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New insights into the ignition characteristics of liquid fuels on hot surfaces based on TG-FTIR

Published

Author(s)

Jian Chen, Zhenghui Wang, Yanni Zhang, Yang Li, Wai Cheong Tam, Depeng Kong, Jun Deng

Abstract

There are many potential hazards related with hot surface in industrial processes. Therefore, the ignition characteristics of liquid fuels on hot surfaces play an important role for fire safety engineering involved with energy utilization. In this study, thermogravimetric analysis and hot surface tests were systematically conducted for some typical liquid fuels. In the hot surface tests, ignition parameters were measured and investigated, including ignition probability and characteristic ignition temperature. It was found that the ignition of liquid fuel on the hot surface was probabilistic. The boil-over phenomenon was observed for transformer oil when the hot surface temperature was above 693 K, where the liquid fuel burned over the pan. For the three liquid fuel selected in our studies, the order of lowest ignition temperature was not consistent with the order of the kinetic parameter. Furthermore, the comparative analysis revealed that the lowest ignition temperatures with ignition probability of 5 % were observed to be within the temperature ranges of combustible gases generation, indicating that the ignition of the liquid fuels on the hot surface was significantly influenced by the combustible gas generated during the evaporation and thermal decomposition. Finally, the model describing the profiles for the concentration and temperature of combustible vapor was introduced to provide a detail explanation for the ignition mechanism of liquid fuels on hot surfaces. The established model could provide scientific basis to the fire risk assessment for liquid fuel fire caused by the hot surface, and further optimize the safe usage of liquid fuels. There are three primary challenges: 1) the lack of full-scale data from flashover fire events. The development of a machine learning model demands a large amount of training data; 2) the lack of an accurate and numerically efficient model for real-time prediction. Traditional fire simulation approaches require lengthy computational time and resources; 3) account for realistic sensor conditions. Fire protection devices have maximum operational temperature limits. However, existing prediction models assume to have sensor information at 400 °C or above; and 4) the need for model testing against real fire scenarios. In order to ensure the reliability and performance, the machine learning model needs to be tested rigorously using various full-scale experimental data. This presentation will address these three challenges. Specifically, we will introduce CData – CFAST Fire Data Generator . We will provide overview on how we can obtain training data for a wide range of different fire and interior door opening conditions. In order to achieve robust model performance and to overcome the model challenges from realistic sensor conditions, we will present a state-of-the-art machine learning model. Our results demonstrate that the model can provide reliable flashover predictions 60 s before the occurrence using temperature information up to 150 °C. For model testing, we will outline our strategies for the implementation and the assessment of the machine learning based model in a series of full scale experiments at NIST. It is believed that the research advances from this work will enhance firefighters' situational awareness in the fire scene, protecting them from hazardous fire environments. It also paves the way to data-driven intelligent firefighting using existing fire protection systems. Topic in Detection and Signaling: Advancements in detection and signaling technology
Citation
Applied Energy

Keywords

Hot surface, Liquid fuels, TG-FTIR analysis, Ignition probability

Citation

Chen, J. , Wang, Z. , Zhang, Y. , Li, Y. , Tam, W. , Kong, D. and Deng, J. (2024), New insights into the ignition characteristics of liquid fuels on hot surfaces based on TG-FTIR, Applied Energy, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935284 (Accessed April 23, 2024)
Created February 1, 2024, Updated March 1, 2024