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Electrical cables are a potential source of ignition and fire hazards in various types of buildings and industrial installations, as well as in all modes of transportation, including aircraft. Fires in buildings pose the greatest threat to human life and health. The composition of thermal degradation products depends mainly on the type of combustible materials and the type of combustion process—flaming or smoldering. The purpose of this paper was to determine, based on experimental studies, the effects of flaming and smoldering combustion on the response times of fire smoke detectors. In addition, the concentrations of fire gases formed in the process of duct combustion, including CO, SO2, NO2, NO, HCN, HCl, HBr and HF, were measured using an FTIR spectrometer. The results presented confirm the significant effect of the way the cable samples burned on detector tripping time. The highest concentration of smoke (Y) in the test chamber was obtained during flame combustion. It was further found that the characteristics of the cable insulation material used, such as the type of PVC, had a significant effect on the toxicity of the emitted gases. The largest amounts of toxic gases were emitted during the smoldering combustion of a cable with a plasticized PVC sheath.

Understanding the combustion dynamics of fuels, and the generation and propagation of smoke in a wildland fire, can inform short-range and long-range pollutant transport models, and help address and mitigate air quality concerns in communities. Smoldering smoke can cause health issues in nearby valley bottoms, and can create hazardous road conditions due to low-visibility. We studied near-field smoke dynamics in a prescribed fire of 3.4 hectares of land in a boreal black spruce forest in central Alberta. Smoke generated from the fire was monitored through a network of five field-deployable micro sensor systems. Sensors were placed within 500–1000 m of the fire area at various angles in downwind. Smoke generated from flaming and smoldering combustions showed distinct characteristics. The propagation rates of flaming and smoldering smoke, based on the fine particulate (PM2.5) component, were 0.8 and 0.2 m/s, respectively. The flaming smoke was characterized by sharp rise of PM2.5 in air with concentrations of up to 940 µg/m3, followed by an exponential decay with a half-life of ~10 min. Smoldering combustion related smoke contributed to PM2.5 concentrations above 1000 µg/m3 with slower decay half-life of ~18 min. PM2.5 emissions from the burn area during flaming and smoldering phases, integrated over the combustion duration of 2.5 h, were ~15 and ~16 kilograms, respectively, as estimated by our mass balance model.

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.