Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (1): 101-108.doi: 10.6040/j.issn.1672-3961.0.2018.552

• Chemistry and Environment • Previous Articles     Next Articles

Study on modeling methods of wastewater treatment processes with canonical correlation analysis

Hongbin LIU1,2(),Liu SONG1   

  1. 1. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
    2. State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China
  • Received:2018-12-24 Online:2020-02-20 Published:2020-02-14
  • Supported by:
    制浆造纸工程国家重点实验室开放基金资助项目(201813);南京林业大学高层次人才科研启动基金(GXL029)

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Abstract:

With the improvement of public awareness of environmental protection, the discharge of industrial wastewater became a crucial issue in industrial production. The typical water quality models were based on static models which ignored the dynamic information in process variables, resulting in the reduction in the accuracy of model prediction and the generalization ability of the models. Considering the time-varying and dynamic characteristics of process variables, a time difference model embedded into canonical correlation analysis was proposed in this paper. The effect of the order of the time difference model on the prediction accuracy was also analyzed. Compared with the traditional canonical correlation analysis, the root mean square error values of effluent chemical oxygen demand and effluent total nitrogen were reduced from 1.502 8 to 0.564 5 and from 2.344 0 to 1.192 6, respectively. The correlation coefficient values were increased from 0.422 7 to 0.847 0 and from 0.405 9 to 0.793 6, respectively. The results indicated that the prediction accuracy and generalization ability of the model were both improved.

Key words: wastewater treatment processes, dynamic processes, time difference, canonical correlation analysis, soft sensor

CLC Number: 

  • X703

Fig.1

Wastewater treatment process variables"

Fig.2

Soft sensor model flow chart of TD-CCA"

Fig.3

Prediction errors of TD Models with different time interval"

Fig.4

Prediction results of CCA"

Fig.5

Prediction results of TD-CCA"

Table 1

Prediction results of CCA and TD-CCA"

模型 集合 出水COD 出水TN
RMSE MAPE/% r RMSE MAPE/% r
CCA
训练集 0.822 9 11.16 0.765 9 1.510 7 15.610 0 0.566 5
测试集 1.502 8 13.74 0.422 7 2.344 0 20.28 0 0.405 9
TD-CCA
训练集 0.454 6 0.922 2 0.860 4 1.072 6 0.828 9 0.864 7
测试集 0.564 5 0.937 6 0.847 0 1.192 6 0.891 7 0.793 6

Fig.6

Prediction results of CCA"

Fig.7

Prediction results of TD-CCA"

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