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作 者:韩梅 李玉宝 邹放 刘畅[3] 樊玉芳 顾军农 HAN Mei;LI Yubao;ZOU Fang;LIU Chang;FAN Yufang;GU Junnong(Beijing Waterworks Group Co.,Ltd.,Beijing Engineering Research Center for Drinking Water Quality,Beijing100012,China;Chengdu Evercreative Technology Co.,Ltd.,Chengdu610000,China;College of Environment and Ecology,Chongqing University,Chongqing400044,China)
机构地区:[1]北京市自来水集团有限责任公司,北京市供水水质工程技术研究中心,北京100012 [2]成都九鼎瑞信科技股份有限公司,四川成都610000 [3]重庆大学环境与生态学院,重庆400044
出 处:《净水技术》2021年第9期40-47,共8页Water Purification Technology
基 金:国家水体污染控制与治理科技重大专项(2017ZX07108-002)。
摘 要:为实现混凝投药的精准控制,以大型水厂粗粒度数据和细粒度数据为基础,采用箱线图和滑动均值异常数据识别技术的预处理方法,对长短期记忆网络、支持向量机、随机森林、XGBoost等算法建立混凝投药模型的效果进行了评估。研究发现:基于A、B水厂的大数据,采用XGBoost算法建立的混凝投药模型效果均最优,对聚合氯化铝(PAC)和氯化铁(FeCl_(3))的建模效果评估MAPE分别为3.42和3.72;采用箱线图结合移动平滑的技术对异常值进行处理对建模效果大幅提升;将一种药剂的投加量作为另一种药剂预测的特征值输入的方法,对双药投加模型的预测效果提升有限;原水时均水量、浑浊度、温度、历史加药数据对混凝剂投加量的准确预测有重要影响。In order to realize the accurate control of coagulant dosing,based on coarse-grained data and fine-grained data of large-scale water treatment plant(WTP),the effects of long short-term memory network,support vector machine,random forest,XGBoost and other algorithms to establish coagulation dosing model were evaluated by using box plot and moving average abnormal data recognition technology.It was found that based on the big data of A and B WTP,the coagulation dosing model established by XGBoost was the best,and the evaluation MAPE value of modeling effect of polyaluminum chloride and ferric chloride were 3.42 and 3.72 respectively.Abnormal value was processed by box diagram combined with moving smooth technology,which greatly improves modeling effect,for the method of inputting the dosage of one agent as the agent of the prediction of another,the prediction effect of two drug dosing model was limited.Hourly average water volume of raw water,raw water turbidity,raw water temperature,and historical dosing data had important influence on the accurate prediction of coagulant dosage.
关 键 词:混凝投药模型 氯化铁(FeCl_(3)) 聚合氯化铝(PAC) 大数据 水厂
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