基于深度学习的多参数合补偿算法用于接式气体计量系统
Lina Niu1,2, Guanglei Yang2, Jingning Li3
1School of Quality and Technical Supervision, Hebei University, Baoding, 071002, Hebei, China.
Scientific reports
|November 26, 2025
概括
一个新的深度学习算法通过补偿温度,压力和密度变化来提高紧气体计量精度. 这种先进的补偿可以提高工业环境中的测量精度.
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
- 物理 物理学 物理
背景情况:
- 紧式气体计量系统由于温度,压力和密度之间的复杂相互依赖,因此面临重大准确性挑战.
- 传统的补偿方法与非线性合效应作斗争,导致动态工业环境中的错误.
研究的目的:
- 开发一种新的深度学习算法,用于在接式气体计量中进行多参数合补偿.
- 通过有效建模非线性参数相互依赖来提高测量准确性.
主要方法:
- 采用了混合长短期记忆 (LSTM) 和卷积神经网络 (CNN) 架构.
- 纳入了实时自适应性校正机制,包括滑动窗处理和动态重量调整.
- 该算法同时模拟多参数数据中的时间依赖性和空间关系.
主要成果:
- 达到0.52%的平均测量误差,比传统的线性补偿 (误差为2.45%) 提高了78%.
- 经过720小时的连续运行证明了长期稳定性.
- 实时处理能力为5.34毫秒,适用于工业应用.
结论:
- 拟议的深度学习算法显著提高了紧式气体计量的准确性和可靠性.
- 该方法为复杂的工业条件下高精度气流量测量提供了实用解决方案.
- 该研究通过先进的AI驱动补偿来推进精确气体流量测量技术.
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