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使用深度神经网络模型预测铁基过器从地下水中去除

Muhammad Ali Martuza¹, Md Shafiquzzaman², Husnain Haider³

¹Department of Computer Engineering, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia.

Scientific reports

|November 3, 2024

概括

深度学习神经网络模型被开发用于预测使用铁基过器从饮用水中去除. 这种方法优化了污染物去除,提高了水的安全性和处理效率.

科学领域:

环境科学环境科学
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:

饮用水中的污染对环境和健康构成重大风险.
基于铁的过器提供了一种具有成本效益和可持续的去除方法.
关于使用机器学习 (ML) 来优化使用这些过器去除的研究有限.

研究的目的:

开发深度学习神经网络 (DLNN) 模型,以预测和其他污染物被铁基过器去除.
调查ML在优化水处理过程中的有效性.
确定影响污染物清除效率的关键地下水参数.

主要方法:

开发了DLNN模型,使用20个现场性能点的数据集,通过立方分线插入扩展到1600个数据点.
采用贝叶斯优化用于超参数调整和分层五倍交叉验证用于模型训练.
利用Sobol灵敏度分析来确定各种参数对污染物去除的影响.

主要成果:

DLNN模型实现了高预测准确性 (的R2为0.990-0.999).
对于铁 (0.774-0.976), (0.934-0.954) 和 (0.878-0.998) 也获得了准确的预测.
,,Eh和温度被确定为污染物去除最敏感的参数.

结论:

开发的DLNN模型为估计从地下水中去除的最佳条件提供了一个强大的工具.
这种以数据为导向的方法可以在设计和运行有效的水处理系统方面发挥重要作用.
该研究强调了ML在推进可持续净水策略方面的潜力.

关键词:

亚是一种.人工智能的人工智能是人工智能.深度学习神经网络深度学习神经网络地下水的地下水.铁基过器是以铁为基础的过器.污染物去除污染物去除

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