Novel method to automatize flash point detection in small volumes of liquid by computer vision using thermal images

• Novel methodology for flash point detection of 15 to 300 μL of liquid. • Closed-cup flash point apparatus combined with thermal imaging camera. • Use of computer vision for flash point detection. • Flash point detection at 95 % accuracy. A novel methodology to automate flash point detection of small solvent volumes has been successfully demonstrated and optimized. Flash point temperatures were measured using a closed-cup rapid flash point tester which was paired with a thermal imaging camera. The thermal imaging camara analyses the apparent temperature of the flame before, during and after the opening of the chamber. Two flash point standards and n-eicosane were used to validate the apparatus. Sample volume ranged between 15 and 300 μL which is of interest for the analysis of expensive or harmful liquids. This approach could eventually be extended to measuring flash points in gel polymer electrolytes, for which flammability testing is of significant interest for battery R&D. In this work, 462 flashpoints were collected to feed the machine learning algorithms. Convolutional neural network, support vector machine and random forest algorithms were used to determine the presence/absence of a flame. Flash points were predicted with an accuracy of 95 % and a precision of ±2 °C. Precision was found to be limited by the flash point detector rather than the analysis by computer vision. Other factors such as humidity (22 % to 55 %), atmospheric pressure (between 99.6 to 102.0 kPa) and volume of solvent were found to have little to no influence on flash point detection. The flash point temperatures tested in this study are limited to a range between 50 °C and 176 °C. Regression algorithms were employed to estimate the flash point temperature based on a single measurement presenting an improvement as accurate flash point detection traditionally requires several measurements.