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机器学习战略通过增量进步确保了城市智能饮用水处理厂的安全

Yu-Qi Wang¹, Hong-Cheng Wang¹, Zi-Jie Xiao²

¹State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.

|March 29, 2025

概括

一个新的Step-by-Step (SBS) 机器学习策略通过提高数据安全性和控制来增强城市饮用水处理厂 (DWTP). 这种方法显著降低了消毒剂剂量和度,提高了智慧城市水系统的弹性.

科学领域:

环境工程环境工程
水处理技术水处理技术
在公用事业中的人工智能.

背景情况:

城市饮用水处理厂 (DWTP) 对公共卫生和智慧城市发展至关重要.
数据安全和可靠性的挑战阻碍了DWTP中的机器学习的有效性,包括不精确的控制,传感器问题和传输错误.

研究的目的:

引入和评估一种新的渐进式Step-by-Step (SBS) 机器学习策略,以加强城市DWTP中的数据安全和控制.
将SBS策略应用于精确的消毒剂剂量控制和整体供水系统数据安全.
评估SBS策略的表现与现有方法相比,以及在模拟不利条件下.

主要方法:

开发和应用一个渐进的Step-by-Step (SBS) 机器学习策略.
深度神经网络与消毒剂剂量控制的SBS策略的整合.
在现实世界DWTP中评估SBS模型,并通过模拟数据丢失场景.

主要成果:

使用SBS策略的深度神经网络超过了其他八种方法.
在现实世界DWTP中,与手动模糊控制相比,SBS模型减少了22.0%的消毒剂剂量和16.0%的废水度.
模拟表明,基于SBS的校正保持了DWTP的稳定性和安全性,即使在极端数据丢失场景中.

结论:

该SBS机器学习策略显著提高了消毒剂剂量的精度,并减少了城市DWTP中的废水度.
该SBS战略增强了供水系统的数据安全性和稳定性,特别是在具有挑战性的条件下.
实施SBS战略可以提高供水网络的智能化,改善应急管理,并支持智慧城市水系统的可持续发展.

关键词:

剂量控制剂量控制饮用水处理厂饮用水处理厂机器学习是机器学习.智慧城市用水系统水安全水的安全.

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