工业4.0中的管道阻力损失计算:基于TransKAN和生成AI的创新框架
Qinyu Zhang1, Huiying Liu1, Zhike Liu2
1College of Science, North China University of Science and Technology, Tangshan 063210, China.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
本研究介绍了一种AI框架,用于准确预测回填采矿中的管道阻力损失,这对于减少能源消耗和提高效率至关重要. 与传统方法相比,新型的TransKAN模型提供了更高的准确性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 在回填采矿中优化管道运输对于深度矿产开采效率至关重要.
- 由于矿井的变化,精确计算管道阻力损失是具有挑战性的,影响工艺效率.
- 工业4.0为采矿业务的智能优化提供数据驱动的解决方案.
研究的目的:
- 开发一个新的管道阻力损失预测框架,用于回填采矿.
- 提高阻力损失计算的准确性,从而减少能量损失和改善填充效果.
- 为此目的,利用生成性人工智能和先进的神经网络架构.
主要方法:
- 整合生成型人工智能,用于创建物理约束的增强数据.
- 使用KAN (科尔摩戈罗夫-阿诺德网络) 模型与B-spline基础函数用于非线性特征提取.
- 应用变压器架构来捕捉管道压力数据中的时空相关性.
主要成果:
- 拟议的TransKAN模型实现了0.9644.4的高R平方值.
- 该模型表现出卓越的性能,RMSE为0.7126和MAE为0.4703.
- 经验验证使用管道压力传感器的实验数据证实了模型的准确性.
结论:
- 这种由人工智能驱动的新型框架提供了一种精确的方法来计算回填采矿中的管道阻力损失.
- TransKAN模型显著优于传统方法和现有的机器学习模型.
- 这一进步支持在深度采矿中智能优化能源消耗和运营效率.
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