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Determination of the Settling Rate of Clay/Cyanobacterial Floccules
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机器学习框架用于模拟花动力学,使用非侵入性的动态图像分析.

Abayomi O Bankole1, Rodrigo Moruzzi2, Rogerio G Negri3

  • 1Civil and Environmental Engineering Department, Faculty of Engineering, Sao Paulo State University, Bauru 17033-360, Brazil; Water Resources Management and Agrometeorology Department, COLERM, Federal University of Agriculture, Abeokuta, Nigeria.

The Science of the total environment
|November 13, 2023
PubMed
概括
此摘要是机器生成的。

机器学习 (ML) 模型可以预测水处理中的花演变. 长短记忆 (LSTM) 模型在预测长度和数量方面表现出卓越的准确性,提高了水处理的可持续性.

关键词:
在花过程中,花.叶群的长度演变过程机器学习是机器学习.神经网络的神经网络的神经网络智能水处理系统是如何处理水的

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科学领域:

  • 环境工程 环境工程
  • 水处理技术水处理技术
  • 机器学习应用 机器学习应用

背景情况:

  • 优化花流程对于有效和可持续的水处理至关重要.
  • 目前的方法在准确预测群的进化方面面临挑战.
  • 机器学习为提高水处理效率提供了一个潜在的解决方案.

研究的目的:

  • 开发一种机器学习 (ML) 模型,用于预测水处理过程中的花演变.
  • 为大规模水处理系统采用ML制定框架.
  • 为了比较不同的ML模型和传统的时间序列模型的性能,以预测化.

主要方法:

  • 使用非侵入性图像采集对花运动的实验研究.
  • 实现多层感知器 (MLP),长短期记忆 (LSTM) 和自动回归集成移动平均 (ARIMA) 模型.
  • 分析了具有不同速度梯度 (Gf 20 和 60 s-1) 和花时间的批量试验数据.

主要成果:

  • flokculation动力学显示了快速的初始增长,随后是平原.
  • 阿里马模型表现不佳,测试准确度为负.
  • MLP实现了高精度 (R2 0.86-1.0培训,0.92-1.0测试).
  • LSTM模型提供了最好的预测准确度 (R2 0.92-1.00) 并在所有条件下准确预测流量数.

结论:

  • ML模型对花动态敏感,需要仔细选择.
  • 在水处理中,LSTM模型对预测长度和数量非常有效.
  • 开发的ML框架可用于水处理建模,促进智能技术的采用.