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人工智能 (AI) 在饮用水处理过程中的应用:可能性

Shakhawat Chowdhury¹, Tanju Karanfil²

¹Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia; IRC for Concrete and Building Materials, King Fahd University of Petroleum & Minerals, Saudi Arabia.

|April 12, 2024

概括

人工智能 (AI) 和机器学习 (ML) 通过提高决策和运营效率来增强水处理过程 (WTP). 深度学习和混合人工智能技术对先进的WTP控制和优化有很大的希望.

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:

人工智能 (AI) 和机器学习 (ML) 在水处理过程中越来越多地用于决策,过程控制,优化和成本管理.
自1997年以来审查了91篇文章,发现AI在凝血/花皮,膜过,消毒副产品 (DBPs) 形成,吸附和WTP中的运营管理中的应用.

研究的目的:

评估人工智能技术在WTP中的发展和应用.
确定水处理人工智能应用的局限性和改进领域.
突出未来的研究方向,以加强WTP控制.

主要方法:

系统审查自1997年以来发表的91篇同行评审文章,这些文章是关于人工智能在WTP中的应用.
对特定WTP过程应用的AI技术的分析,包括凝血/花,膜过,DBPs形成和吸附.
评估不同人工智能方法的性能和能力,包括深度学习和混合技术.

主要成果:

人工智能技术已经提高了对凝固剂剂量,膜流量,排斥,污染,DBP形成和污染物去除在WTP中的预测能力.
深度学习 (DL) 展示了强大的特征提取和数据挖掘能力,使图像识别框架能够用于分析和凝固剂剂量预测.
混合人工智能技术,将回归或物理/运动模型与人工智能结合起来,表现出优越的预测性能.

结论:

人工智能,特别是ML和DL,在优化和控制水处理过程方面取得了重大进展.
未来的研究应该专注于改进人工智能技术,包括混合模型和基于DL的图像识别,以实现更有效的WTP管理.
持续开发人工智能应用对于应对挑战和提高WTP的效率和预测准确性至关重要.

关键词:

凝血和花皮形成的过程消毒副产品消毒的副产品机器学习机器学习水处理工艺水处理过程.人工智能的人工智能是人工智能.

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