基于多模型的煤炭水介质分离效应预测研究
Peng Chen1,2,3, Chengyong Wang1,3, Shiwei Wang1,3
1School of Mining and Mechanical Engineering, Liupanshui Normal University, Liupanshui 553004, China.
Heliyon
|May 21, 2024
概括
一种新的多模型 (MM) 方法准确地预测了煤水介质分类的分区系数 (PC). 这种方法可以改进单个模型,提高清洁煤的利用率和生产效率.
科学领域:
- * 矿物加工 矿物加工
- * 煤炭制备工程 煤炭制备工程
- * 计算化学 计算机化学
背景情况:
- *精确的分割系数 (PC) 对于优化煤炭水介质分类效率和清洁煤炭利用至关重要.
- * 现有的PC预测单个模型在准确度上有局限性.
- *原煤分离需要精确的方法,以确保有效的资源管理.
研究的目的:
- * 开发和验证一种多模型 (MM) 计算方法,用于预测煤水介质分类中的分区系数 (PC).
- * 与传统单个模型相比,提高PC预测的准确性.
- *为调节分拣密度和提高生产效率提供更可靠的工具.
主要方法:
- * 开发一种多模型 (MM) 计算方法,集成戈珀茨模型 (GM),物流模型 (LM),Arctangent模型 (AM) 和近似公式 (AFM).
- *使用四组煤样和两种特殊情况验证MM方法.
- *使用统计指标 (E,R2,F值) 对个人模型进行MM预测的比较分析.
主要成果:
- *MM方法以最小的误差 (E:0.91-8.84) 和高相关性 (R2:0.9648-0.9994) 显示出卓越的准确性.
- 在所有测试模型中,MM获得了最高的显著性 (F值:199.17-11352.31).
- *MM对分离密度的预测与单个模型相比,更接近清洗煤灰的实际值.
结论:
- * 拟议的多模型 (MM) 方法显著提高了煤水介质分类中的分区系数预测的准确性.
- *MM被认为是最适合各种煤炭水介质分离工艺的模型.
- *MM方法提供了一种预测PC和优化分类密度的途径,以提高生产效率.
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