通过喷射成像和深度学习实时监测污泥湿度
Environmental science and ecotechnology
|September 25, 2025
概括
使用高速成像和深度学习来分析喷气特性,可以实现精确的污泥湿度监测. 这种快速的非牛顿式流体分析为废弃活性污泥管理提供了具有成本效益的解决方案.
科学领域:
- 环境工程 环境工程
- 流体力学 流体力学 流体力学
- 人工智能的人工智能
背景情况:
- 废物活性污泥由于含水量高,因此存在重大处置和资源回收挑战.
- 精确的湿度监测对于优化污泥处理过程,如无氧消化和堆肥至关重要.
- 现有的湿度测量方法通常是缓慢的,昂贵的,或缺乏实时功能.
研究的目的:
- 开发一种快速而准确的方法来确定污泥的水分含量.
- 为了利用污泥喷流的非牛顿流体特性进行湿度分析.
- 应用深度学习技术进行实时湿度预测.
主要方法:
- 开发了一种瞬间捕获活性污泥喷气膨胀图像 (iCASJEI) 系统.
- 获取了超过11000个污泥喷射图像,覆盖了各种湿度范围 (79-94%).
- 用于图像分析的卷积神经网络 (VGG-16,AlexNet,LeNet) 的训练和优化.
主要成果:
- 该iCASJEI系统与深度学习相结合,在20秒内准确预测了污泥含水量.
- 在2%的精度下,VGG-16实现了93.5%的验证精度,在1%的精度下达到87.6%.
- 该方法有效地捕获了用于湿度预测的非牛顿式流体特性.
结论:
- 对污泥喷射特征的深度学习分析提供了一种显著更快的方法来确定水分含量.
- iCASJEI方法为传统和新兴的湿度测量技术提供了具有成本效益和准确性的替代方案.
- 这种方法有可能在各种行业中实时检测其他粘性,非牛顿流体中的水分.
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