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二次供水流量预测模型的构建和应用

肖磊 蒋瑜 刘书明 吴雪 陈春芳

净水技术2024,Vol.43Issue(5):71-79,9.
净水技术2024,Vol.43Issue(5):71-79,9.DOI:10.15890/j.cnki.jsjs.2024.05.008

二次供水流量预测模型的构建和应用

Construction and Application of Flow Prediction Model in Secondary Water Supply

肖磊 1蒋瑜 2刘书明 3吴雪 3陈春芳2

作者信息

  • 1. 清华大学环境学院,北京 100084||常州通用自来水有限公司,江苏常州 213003
  • 2. 常州通用自来水有限公司,江苏常州 213003
  • 3. 清华大学环境学院,北京 100084
  • 折叠

摘要

Abstract

At present,the water pump in the secondary water supply system generally has the problem of high energy consumption caused by"big horse pulls a small carriage".In order to solve the problem,a kind of secondary water supply flow prediction model which is more suitable for the actual working conditions need to be established.Based on the flow monitoring data of 149 secondary water supply communities in Changzhou,Jiangsu Province,this paper formed a characteristic sample set of secondary water supply flow.It was the first time that the characteristic data such as the number of pressurized households,the occupancy rate,the highest daily flow rate and the maximum hourly flow rate on the highest flow rate day were integrated,and the BP neural network optimized based on genetic algorithm was used for data mining.The prediction model of secondary water supply flow with availability was constructed and applied to the renovation of old pump house.Then power consumption had decreased by over 19%.Good energy saving effect was achieved.Flow prediction model can provide more accurate flow evaluation tools for secondary water supply design selection,energy saving renovation and other work,but also provide new research ideas for secondary water supply energy saving and emission reduction,help achieve the"double carbon"goal,and promote the green development of water supply.

关键词

二次供水/遗传算法/BP神经网络/流量预测/节能减排

Key words

secondary water supply/genetic algorithm/BP neural network/flow prediction/energy saving and emission reduction

分类

土木建筑

引用本文复制引用

肖磊,蒋瑜,刘书明,吴雪,陈春芳..二次供水流量预测模型的构建和应用[J].净水技术,2024,43(5):71-79,9.

基金项目

国家水体污染控制与治理科技重大专项(2017ZX07201002) (2017ZX07201002)

净水技术

OACSTPCD

1009-0177

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