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Ash determination of coal flotation concentrate by analyzing froth image using a novel hybrid model based on deep learning algorithms and attention mechanism

发布时间:2023-01-30点击次数:

  • DOI码:10.1016/j.energy.2022.125027
  • 发表刊物:Energy
  • 关键字:Flotation;Ash determination;Deep learning;Convolution neural network;Attention mechanism
  • 摘要:Flotation is an important separation method for coal preparation, where ash content is critical to coal product quality. However, the absence of fast and accurate ash determination of coal flotation concentrate restricts the automation of flotation. Therefore, this paper presents a novel hybrid model, named as convolution-attention parallel network (CAPNet), for rapid and accurate determination of the ash content of coal flotation concentrate by analyzing froth images. First, we construct the CAPNet model by combining the classic CNN model (ResNet) and attention mechanism. Two parts are run in parallel so that they can learn from each other without mutual interference. Second, the hyperparameters of CAPNet are optimized using the orthogonal experimental design (OED) method. Finally, the proposed CAPNet is extensively compared with baseline models. Results show that CAPNet outweighs other methods in terms of accuracy and stability. It can achieve a R2 of 0.926, which is about 5%–10% greater than those of baseline CNN models, and over 30% higher than those of machine learning (ML) methods. As for other metrics, such as MAE, MAPE, RMSE, TIC, MPD, MGD and Var, the proposed CAPNet achieves 10%–50% of improvement compared to CNN models, and 50%–80% of improvement compared to ML methods. Extensive cross-comparison of performance between models clearly indicates that the CAPNet is superior to its competitors for the ash determination of coal flotation concentrate using froth images. Furthermore, CAPNet can also reduce the ash determination time from hours needed by existing standard method to 6 ms, which is ideal for engineering applications. We believe that the application of CAPNet in real production will significantly improve the automation and intelligence level of coal flotation, which can also increase economic benefits.
  • 论文类型:期刊论文
  • 论文编号:125027
  • 卷号:260
  • 是否译文:否
  • 发表时间:2022-12-01
  • 收录刊物:SCI、EI
  • 发布期刊链接:https://www.sciencedirect.com/science/article/pii/S0360544222019247
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    博士生导师
    硕士生导师
  • 教师拼音名称:yuhesheng
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  • 所在单位:化工学院
  • 职务:Professor
  • 办公地点:中国矿业大学文昌校区综合楼203
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  • 学位:博士
  • 职称:教授
  • 毕业院校:加拿大滑铁卢大学

学术荣誉:

  • 2020当选:江苏特聘教授

曾获荣誉:

  • 2020-08-01江苏特聘教授
  • 2014-10-15加拿大国家自然科学基金工业博士后奖学金
  • 2020-08-01中国矿业大学“高端人才计划”攀登学者
  • 2019-06-01江苏省“六大人才高峰”高层次人才
  • 2018-06-28江苏省“双创博士”
  • 2016-08-01AIChE Journal Editor’s Choice Paper

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