 μm and T4.7 μm, respectively, and the black arrows indicate the starting point of the sample inflow. The transmittances for 4.2 μm light and 4.7 μm light were observed to approximately 1 at the absence of sample inflow (Figure 3(a)). With gas inflow when the lighter flame is ignited, T4.7 μm is constant at approximately 1, whereas is T4.2 μmslightly decreased (Figure 3(b)). The 4.2 μm mid-IR LED light source with a wide bandwidth also emits 3.4 μm light [11], which was absorbed by the butane gas [12]. The measured transmittances of the smoke detector tester spray which consists of isobutane, propane, siloxane, and butane gases are exhibited in Figure 3(c). Both T4.2 μm and T4.7 μm decreased owing to the multiple gases. For CO2 gas, which has a strong absorption spectrum at approximately 4.26 μm, T4.2 μmdecreased substantially, and a slight decrease was observed at T4.7 μm because of the wide bandwidth of the 4.7 μm LED light source (Figure 3(d)). The absorption spectrum of water highly depends on its molecular state (solid, liquid, or gas) [13,14]. For water vapor, both T4.2 μm and T4.7 μm were significantly dropped due to strong IR absorption (Figure 3(e)).
Figure 4(a) illustrates a three-dimensional (3D) graph of the values of T4.2 μm, T4.7 μm, and RatioT measured in various samples. T4.2 μm and T4.7 μm were calculated from minimum values measured when the sample was sprayed. A hierarchical cluster analysis was performed to classify the samples by calculating the distance between the clusters. The distance can be expressed as
(1)
d(r,s)=2n,ns(nr+ns)x¯r-x¯s2  
where d is the distance between clusters, r and s are objects, x¯r and x¯s are the cluster centers, and nr and ns are the numbers of elements. Air (i.e., free space), water vapor, and CO2 gas clusters were separated from each other, whereas for the lighter and the smoke detector tester spray, a few objects overlapped with each other. This is likely because they share a similar gas composition with butane.

4. Conclusions

We detected and classified aerosol particles and gases employing the NDIR-based dual-channel mid-IR measurement. 4.2 μm and 4.7 μm mid-IR LEDs were used for light sources, which were modulated with 900 Hz and 1,000 Hz, respectively to reduce interference between the light sources and ambient light, and increase SNR. The modulated 4.2 μm and 4.7 μm lights passed through lenses and were acquired by a photodetector, and the transmittances (T4.2 μm, T4.7 μm) were obtained to detect the aerosol particles and gases. The aerosol particles and gases were classified by the distance between clusters of objects via T4.2 μm, T4.7 μm and RatioT. To detect CO and CO2, which are representative gases generated during hydrocarbon combustion, we utilized mid-IR LED light sources in the 4.2 μm and 4.7 μm bands, which can be strongly absorbed by the gases. In an actual fire, a variety of gases are produced depending on the combustible’s chemical properties and combustion state. For example, carbon monoxide, carbon dioxide, hydrogen cyanide, and ammonia are generated in fires caused by general heating materials such as wood and textiles (Class A- general fires); propane gas (Class B- oil fires); hydrogen fluoride, hydrogen chloride, sulfur dioxide, and carbon monoxide in lithium-ion battery fires (Class C- electrical fires); and hydrogen gas (Class D- metal fires). Therefore, to detect the fire type, it is critical to select a light source suitable for gas detection. The acquisition speed is determined by the sampling rate of the photodetector (up to 9 MHz) and the number of data acquired. In this study, data were acquired at a rate of 500 kHz and the aerosol particles and gases were analyzed at 0.25-second intervals using a window size of 250,000 samples and an overlap of 125,000 samples. Propane gas, CO2 gas, and water vapor were classified through a hierarchical cluster analysis of the data expressed as T4.2 μm, T4.7 μm, and RatioT values. By measuring multiple spectral absorbances, analyzing the relationship between absorbances, and applying a machine learning or deep learning-based classification algorithm, it is expected to accurately classify various types of aerosol particles and gases. This can be applied in fire detection to develop a highly useful technique that can classify fire types and distinguish between fires and non-fires. Furthermore, the NDIR-based dual-channel mid-IR method is low-cost and simple and can perform high-speed real-time measurements. Therefore, in addition to fire type detection, it is expected to be actively employed in many applications such as monitoring toxic gases generated in industrial sites.

Notes

Author Contributions

Conceptualization, S.K., S.P. and K.L.; methodology, S.K.; software, S.K.; validation, S.K., S.P. and K.L.; formal analysis, S.K.; investigation, S.P. and K.L.; resources, S.P. and K.L.; data curation, S.K.; writing-original draft preparation, S.K.; writing-review and editing, S.K., S.P. and K.L.; visualization, S.K.; supervision, K.L.; project administration, S.P.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgments

This work was supported by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT)(No. 2020-0-00012, Development of intelligent fire detection equipment based on smoke particle spectrum analysis).

