Deep learning-based estimation of ash content in coal: Unveiling the contributions of color and texture features

Abstract

Determining coal ash content is paramount when evaluating coal quality and optimizing industrial processes. Conventional methods reliant on manual analysis prove exceedingly time-consuming and labor-intensive. The advent of deep learning has galvanized researchers to explore various models aimed at precision and efficiency in the coal industry. However, the selection of appropriate features assumes a pivotal role in achieving accurate estimation. This study meticulously scrutinizes the importance of color and texture features in estimating coal ash content using neural networks. The proposed framework for elucidating feature contributions encompasses the following steps: (1) A feature disentangled pipeline was employed to generate a color and texture dataset from the original dataset; (2) Harnessing convolutional neural network (CNN), vision Transformers(ViT), a hybrid model combing CNN and ViT, and graph convolution network (GCN) to learning feature representation for texture and color. 3) An interpretable decision module was employed to aggregate these two feature representations, thereby achieving an interpretable estimation of ash content. Experimental results and visualizations demonstrated the substantial importance of color in CNN, accounting for an impressive 64.77%, whereas the texture feature modestly contributed at 35.23%. The analysis of feature contributions assumes a crucial role in guiding the design of accuracy-driven models and in comprehending the inherent contributions or biases within deep learning architectures.