首页|原水2-MIB影响因素分析及数据AI应用探索

原水2-MIB影响因素分析及数据AI应用探索

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《生活饮用水卫生标准》(GB 5749-2022)将2-甲基异莰醇(2-MIB)列为扩展指标,并提出0。000 01 mg/L(10 ng/L)的要求,部分水厂现有工艺难以满足要求,需积极应对。文章以深圳市SZ水库、TG水库、XL水库、SY水库2021年-2022年的2-MIB数据为基础,分析2-MIB变化规律及其影响因素。结果表明,不同水库2-MIB浓度水平差异较大,2-MIB整体波动明显并呈季节性波动特征,其浓度受水温、藻密度、高锰酸盐指数、气温、风速等因素影响较大。深圳地区部分常规处理工艺水厂需通过投加粉末活性炭以保障出水2-MIB达标,为预判2-MIB浓度每日变化趋势以及时调整粉末活性炭投加量,文章基于2-MIB变化规律及影响因素分析结果,结合AI算法构建原水2-MIB预测预警模型,该模型可预测未来15 d内2-MIB逐日浓度,平均误差达到4。83 ng/L,有效助力2-MIB稳定达标。
Analysis of 2-MIB Influencing Factors in Raw Water and Exploration of Data AI Application
Standards for Drinking Water Quality(GB 5749-2022)lists 2-methylisobornyl alcohol(2-MIB)as an extended index and proposes a requirement of 0.000 01 mg/L(10 ng/L).Some water treatment plants(WTPs)have existing processes that are difficult to meet the requirements and need to actively respond.This article was based on the 2-MIB data of SZ Reservoir,TG Reservoir,XL Reservoir and SY Reservoir in Shenzhen from 2021 to 2022,analyzing the variation patterns and influencing factors of 2-MIB.The results showed that there were significant differences in the concentration levels of 2-MIB in different reservoirs,and the overall fluctuation of 2-MIB was significant and exhibited seasonal characteristics.Its concentration was greatly influenced by factors such as water temperature,algal density,permanganate index,temperature and wind speed.Some conventional treatment process WTPs in Shenzhen needed to add powdered activated carbon to ensure that the effluent meets the 2-MIB standard.In order to predict the daily trend of 2-MIB concentration and adjust the dosage of powdered activated carbon in a timely manner,2-MIB of raw water prediction and warning model was constructed based on the analysis of the 2-MIB change law and influencing factors,combined with AI.This model can predict the daily concentration of 2-MIB in the next 15 days,with an average error of 4.83 ng/L,assist in achieving stable 2-MIB standards effectively.

2-methylisobornyl alcohol(2-MIB)raw waterinfluencing factorchange patternprediction model

梁思宸、张金松、伍驰中、安娜、李悦、王巍巍

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深圳市水务<集团>有限公司,广东 深圳 518000

2-甲基异莰醇(2-MIB) 原水 影响因素 变化规律 预测模型

2024

净水技术
上海市净水技术学会,上海市城乡建设和交通委员会科学技术委员会办公室

净水技术

CSTPCD
影响因子:0.643
ISSN:1009-0177
年,卷(期):2024.43(12)