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一个先前知识增强的变压器模型用于工业园区废水处理厂的数据异常识别和处理
Wen-Chao Li1, Hui-Min Li1, Zhi-Wei Guo2
1Key Laboratory of the Three Gorges Reservoir Region's Eco-Environments of MOE, College of Environment and Ecology, Chongqing University, Chongqing 400045, China.
Water research
|December 13, 2025
概括
本研究介绍了一种用于工业废水处理厂 (WWTP) 的新型先前知识增强型变压器 (PK-T) 模型. PK-T模型显著改善了异常识别和数据重建,以实现智能工厂运行.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 工业园区的废水处理厂 (WWTP) 需要精确的异常识别和数据重建,以实现智能操作.
- 传统的方法在WWTP数据中的复杂的时空异常中扎.
研究的目的:
- 引入一种新的先前知识增强型变压器 (PK-T) 模型,用于高精度异常识别和数据重建WWTP.
- 在深度学习框架内利用特定领域的废水处理知识.
主要方法:
- 开发和应用预先知识增强型变压器 (PK-T) 模型.
- 评估使用来自中国两个工业园区的WWTP的一年运营数据.
- 对统计,基于距离和基于模型的方法进行比较分析.
主要成果:
- 与传统方法相比,PK-T模型在识别和重建复杂异常方面表现出卓越的性能.
- 对于关键的水质参数,重建精度超过了90%.
- 观察到异常识别准确度和数据重建精度的显著改善.
结论:
- PK-T模型提供了一个创新的解决方案,用于工业废水处理中的可靠数据管理.
- 这项研究为通过智能数据处理来提高WWTP的运营效率提供了实际意义.
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