Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (3): 133-142.doi: 10.6040/j.issn.1672-3961.0.2019.009

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Application of variable selection in soft sensor modeling of wastewater treatment processes

Hongbin LIU1,2(),Qiyue WU1,Liu SONG1   

  1. 1. Jiangsu 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:2019-01-07 Online:2020-06-20 Published:2020-06-16
  • 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:

Chemical oxygen demand and suspended solid were important monitoring indices of effluent discharge in paper-making industry. An effective model of effluent quality of wastewater treatment processes was of key importance to monitoring and controlling pollution emission. Concerning the strong correlations among the input variables and the complicated characteristics of wastewater treatment processes in paper-making industry, partial least squares (PLS) method was applied to extract information of variables importance in projection (VIP) for variable selection (VS). Then the optimal variables were chosen as new input variables for soft sensor models to predict the effluent qualities of a papermaking wastewater treatment process. Compared to the LSSVM model, the root mean square error (RMSE) of VS-based LSSVM model was reduced by 15.2%, and the correlation coefficient (r) was increased by 14.4%. For the effluent SS, the value of RMSE was decreased by 20.5%, and the value of r was increased by 16.1%. The results showed that the proposed method not only reduced the model complexity, but also enhanced the model generalization capacity.

Key words: wastewater treatment, effluent quality, variable selection, variables importance in projection, partial least squares

CLC Number: 

  • X703

Fig.1

VIP scores of auxiliary variables"

Fig.2

Coefficient of stepwise regression"

Fig.3

Prediction results of effluent SS"

Table 1

Prediction results of models"

模型出水COD出水SS
RMSEMAPE/%rRMSEMAPE/%r
BPNN训练集5.196 14.370 50.807 90.698 32.415 80.832 3
测试集5.523 66.373 70.660 40.714 82.610 80.715 7
VS-BPNN训练集4.396 44.553 10.860 40.632 62.259 30.864 7
测试集4.404 15.324 30.797 30.649 72.270 30.801 6
LSSVM训练集4.324 84.529 50.883 60.723 42.580 60.829 1
测试集4.948 15.740 40.697 60.758 42.680 70.701 6
VS-LSSVM训练集4.003 94.169 70.890 50.596 62.134 50.889 5
测试集4.194 64.800 00.798 40.603 32.167 60.814 3

Fig.4

Prediction results of effluent SS"

Fig.5

Prediction results of effluent COD"

Fig.6

Prediction results of effluent COD"

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