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| Random Forest Modeling for Prediction of Effluent Indices of Papermaking Wastewater |
| Received:February 24, 2019 |
| DOI：10.11980/j.issn.0254-508X.2019.08.010 |
| Key Words:wastewater treatment processes random forest model effluent indicators regression model |
| Fund Project:制浆造纸工程国家重点实验室开放基金资助项目 201813制浆造纸工程国家重点实验室开放基金资助项目（201813）。 |
| Author Name | Affiliation | | XIN Chen | Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu Province,210037 | | LIU Hongbin | Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu Province,210037
State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou， Guangdong Province,510640)
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| Abstract:Effluent chemical oxygen demand (COD) and effluent suspended solid (SS) are important quality indicators of papermaking wastewater treatment process. To improve the prediction performance of these two indicators, a random forest (RF) model was proposed and implemented regressing forecasting using R. Compared with the conventional models including partial least squares (PLS), support vector regression (SVR), and artificial neural network (ANN), the RF model had the advantages of high prediction accuracy, small error, better generalization perforamce, and fewer parameters adjustment. In terms of the effluent COD prediction, the correlation coefficient r value of RF was 0.7954, which increased by 8.88%, 10.73%, and 14.68% compared with PLS, SVR, and ANN, respectively. In terms of the effluent SS prediction, the r value of RF was 0.8551, which increased by 15.43%, 24.25% and 30.79% compared with PLS, SVR, and ANN, respectively. |
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