Enhancing Accuracy of Flame Equivalence Ratio Measurements: An Attention-Based Convolutional Neural Network Approach for Overcoming Limitations in Traditional Color Modeling

This paper addresses the inherent limitations in traditional color modeling techniques for measuring the flame equivalence ratio (<i>Φ</i>), particularly focusing on the subjectivity involved in threshold settings and the challenges posed by uneven 2D color distribution. To overcome these issues, this study introduces an attention-based convolutional neural network (<i>ACN</i>) model, a novel approach that transcends the conventional reliance on B/G color features (<i>T_f</i>). The <i>ACN</i> model leverages adaptive feature extraction, augmented by a spatial attention mechanism, to more effectively analyze flame images. By amplifying key features, autonomously minimizing background noise, and standardizing variations in color distribution, the ACN model in this experiment achieved a prediction accuracy of 99%, with a 76% reduction in error rate compared to the original model, significantly improving the accuracy and objectivity of flame <i>Φ</i> measurement. This method marks a substantial development in the precision and reliability of flame analysis.