Figure 1.
Configuration of the NDIR-based dual-channel mid-IR measurement.
KIFSE-3d1404d5f1.jpg
Figure 2.
Signal processing to calculate Tλ and RatioT. (a) raw data measured for 0.5 s. (b) frequency signals modulated at 900 Hz and 1,000 Hz in the frequency domain. (c) transmittance and (d) RatioT calculated through short-time Fourier transform at 0.25-second intervals for 2 s.
KIFSE-3d1404d5f2.jpg
Figure 3.
Measured transmittances of inflowing aerosol particles and gases from (a) free space, (b) lighter, (c) smoke detector tester spray, (d) CO2 gas, and (e) water vapor.
KIFSE-3d1404d5f3.jpg
Figure 4.
(a) 3D graph of measured T4.2 µm, T4.7 µm, and RatioT, (b) Dendrogram of hierarchical clustering with the distance.
KIFSE-3d1404d5f4.jpg

References

1. W. Y. Jacob and L. A. Roy, "Non-dispersive Infrared gas measurement", International Frequency Sensor Association Publishing, Tennessee, USA (2012).

2. T. V. Dinh, I. Y. Choi, Y. S. Son and J. C. Kim, “A Review on Non-dispersive Infrared Gas Sensors: Improvement of Sensor Detection Limit and Interference Correction”, Sensors and Actuators B: Chemical, Vol. 231, pp. 529-538 (2016), https://doi.org/10.1016/j.snb.2016.03.040.
crossref
3. S. J. Chen, D. C. Hovde, K. A. Peterson and A. W. Marshall, “Fire Detection Using Smoke and Gas Sensors”, Fire Safety Journal, Vol. 42, No. 8, pp. 507-515 (2007), https://doi.org/10.1016/j.firesaf.2007.01.006.
crossref
4. Q. Tan, L. Tang, M. Yang, C. Xue, W. Zhang, J. Liu and J. Xiong, “Three-gas Detection System with IR Optical Sensor Based on NDIR Technology”, Optics and Lasers in Engineering, Vol. 74, pp. 103-108 (2015), https://doi.org/10.1016/j.optlaseng.2015.05.007.
crossref
5. J. Kwon, G. Ahn, G. Kim, J. C. Kim and H. Kim, "A Study on NDIR-based CO2 Sensor to Apply Remote Air Quality Monitoring System", ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009 Proceedings, pp. 1683-1687 (2009).

6. T. A. Vincent and J. W. Gardner, “A Low Cost MEMS based NDIR System for the Monitoring of Carbon Dioxide in Breath Analysis at ppm Levels”, Sensors and Actuators B: Chemical, Vol. 236, pp. 954-964 (2016), https://doi.org/10.1016/j.snb.2016.04.016.
crossref
7. D. Gutmacher, U. Hoefer and J. Wöllenstein, “Gas Sensor Technologies for Fire Detection”, Sensors and Actuators B: Chemical, Vol. 175, pp. 40-45 (2012), https://doi.org/10.1016/j.snb.2011.11.053.
crossref
8. A. Solórzano, J. Fonollosa, L. Fernández, J. Eichmann, and S. Marco, "Fire Detection Using a Gas Sensor Array with Sensor Fusion Algorithms", 2017 ISOCS/IEEE International Symposium on Olfaction and Electronic Nose, pp. 1-3 (2017). http://doi.org/10.1109/I SOEN.2017.7968889.

9. Y. Li, L. Yu, C. Zheng, Z. Ma, S. Yang, F. Song, K. Zheng, W. Ye and et al, “Development and Field Deployment of a Mid-infrared CO and CO2 Dual-Gas Sensor System for Early Fire Detection and Location”, Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol. 270, pp. 120834(2022), https://doi.org/10.1016/j.saa.2021.120834.
crossref pmid
10. J. Eichmann, L. Fernandez, B. Ziems, J. M. Jiménez-Soto, S. Marco and J. Fonollosa, “Early Fire Detection Based on Gas Sensor Arrays: Multivariate Calibration and Validation”, Sensors and Actuators B: Chemical, Vol. 352, No. 1, pp. 130961(2022), https://doi.org/10.1016/j.snb.2021.130961.
crossref
11. K. Thurmond, Z. Loparo, W. Partridge and S. S. Vasu, “A Light-Emitting Diode-(LED-) Based Absorption Sensor for Simultaneous Detection of Carbon Monoxide and Carbon Dioxide”, Applied Spectroscopy, Vol. 70, No. 6, pp. 962-971 (2016), https://doi.org/10.1177/0003702816641261.
crossref pmid pdf
12. J. Jágerská, B. Tuzson, H. Looser, A. Bismuto, J. Faist, H. Prinz and L. Emmenegger, “Highly Sensitive and Fast Detection of Propane- Butane Using a 3 μm Quantum Cascade Laser”, Applied Optics, Vol. 52, No. 19, pp. 4613-4619 (2013), https://doi.org/10.1364/AO.52.004613.
pmid
13. J. A. Curcio and C. C. Petty, “The Near Infrared Absorption Spectrum of Liquid Water”, Journal of the Optical Society of America, Vol. 41, No. 5, pp. 302-304 (1951), https://doi.org/10.1364/JOSA.41.000302.
crossref
14. W. S. Benedict, H. H. Claassen and J. H. Shaw, “Absorption Spectrum of Water Vapor Between”, Journal of Research of the National Bureau of Standards, Vol. 49, No. 2, pp. 91-95 (1952).

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