Scholarlink

<p>Calorific value is one of the most important properties of coal. Machine learning (ML) can be used in the prediction of calorific value to reduce experimental costs. China is one of the world’s largest coal production countries and coal occupies an important position in its national energy structure. However, ML models with a large database for the overall regions of China are still missing. Based on the extensive coal gasification practices in East China University of Science and Technology, we have built ML models with a large database for overall regions of China. An AutoML model was proposed and achieved a minimum MSE of 1.021. SHAP method was used to increase the model interpretability, and model validity was proved with literature data and additional in-house experiments. The model adaptability was discussed based on the databases of China and USA, showing that geography-specific ML models are essential. This study integrated a large coal database and AutoML method for accurate calorific value prediction and could offer key tools for Chinese coal industry.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AutoML for calorific value prediction using a large database from the coal gasification practices in China

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

查看全文
  • Yuchao Guo,
  • Xia Liu,
  • Yunfei Gao,
  • Xiaoyu Wang,
  • Lu Ding,
  • Weitong Pan,
  • Cheng Hua,
  • Yulian He,
  • Xueli Chen,
  • Zhenghua Dai,
  • Guangsuo Yu,
  • Fuchen Wang

摘要

Calorific value is one of the most important properties of coal. Machine learning (ML) can be used in the prediction of calorific value to reduce experimental costs. China is one of the world’s largest coal production countries and coal occupies an important position in its national energy structure. However, ML models with a large database for the overall regions of China are still missing. Based on the extensive coal gasification practices in East China University of Science and Technology, we have built ML models with a large database for overall regions of China. An AutoML model was proposed and achieved a minimum MSE of 1.021. SHAP method was used to increase the model interpretability, and model validity was proved with literature data and additional in-house experiments. The model adaptability was discussed based on the databases of China and USA, showing that geography-specific ML models are essential. This study integrated a large coal database and AutoML method for accurate calorific value prediction and could offer key tools for Chinese coal industry.