用户登录
期刊信息
  • 主管单位:
  • 上海市教育委员会
  • 主办单位:
  • 上海理工大学
  • 主  编:
  • 庄松林
  • 地  址:
  • 上海市军工路516号
  • 邮政编码:
  • 200093
  • 联系电话:
  • 021-55277251
  • 电子邮件:
  • xbzrb@usst.edu.cn
  • 国际标准刊号:
  • 1007-6735
  • 国内统一刊号:
  • 31-1739/T
  • 邮发代号:
  • 4-401
  • 单  价:
  • 15.00
  • 定  价:
  • 90.00
刘洪波,黄剑虹,张国荣,吴燕,刘丽梅.给水厂混凝剂智能投加模型构建与应用[J].上海理工大学学报,2022,44(4):351-356,387.
给水厂混凝剂智能投加模型构建与应用
Modeling and application of intelligent coagulant dosing model in drinking water plant
投稿时间:2022-07-02  
DOI:10.13255/j.cnki.jusst.20220702002
中文关键词:  给水厂  智能加药  局部离群因子  灰色关联度  BP神经网络  贝叶斯优化
英文关键词:drinking water plant  intelligent dosing  local outlier factor  grey correlation degree  BP neural network  Bayesian optimization
基金项目:
作者单位
刘洪波 上海理工大学 环境与建筑学院上海 200093 
黄剑虹 上海理工大学 环境与建筑学院上海 200093 
张国荣 昆山市自来水集团有限公司昆山215300 
吴燕 昆山市自来水集团有限公司昆山215300 
刘丽梅 昆山汉元经水水务科技有限公司昆山215300 
摘要点击次数: 41
全文下载次数: 40
中文摘要:
      选取华东地区某给水厂生产中积累的数据建立加药量预测模型,以求在给水厂中实现混凝剂的智能投加。在数据清洗时,针对水厂运行实际情况,结合局部离群因子(LOF)算法、K最近邻插补(KNN)算法与平滑滤波算法对随机误差进行处理。同时在建模前使用灰色关联度分析评估了各原水指标与混凝剂加药量的联系,对联系较为紧密的原水指标进行混凝机理的分析。模型选用BP神经网络,并使用贝叶斯优化算法对模型的参数寻优,建立了多个模型进行评估,其中最优模型在测试集6万个样本上的平均绝对误差为3.66 L/h,结果表明最优模型能够准确预测混凝剂加药量。在此基础上,展望将建立的智能投加模型应用于水厂加药系统中,帮助水厂加药系统的智能化改造方案的实施。
英文摘要:
      The data accumulated in the production of a drinking water plant in East China were selected to establish a dosing prediction model for intelligent coagulant dosing in the plant. During data cleaning, according to the operation conditions of the plant, the random error was processed by combining the local outlier factor (LOF) algorithm, K-nearest neighbor imputation (KNN) algorithm and data smoothing algorithm. The association of each raw water indicator with coagulant dosage was also evaluated using gray correlation analysis before modeling, and the closely related raw water indicators were analyzed for coagulation mechanism. The model uses BP neural network, and the Bayesian optimization algorithm was used to optimize the parameters of the model. Several models were built for evaluation, and the mean absolute error of the optimal model on sixty thousand samples in the test set was 3.66 L/h. The results show that the optimal model can accurately predict the dosage of coagulant. On this basis, the established intelligent dosing model was prospected to be applied to the water plant dosing system to help the implementation of the intelligent modification of the water plant dosing system.
HTML   查看全文  查看/发表评论  下载PDF阅读器