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文章摘要
基于偏最小二乘的絮体图像信息与水质的相关性
Correlation Between Floc Morphology and Water Quality Based on Partial Least Squares
  
DOI:
中文关键词: 图像处理技术  偏最小二乘法  出水指示  活性污泥
英文关键词: Image processing technology  Partial least squares(PLS)  Effluent indication  Active sludge
基金项目:国家重点研发计划“工业互联网边缘计算节点设计方法与运行关键技术”基金资助项目(2018YFB1700200)
作者单位
和梦雪 沈阳化工大学环境与安全工程学院 
刘健 沈阳化工大学环境与安全工程学院 
范文玉 沈阳化工大学环境与安全工程学院 
赵立杰 沈阳化工大学信息工程学院 
左越 沈阳化工大学信息工程学院 
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中文摘要:
      针对污水处理运行过程COD、BOD5、TN和TP实时测量的问题,提出一种基于絮体形态参数的偏最小二乘法模型。采用图像分析技术对污泥絮体形态特征进行提取,基于相关性从提取的形态参数和试验期间的运行参数中选择模型的输入变量,通过偏最小二乘法建立输入变量与4个水质指标的预测关系模型。结果表明,COD、BOD5、TN、TP的交叉有效性系数分别为0.736、0.682、0.839、0.618,絮体形态参数与水质指标有明显的相关性,可用于预测出水水质。
英文摘要:
      Based on floc morphology parameters, a partial least squares model was established to solve the problem of real time measurement of COD, BOD5, TN and TP during the operation of sewage treatment. The floc morphological characteristics of sludge were extracted by image analysis technology. Based on the correlation, the input variables of the model were selected from the extracted morphological parameters and the operation parameters during the experiment. The prediction model of the relationship between input variables and four water quality indexes was established by partial least squares. 〖JP2〗The results indicated that the cross validity coefficients of COD, BOD5, TN and TP were 0.736, 0.682, 0.839 and 0.618, respectively. There was a significant correlation between 〖JP3〗floc morphology parameters and water quality indexes. This model could be used to predict effluent quality.
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