| 引用本文: | 姜旭,赵慕南,纪峰,崔崇威.东北地区原水氯化消毒副产物三氯甲烷生成模型预测[J].哈尔滨工业大学学报,2020,52(11):33.DOI:10.11918/201906127 |
| JIANG Xu,ZHAO Munan,JI Feng,CUI Chongwei.Prediction of chlorination disinfection by-product trichloromethane generation model of raw water in northeast China[J].Journal of Harbin Institute of Technology,2020,52(11):33.DOI:10.11918/201906127 |
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| 摘要: |
| 针对含有天然有机物的原水经过液氯或次氯酸钠消毒后生成多种含卤素的化合物——消毒副产物（DPBs)问题,尤其三氯甲烷(TCM)的出现引起广泛关注,其生成影响因素主要包括水中的有机质含量、pH、水温、投氯量、消毒接触时间等.经调查,东北地区许多市、县净水厂规模多为5万t/d以下的小型水厂,由于检测能力及成本的问题,无法实现对出厂水中TCM的检测,一旦原水水质发生变化则无法保证出厂水水质安全.因此,建立常规指标与TCM生成量之间的预测模型,将更好地帮助不具备TCM检测能力的水厂预测TCM的生成情况.以东北某大型净水厂为例,采用国家标准方法检测各指标含量,通过多元线性回归分析方法,对原水常规指标水温、pH、浑浊度、高锰酸盐指数、投氯量这些基于原水和消毒工艺的主要参数进行统计分析,建立TCM的生成模型,为具有类似水源及消毒方式的中小型净水厂预测TCM的生成提供了很好的预判方式. |
| 关键词: 天然有机物 三氯甲烷 氯化消毒 消毒副产物 生成模型 |
| DOI：10.11918/201906127 |
| 分类号:X524 |
| 文献标识码:A |
| 基金项目:哈尔滨工业大学任南琪工作室,环境与生态学(HSCJ201704) |
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| Prediction of chlorination disinfection by-product trichloromethane generation model of raw water in northeast China |
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JIANG Xu1,2,ZHAO Munan1,JI Feng2,CUI Chongwei1
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(1.State Key Laboratory of Urban Water Resource and Environment (Harbin Institute of Technology), Harbin 150090, China; 2. Harbin Water Supply Group Co., Ltd., Harbin 150001, China)
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| Abstract: |
| Raw water with natural organic matters produces a variety of halogenic organic compounds after liquid chlorine or sodium hypochlorite disinfection, known as disinfection by-products (DBPs). Among them, the generation of trichloromethane (TCM) has attracted extensive attention. Factors affecting its formation including the organic content, pH, water temperature, chlorine dosage, disinfection contact time, and so on. According to survey, the scale of the water treatment plants in many cities and counties in northeast China is mainly small with a daily production capacity of less than 50 000 t/d. Moreover, due to the problems of testing capability and cost, it is impossible to detect TCM continuously in effluent water, and once the raw water quality changes, the safety of the effluent water quality will not be guaranteed. Therefore, the establishment of the prediction model between TCM and conventional parameters will help water treatment plants with no TCM detection abilities to predict the generation of TCM. In this study, taking a large water treatment plant in northeast China as an example, the contents of each parameter were detected using national standard methods. With the method of multiple linear regression analysis, the parameters which concern with the characteristics of raw water and disinfection processes, such as water temperature, pH, turbidity, potassium permanganate index, and chlorine dose, were added to the establishment of the TCM generation model. The multiple regression model provides a good prediction method for the generation of TCM in small and medium-sized water treatment plants with similar raw water characteristics and disinfection processes. |
| Key words: natural organic matter trichloromethane chlorination disinfection by-products generation model |
