基于科里奥利斯流量计和神经网络的煤炭水泥固体液体双相流量测量的研究
Jie Liu1,2, Lingfei Kong1, Jiahao Ma1
1School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
这项研究开发了一种深度学习校正框架,用于测量煤水泥 (CWS) 的科里奥利斯质流量计. 优化的模型显著减少了液体-固体双相流程的测量误差,提高了工业应用的精度.
科学领域:
- 工程 工程师 工程师 工程师
- 流量测量 流量测量
- 人工智能的人工智能
背景情况:
- 煤水泥 (CWS) 是一种正在发展中的以煤为基础的化学产品,具有潜在的石油替代品.
- 科里奥利斯质流量计在单相流量中提供了高精度,但在多相场景中存在困难.
- 在CWS多相流量测量的重大错误需要改进技术.
研究的目的:
- 调查CWS液体-固体双相流程中的科里奥利斯质流量计错误.
- 开发一个深度学习校正框架来缓解这些错误.
- 提高测量精度,为工业过程控制提供可靠的数据.
主要方法:
- 设计和建造了一个CWS液体-固体双相流量测量平台.
- 开发了一个使用BP神经网络的深度学习校正框架.
- 建立了两个校正模型,包括一个算法优化的版本.
主要成果:
- 最初的BP神经网络模型将预测误差从5.11%降低到3.98%.
- 算法优化的模型进一步降低了预测误差到1.01%.
- 在CWS流量测量准确度方面显著改进.
结论:
- 深度学习框架为Coriolis质流量计在液体-固体两相流程中提供了一种新的技术方法.
- 优化的模型提高了测量准确性,降低了成本,并支持工业过程控制.
- 这项研究为更可靠的CWS流量数据提供了途径.
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