| 引用本文: | 徐斌,金芯百,罗振宁,张天阳.人工智能驱动的饮用水消毒方式优化与风险控制研究进展[J].哈尔滨工业大学学报,2026,58(6):142.DOI:10.11918/202511046 |
| XU Bin,JIN Xinbai,LUO Zhenning,ZHANG Tianyang.Research progress on artificial intelligence-driven optimization of drinking water disinfection methods and risk control[J].Journal of Harbin Institute of Technology,2026,58(6):142.DOI:10.11918/202511046 |
|
| |
|
|
| 本文已被：浏览 1274次 下载 74次 |
码上扫一扫！ |
|
|
| 人工智能驱动的饮用水消毒方式优化与风险控制研究进展 |
|
徐斌1,2,金芯百1,2,罗振宁1,2,张天阳1,2
|
|
(1.同济大学 环境科学与工程学院,上海 200092;2.水利部长三角城镇供水节水及水环境治理重点实验室,上海 200092)
|
|
| 摘要: |
| 高质量、安全可靠的饮用水供应是社会经济可持续发展的必要保障。消毒是饮用水处理中必不可少的工艺,消毒过程既要确保病原体被有效灭活,又要严格控制消毒剂残留与副产物生成,实现生物风险与化学风险的协同管控。饮用水消毒效果受环境条件、水质背景、消毒方法、标准要求等多重因素影响,其效能控制尤为困难。由此,消毒工艺呈现出更高的复杂性,对运行的精细化与动态调控提出了更高要求。近年来,人工智能（AI）技术的快速发展为饮用水消毒工艺的优化与衍生风险控制提供了新的思路。本研究系统阐述了AI驱动多源数据融合在提升消毒工艺效能方面的应用,以前体物风险识别、消毒效能评估、副产物预测及水质安全筛查等关键环节为对象,系统梳理了动态学习算法在提升微生物灭活效果、控制消毒副产物生成以及毒理学风险快速筛查中的应用技术方法,提出了AI技术在消毒工艺中应用的策略和发展方向。随着AI技术不断发展和大数据的广泛应用,AI将在消毒工艺优化应用、保障水质生物和化学安全以及应对复杂水质问题上发挥更重要的作用。 |
| 关键词: 人工智能 饮用水消毒 消毒副产物 水质监测 风险控制 |
| DOI：10.11918/202511046 |
| 分类号:X703 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(0,3)；同济大学建筑设计研究院(集团)有限公司科研项目(2023J-JB10) |
|
| Research progress on artificial intelligence-driven optimization of drinking water disinfection methods and risk control |
|
XU Bin1,2,JIN Xinbai1,2,LUO Zhenning1,2,ZHANG Tianyang1,2
|
|
(1.College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China; 2.Key Laboratory of Urban Water Supply, Water Saving and Water Environment Governance in the Yangtze River Delta of Ministry of Water Resources, Shanghai 200092, China)
|
| Abstract: |
| Ensuring a high-quality, safe, and reliable drinking water supply is a necessary guarantee for sustainable socio-economic development. Disinfection is an indispensable process in drinking water treatment. The disinfection process must effectively inactivate pathogens, strictly control disinfectant residuals and byproduct generation, and achieve collaborative control of biological and chemical risks. The effect of drinking water disinfection is influenced by multiple factors including environmental conditions, water quality background, disinfection methods, and standard requirements, making its efficacy control particularly difficult. Consequently, the disinfection process exhibits higher complexity, placing higher demands on the refined and dynamic regulation of operation. In recent years, the rapid development of artificial intelligence (AI) technology has provided new ideas for the optimization of drinking water disinfection processes and the control of derived risks. This paper systematically elaborated on the application of AI-driven multi-source data fusion in improving the efficacy of disinfection processes. By taking key links such as precursor risk identification, disinfection efficacy assessment, byproduct prediction, and water quality safety screening as objects, this paper systematically reviewed the technical methods for applying dynamic learning algorithms in improving microbial inactivation effects, controlling disinfection byproduct generation, and rapidly screening toxicological risks and proposed strategies and development directions for the application of AI technology in disinfection processes. With the continuous development of AI technology and the wide application of big data, AI will play a more important role in the optimal application of disinfection processes, ensuring the biological and chemical safety of water quality and coping with complex water quality problems. |
| Key words: artificial intelligence drinking water disinfection disinfection byproduct water quality monitoring risk control |
|
|
|
|
