# Accurate ignition detection of solid fuel particles using machine learning

arXiv: https://arxiv.org/abs/2305.00004 · HTML: https://mlsanity.com/paper/2305.00004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Using machine learning to construct velocity fields from OH-PLIF images](https://mlsanity.com/paper/1909.13669/) (2019)

## Abstract (arXiv)

In the present work, accurate determination of single-particle ignition is focused on using high-speed optical diagnostics combined with machine learning approaches. Ignition of individual particles in a laminar flow reactor are visualized by simultaneous 10 kHz OH-LIF and DBI measurements. Two coal particle sizes of 90-125μm and 160-200μm are investigated in conventional air and oxy-fuel conditions with increasing oxygen concentrations. Ignition delay times are first evaluated with threshold methods, revealing obvious deviations compared to the ground truth detected by the human eye. Then, residual networks (ResNet) and feature pyramidal networks (FPN) are trained on the ground truth and applied to predict the ignition time.~Both networks are capable of detecting ignition with significantly higher accuracy and precision. Besides, influences of input data and depth of networks on the prediction performance of a trained model are examined.~The current study shows that the hierarchical feature extraction of the convolutions networks clearly facilitates data evaluation for high-speed optical measurements and could be transferred to other solid fuel experiments with similar boundary conditions.