scholarly journals Fire Detection Factor of Polyurethane Foam Fire Experiment in UL 268

2021 ◽  
Vol 35 (1) ◽  
pp. 58-66
Author(s):  
Yoo-Jeong Choi ◽  
Su-Gil Choi ◽  
Yeong-Jae Nam ◽  
Si-Kuk Kim
Keyword(s):  
Polyurethane Foam ◽  
Fire Detection ◽  
Combustion Products ◽  
Adaptation Level ◽  
Gas Analyzer ◽  
Combustion Gas ◽  
Black Smoke ◽  
Fire Experiment ◽  
Fire Detector ◽  
Pm 10

This study focused on fire detection factors by confirming the adaptation level of fire detection due to black smoke generated in the case of polyurethane foam fire, and it compared and analyzed the change in combustion products. An experiment was conducted to derive fire detection factors using the UL 268 polyurethane foam real fire test standard. Combustion products were measured using a particulate matter sensor (PMS), combustion gas analyzer (CGA), and gas analyzer (GA). As a result of the experiment, it was confirmed that the adaptability of the existing fire detector deteriorated when black smoke was generated. The fire detection factor was derived using the measured values of combustion products. In particular, among the measured factors, the most adaptable combustion products were found to be PM 2.5, PM 10, NO, and CO. They were considered to improve reliability when used as fire detection factors, as compared to existing fire detectors.

scholarly journals Fire Detection Tendency through Combustion Products Generated during UL 268 Wood Flame Fire and Smoldering Fire Test

2021 ◽  
Vol 35 (1) ◽  
pp. 48-57
Author(s):  
Su-Gil Choi ◽  
Yoo-Jeong Choi ◽  
Yeong-Jae Nam ◽  
Si-Kuk Kim
Keyword(s):  
Fire Detection ◽  
Combustion Products ◽  
Smoke Detector ◽  
Gas Analyzer ◽  
Continuous Increase ◽  
Combustion Gas ◽  
Particle Matter ◽  
Smoke Generation ◽  
Wood Fire ◽  
Pm 10

This experiment analyzes the tendency of fire detection through combustion products generated during UL 268 wood flame fires and smoldering tests. Fire detection tendency was measured using a particle matter sencor (PMS), combustion gas analyzer (CGA), and gas analyzer (GA). The combustion products were matched and analyzed at 5 %/m (non-operation), 10 %/m, and 15 %/m of the smoke sensitivity measured by the smoke detector. In the case of wood flaming fire, PMS PM 10, CGA CO, SO<sub>2</sub>, GA HCHO, and TVOC, the trend was observed because of the continuous increase in the measured value according to the smoke generation. In the case of smoldering, PM 10, CO, and HCHO were adaptable to the tendency to be observed. Finally, in the case of wood fire accompanied by flame fire and smoldering to PM 10, CO and HCHO were considered to be the optimal fire detection factors.

scholarly journals A Study on the Response Characteristics of Fire Detector and Indoor Air Quality Measurement Factor According to UL 268 Cooking Nuisance Test

2021 ◽  
Vol 35 (1) ◽  
pp. 67-77
Author(s):  
Yoo-Jeong Choi ◽  
Su-Gil Choi ◽  
Yeong-Jae Nam ◽  
Jae-Hyeon Yu ◽  
Euy-Hong Hwang ◽  
...  
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Quality Measurement ◽  
Gas Analyzer ◽  
Combustion Gas ◽  
Pm 2.5 ◽  
Response Characteristics ◽  
Fire Detector ◽  
Pm 10

The response characteristics of fire detectors and indoor air quality measurement factors were studied according to the UL 268 cooking nuisance test. The response characteristics of the fire detector were confirmed through the U.S. UL 268 cooking nuisance test, newly introduced in 2020; the smoke concentration in the test was found to be a maximum of 7.8 %/m. The response characteristics of the indoor air quality measurement factors matched to the smoke detector's concentration in the nonoperation test (5 %/m), type 1 smoke detector's concentration in the operation test (7.5 %/m), and maximum smoke concentration (7.8 %/m) measured in the test were analyzed to derive factors applicable to avoiding unwanted fire alarms. In this test, the variation was confirmed at PM 1.0, PM 2.5, and PM 10 of the fine dust sensor, for NO and SO<sub>2</sub> for the combustion gas analyzer, and for CO, CO<sub>2</sub>, and HCHO for the gas analyzer. In particular, the most-adaptable factors measured in this experiment were PM 2.5 and PM 10, which can be utilized as unwanted-alarm factors.

