Development of a Detection Algorithm for Kitchen Cooktop Ignition Prevention

Published

September 30, 2018

Author(s)

Amy Mensch, Anthony Hamins, Kathryn Markell

Abstract

A small number of previous studies focused on cooktop fire sources and considered multi-detector sensing of pre-ignition signatures in a kitchen environment. Johnsson conducted a series of experiments investigating the feasibility of distinguishing between normal cooking activities and preignition conditions using a variety of sensors in a mock kitchen [5] with a closed door. Sensors were placed above the cooktop and on the compartment ceiling. Signals from alcohol, CO, and hydrocarbon sensors showed potential to predict ignition while discriminating from normal cooking [6]. Nearly all the experiments were conducted with the range hood off and the effects of room configuration and transport likely played a significant role in the interpretation of results. Jain et al. conducted cooking oil autoignition experiments, considering the effectiveness of various inexpensive sensors to detect pre-ignition conditions [7]. Jain et al. reported that the rate of change of the moving average of the CO concentration was a robust indicator of impending ignition. The study, however, did not consider normal cooking or common nuisance sources. The objective of this study was to determine which sensors/sensor combinations showed potential for use as detection algorithm for cooktop ignition prevention. The initial set of experiments were focused on sensor response and were designed to avoid transport considerations.

Proceedings Title

SUPDET 2018

Conference Dates

September 11-13, 2018

Conference Location

Cary, NC, US

Conference Title

Suppression, Detection and Signaling Research and Applications Conference

Pub Type

Conferences

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Keywords

ignition prevention, cooktop fires, detection algorithm

Citation

Mensch, A. , Hamins, A. and Markell, K. (2018), Development of a Detection Algorithm for Kitchen Cooktop Ignition Prevention, SUPDET 2018, Cary, NC, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926583 (Accessed December 7, 2024)