多模式机器学习用于Cr(VI) 移除和使用基于图像的流体特征和操作参数进行流体沉降
Yaqi Zhu1, Anlei Wei2, Jirui Zou1
1Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China.
Journal of environmental management
|February 20, 2026
概括
这项研究引入了一个新的AI框架,以改善利用电凝水从废水中去除. 该模型准确预测性能,适应电凝与不断变化的条件,以更好地处理废水.
科学领域:
- 环境工程 环境工程
- 水处理技术水处理技术
- 环境科学中的人工智能
背景情况:
- 电凝对于六价 (Cr(VI)) 的去除是有效的,但它与变化的废水条件作斗争.
- 诸如pH值,电解质度和速率等因素会影响花的形成和沉,限制工艺效率.
研究的目的:
- 开发一种多式机器学习框架,用于预测在电凝过程中Cr(VI) 移除和花沉积的情况.
- 提高电凝过程在不同操作条件下的适应性和准确性.
主要方法:
- 利用深度学习 (ResNet50) 来提取基于图像的流体特征.
- 将这些特征与操作参数集成到分类回归和直接回归模型中.
- 采用机器学习算法,包括支持矢量机器,包装分类器和额外树.
主要成果:
- 综合图像特征和操作参数的多式联接方法显著提高了预测准确性.
- 直接回归模型实现了高的R2值:0.971的Cr(VI) 移除和0.986的花沉.
- 与传统方法相比,该框架在预测电凝效率方面表现优越.
结论:
- 开发的多式联机机器学习框架为优化电凝提供了一个强大的解决方案.
- 这种方法提高了预测准确性和流程适应性,以便在各种条件下有效处理废水.
- 开创了基于深度学习的图像分析与操作参数的使用,推动了废水处理优化.
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