使用机器学习方法预测深井在液压头变动期间的性能
1Selçuk University, Faculty of Agriculture, Department of Agricultural Machinery and Technology Engineering, 42140, Konya, Turkiye.
Heliyon
|June 3, 2024
概括
机器学习使用液压头和运行数据准确预测抽水厂的效率. 人工神经网络在各种条件下估计系统效率方面表现出卓越的性能.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 站的效率对于节能和降低运营成本至关重要.
- 在不同的液压条件下准确预测系统效率对于优化性能至关重要.
- 传统的效率估计方法可能无法有效地捕捉复杂的系统动态.
研究的目的:
- 采用机器学习技术来估计和预测抽厂的系统效率.
- 评估不同机器学习算法的性能,以建模站效率.
- 确定影响系统效率的关键参数及其对预测准确性的影响.
主要方法:
- 使用的测量参数包括流量,输出压力, drawdown 和功率.
- 实施了两种方法:方法I附加参数 (液压头,下拉,流量,功率,输出压力) 和方法II附加液压头,输出压力和功率.
- 应用了七个机器学习算法,包括人工神经网络,支持矢量机器回归和拉索回归.
主要成果:
- 在不同流速下,液压头的减少125厘米将系统效率降低6.45%13.8%.
- 人工神经网络,支持向量机回归和拉索回归在Approach-I中显示出高性能 (R2值高达0.995).
- 人工神经网络在两种方法中表现最好,R2值为0.995和0.996.
结论:
- 机器学习技术,特别是人工神经网络,对于预测站系统效率非常有效.
- 该研究表明了数据驱动方法在优化系统运行和维护方面的潜力.
- 准确的效率预测可以带来显著的节能,并改善站的运营管理.
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