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基于混合物理信息健康指标的性水电解剂的智能状态监测框架

Jie Liu¹, Zhiying Wang¹, Tingting Ma¹

¹Xinjiang Chemical Engineering Design & Research Institute Co., Ltd., Urumqi 830010, China.

Sensors (Basel, Switzerland)

|February 27, 2026

概括

本研究介绍了一种人工智能框架,用于监测用于绿色生产的性水电解剂 (AWE). 它使用基于物理的机器学习来创建健康指标,提高运营安全和效率.

科学领域:

电化学工程电化学工程
人工智能的人工智能
可再生能源系统可再生能源系统

背景情况:

性水电解剂 (AWE) 对于绿色的生产至关重要.
运营风险来自不稳定的可再生能源,影响了AWE的稳定性.
监控AWE的内部状态是确保可靠运行的挑战.

研究的目的:

为AWE开发一个智能状态监控框架.
解决AWE中不可访问的内部状态的挑战.
提高绿色生产的安全性和效率.

主要方法:

采用了混合物理知情机器学习 (ML) 方法.
开发并验证了一种高保真度计算流体动力学 (CFD) 模型.
该CFD模型生成了用于训练ML模型的多物理模拟数据.

主要成果:

八个关键的操作参数被导出为健康指标 (HI).
训练了三个ML模型,并对健康状况分类进行了基准测试.
多层感知器 (MLP) 模型实现了90.43%的准确性.

结论:

拟议的框架使AWE可靠的HI构建成为可能.
人工智能增强的条件监测是提高AWE安全性和效率的可行途径.
这种方法支持绿色生产技术的发展.

关键词:

性水电解仪性水电解仪计算流体动力学的流体动力学.状态监控状态监控状态监控健康指标健康指标智能感知智能感知智能感知机器学习是机器学习.预测性维护是指预测性维护.

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