Predicting the ash content in coal flotation concentrate based on convolutional neural network

Abstract

ABSTRACT Convolutional neural networks (CNNs) are currently one of the most popular image classification technologies. Their excellent image classification ability enables the prediction of the ash content in clean coal using coal flotation froth images. Herein, a scheme is proposed to predict the ash content in coal flotation concentrate using coal flotation froth images and CNN architecture. According to the ash content of the concentrate, the coal flotation froth image data set is divided into seven interval categories, and data augmentation is used to expand the data set. Then, several recently trained CNNs (ResNet, EfficientNet, EfficientNetV2, ConvNeXt) are used to classify the coal flotation froth images with different concentrate ash content interval (integer ±1%). The classification performance of each network model, relationship between model performance and hyperparameters, and the abstract pixel features of the best model are visualized. The EfficientNetV2-L network achieves the highest classification accuracy (99%) after fine-tuning. The results indicate that the CNNs are effective in predicting the ash content of clean coal by classifying coal flotation froth images. The optimized CNN model is applied to froth images in the industry, resulting in high classification performance, which demonstrates that the CNN has high potential for industrial applications.