scholarly journals Manage the Selectivity of the Analysis

2019 ◽  
pp. 62-65
Author(s):  
V. Kozubovskyy
Keyword(s):  
Total Concentration ◽  
Optical Path ◽  
Gas Analyzer ◽  
Combustion Gas ◽  
Signal Value ◽  
Useful Signal ◽  
Combustible Gases

In the traditional analyzers of the total concentration of combustible gases (a mixture of concentrations of combustible gases), the dependence of the signal value for each component of the mixture is determined by from their concentrations. This signal should be enclosed in the range from 0.5% LEL to 50% of the LEL. The calibration of this analyzer is carried out on some gas that lies within the this range. Usually methane or propane is used for this purpose [1,2]. In the article the possibility of controlling the selectivity of the analysis of gas components by the methods of negative filtration [3] of the useful signal in the optical path of the gas analyzer is considered. There are many analytical tasks for which it is necessary to achieve the same sensitivity to the measured gas components. For example, in the analyzer mentioned above, the total concentration of combustible gases in the air. Indeed, different combustible gases can lead to similar effects and it is not possible to determine the concentration of each. The most common are flammable gases such as methane, butane, propane. Of course, it is desirable that the combustion gas alarm has the same sensitivity to these gases and does not respond to interfering, such as CO2.

scholarly journals Alat Pendeteksi Kebakaran Menggunakan SMS

2020 ◽  
Vol 1 (2) ◽  
pp. 251-254
Author(s):  
Sindi Permata Sari ◽  
Oriza Candra ◽  
Jhefri Asmi
Keyword(s):  
Human Factors ◽  
Temperature Sensor ◽  
Human Life ◽  
Fire Detection ◽  
Smoke Detectors ◽  
Fire Detector ◽  
Arduino Uno ◽  
Main Component ◽  
The Brain ◽  
Early Fire Detection

Lately, there are frequent fires caused by human factors. Because we cannot predict the process of fire in advance. And the delay in knowing the occurrence of a fire is very fatal to the safety of human life and property. With advances in technology, we can overcome fires by making early fire detection devices. With the presence of temperature and smoke detectors, we can detect fires as early as possible and be delivered quickly via alarms and SMS gateways. The main component of this fire detector is the Arduino Uno. This Arduino uno acts as the brain of the fire detection device. This tool works based on the detection of the temperature condition by the DHT11 temperature sensor, which is when the temperature is above normal, an alert notification will be sent via the SMS gateway and so will the MQ2 smoke and the buzzer will sound as a warning alarm.

Development of a multiple gas analyzer using cavity ringdown spectroscopy for use in advanced fire detection

2009 ◽  
Vol 48 (4) ◽  
pp. 695 ◽  
Author(s):  
Eric A. Fallows ◽  
Thomas G. Cleary ◽  
J. Houston Miller
Keyword(s):  
Fire Detection ◽  
Cavity Ringdown Spectroscopy ◽  
Gas Analyzer ◽  
Cavity Ringdown

scholarly journals FIRE DETECTOR SYSTEM WITH WIRELESS COMMUNICATION FOR DOMESTIC USE

2020 ◽  
Vol 3 (1) ◽  
pp. 44-55
Author(s):  
Wongalethu Kaptein ◽  
Ali El-fallah ◽  
Ali M Almaktoof
Keyword(s):  
Mobile Communications ◽  
Fire Detection ◽  
Short Message ◽  
Wireless System ◽  
Residential Community ◽  
Alert Message ◽  
Domestic Use ◽  
Fire Detector ◽  
House Fires ◽  
Human Death

The surveillance of home places through sensors and the prevention of problems via prediction are of vital importance for the safety of these areas. Fires usually occur in homes due to inattention and changes in environmental conditions. They cause threats to the residential community and may lead to human death and property damage. Thus, house fires must be detected as early as possible to prevent these types of threats. This study provides a simple design for a wireless fire detection network system. This system is meant to notify the household of a fire occurrence in the house by sending a short message through the Global System for Mobile Communications (GSM). In order to pre-monitor the possibility of a fire, the Arduino programmed board receives temperatures and humidity levels from sensors. When it detects that the collected data exceeds a predefined and precalculated threshold, it promptly communicates with the wireless system and sends an alert message to the user through the GSM.

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