Dual-wavelength optical sensor for fire detection and measurement of aerosol mass concentration

Xia, Jingjing; Zhou, Yi; Zeng, Jin

doi:10.1016/j.firesaf.2024.104129

Fire Safety Journal

2024

DOI: 10.1016/j.firesaf.2024.104129

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Dual-wavelength optical sensor for fire detection and measurement of aerosol mass concentration

Jingjing Xia,

Yi Zhou,

Jin Zeng

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2024

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Cited by 2 publications

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Particle Size Prediction Method for Fire or Nuisance Event using a Simple Photoelectric Detector

Özyurt

2024

J. Phys.: Conf. Ser.

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This study demonstrates that a simple smoke detector with minimal components can be used to estimate the particle size of fires or nuisance incidents. Machine learning (ML) models were created using scattered light data. Various test materials such as wood, cotton, polyurethane foam, cigarette, N-heptane, printed circuit board (PCB), paraffin, polyalpha olefin (PAO), di-ethyl hexyl-sebacate (DEHS), plaster powder, and cement dust were used in the experiments. The proposed prediction method was tested against completely unknown particles of cigarette, PAO, PCB, and plaster powder, which were not used in the training. The particle size prediction capability of forward, backward, and side scattering of light at 980 nm was investigated using ML models with time correlation function (TCF) data. The prediction errors of the best ML model for particle median sizes ranged from 0.4% to 35.1%. Traditional simple smoke detectors using the proposed methodologies can measure the median particle size and volume concentration, thereby effectively suppressing false alarms.

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Particle Size Prediction Method for Fire or Nuisance Event using a Simple Photoelectric Detector

Özyurt

2024

J. Phys.: Conf. Ser.

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Experimental Study on Early Fire Smoke Characteristics in a High-Volume Space: A Fire Detection Perspective

Wang,

Zhang,

et al. 2024

Fire

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High-volume space structures are characterized by high combustible loads, rapid fire development, difficulty in firefighting, and potential building collapse risks, making early fire detection particularly crucial. The effectiveness of early fire detection technologies relies on their ability to adapt to the characteristics of smoke-dominant combustion products in the protected space. However, there is a lack of targeted research on the characteristics of the smoke generated during the early low-power stages of fires in high-volume spaces, which has not supported the development of early fire detection technologies for such environments. To address this, this paper presents an experimental study that, for the first time, collects data on fire smoke parameters such as temperature, velocity, concentration, and particle size at heights ranging from 6.5 m to 18.5 m using lightweight sensors. The study analyzes the characteristic and correlations of these parameters and their impact on early fire detection in high-volume spaces for the first time, presenting variation patterns in the plume velocity and particle size distribution of early fire smoke with height. It identifies three patterns of particle size distribution, contrasting with previous studies, and offers a qualitative explanation for these findings. This research enhances the understanding of early fire smoke signals in large spaces and offers valuable insights for developing more accurate and efficient fire detection strategies and technologies.

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Dual-wavelength optical sensor for fire detection and measurement of aerosol mass concentration

Cited by 2 publications

References 12 publications

Particle Size Prediction Method for Fire or Nuisance Event using a Simple Photoelectric Detector

Particle Size Prediction Method for Fire or Nuisance Event using a Simple Photoelectric Detector

Experimental Study on Early Fire Smoke Characteristics in a High-Volume Space: A Fire Detection Perspective

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