Investigating the Influence of Froth Image Attributes on Clean Coal Ash Content: A Novel Hybrid Model Employing Deep Learning and Computer Vision Techniques for Prediction Exploration

Abstract

In froth flotation, one of the pivotal metrics employed to evaluate the flotation efficacy is the clean ash content, given its widely acknowledged status as a paramount gauge of coal quality. Leveraging deep learning and computer vision, our study achieved the dynamic recognition of coal flotation froth, a key element for predicting and controlling the ash content in coal concentrate. A comprehensive dataset, assembled from 90 froth flotation videos, provided 16,200 images for analysis. These images revealed key froth characteristics including bubble diameter, quantity, brightness, and bursting rate. We employed Keras to build a comprehensive deep neural network model, incorporating multiple features and mixed data inputs, and subsequently trained it with a rigorous 10-fold cross-validation strategy. Our model was evaluated using robust metrics including the mean squared error, mean absolute error, and root mean squared error, demonstrating a high precision with respective values of 0.003017%, 0.053385%, and 0.042640%. With this innovative approach, our work significantly enhances the accuracy of ash content prediction and provides an important breakthrough for the intelligent advancement and efficiency of froth flotation processes in the coal industry.

Keywords

• deep learning
• computer vision
• froth flotation
• static features
• clean coal ash content