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| Study on Intelligent Prediction Model of Papermaking Wastewater Effluent Quality Index Based on Ensemble Deep Learning |
| Received:August 07, 2024 Revised:October 21, 2024 |
| DOI：10.11981/j.issn.1000-6842.2025.02.173 |
| Key Words:papermaking wastewater treatment process;data dimensionality reduction;long short-term memory network;ensemble learning;soft sensor mode |
| Fund Project:山东省自然科学基金（ZR2021MF135）；江苏省高等学校自然科学研究重大项目（22KJA530003）。 |
| Author Name | Affiliation | Postcode | | WANG Jinyong* | Nanjing Forestry University， Nanjing， Jiangsu Province， 210037 | 210037 | | WANG Xinyuan | Nanjing Forestry University， Nanjing， Jiangsu Province， 210037 | 210037 | | WEI Wenguang | Shandong Huatai Paper Co.， Ltd.， Dongying， Shandong Province， 257335
Shandong Yellow Triangle Biotechnology Industry Research Institute Co.， Ltd.， Dongying， Shandong Province， 257399 | 257399 | | ZHANG Fengshan | Shandong Huatai Paper Co.， Ltd.， Dongying， Shandong Province， 257335
Shandong Yellow Triangle Biotechnology Industry Research Institute Co.， Ltd.， Dongying， Shandong Province， 257399 | 257399 | | HUANG Peng | Shandong Huatai Paper Co.， Ltd.， Dongying， Shandong Province， 257335 | 257335 | | ZHOU Jingpeng | Shandong Huatai Paper Co.， Ltd.， Dongying， Shandong Province， 257335 | 257335 | | WAN Bing | Shandong Huatai Paper Co.， Ltd.， Dongying， Shandong Province， 257335 | 257335 | | NIU Guoqiang | South China Normal University， Guangzhou， Guangdong Province， 510006 | 510006 | | LIU Hongbin* | Nanjing Forestry University， Nanjing， Jiangsu Province， 210037
Shandong Huatai Paper Co.， Ltd.， Dongying， Shandong Province， 257335 | 257335 |
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| Abstract: |
| To address the limitations of single model and improve overall robustness， this study focused on a wastewater treatment dataset from a single processing stage of a small-scale papermaking plant， took kernel principal component analysis （KPCA） for dimensionality reduction to effectively extract key features from the data， firstly. Then， integrated multiple long-short term memory （LSTM） learners using the Bagging ensemble strategy， and constructed a KPCA-Bagging-LSTM prediction model， where LSTM was capable of modeling the temporal characteristics of wastewater data. The results showed that the KPCA-Bagging-LSTM model achieved a coefficient of determination （R²） of 0.76， significantly outperforming other methods. The Root Mean Square Error （RMSE） and Mean Absolute Percentage Error （MAPE） were 3.55 mg/L and 4.01%， respectively， indicating lower prediction error and higher accuracy. By combining feature dimensionality reduction with ensemble learning， the proposed KPCA-Bagging-LSTM model enhanced prediction performance and provided an effective solution for forecasting COD and other effluent indicators in papermaking wastewater treatment. |
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