Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN
To accurately predict the SS<sub>eff</sub> (effluent SS) content and COD<sub>eff</sub> (effluent COD) concentration in water quality parameters and further improve the water quality early warning mechanism,this paper proposes the PSOGA-WNN soft measurement model of paper wast...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2023-01-01
|
| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.08.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841536120636047360 |
|---|---|
| author | LIU Yuhui MAI Wenjie LI Xiaoyong ZHAO Yinzhong HE Xinzhong HUANG Mingzhi |
| author_facet | LIU Yuhui MAI Wenjie LI Xiaoyong ZHAO Yinzhong HE Xinzhong HUANG Mingzhi |
| author_sort | LIU Yuhui |
| collection | DOAJ |
| description | To accurately predict the SS<sub>eff</sub> (effluent SS) content and COD<sub>eff</sub> (effluent COD) concentration in water quality parameters and further improve the water quality early warning mechanism,this paper proposes the PSOGA-WNN soft measurement model of paper wastewater effluent quality to obtain the main water quality technical parameters,COD<sub>inf</sub> (influent COD),Q (influent flow),pH (influent pH),SS<sub>inf</sub> (influent SS),T (influent temperature),DO (influent dissolved oxygen),COD<sub>eff</sub>,and SS<sub>eff,</sub> for predicting the quality of wastewater from the wastewater treatment plant.Among them,the prediction results of PSOGA-WNN are compared with the neural networks of PSO-WNN,GA-WNN,and PSOGA-BP.The results show that the PSOGA-WNN neural network has the highest prediction accuracy,which indicates that the PSOGA hybrid parameter optimization algorithm based on the genetic algorithm and particle swarm algorithm has obvious superiority in optimizing the prediction accuracy of the model.The WNN neural network has certain advantages over BP neural network in terms of fitting degree as well as error accuracy and is an effective means of simulation prediction. |
| format | Article |
| id | doaj-art-aa2b96b314014b8f843e01a3c454a96d |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-aa2b96b314014b8f843e01a3c454a96d2025-01-15T02:22:15ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352023-01-014447638018Soft Measurement of Wastewater Treatment System Based on PSOGA-WNNLIU YuhuiMAI WenjieLI XiaoyongZHAO YinzhongHE XinzhongHUANG MingzhiTo accurately predict the SS<sub>eff</sub> (effluent SS) content and COD<sub>eff</sub> (effluent COD) concentration in water quality parameters and further improve the water quality early warning mechanism,this paper proposes the PSOGA-WNN soft measurement model of paper wastewater effluent quality to obtain the main water quality technical parameters,COD<sub>inf</sub> (influent COD),Q (influent flow),pH (influent pH),SS<sub>inf</sub> (influent SS),T (influent temperature),DO (influent dissolved oxygen),COD<sub>eff</sub>,and SS<sub>eff,</sub> for predicting the quality of wastewater from the wastewater treatment plant.Among them,the prediction results of PSOGA-WNN are compared with the neural networks of PSO-WNN,GA-WNN,and PSOGA-BP.The results show that the PSOGA-WNN neural network has the highest prediction accuracy,which indicates that the PSOGA hybrid parameter optimization algorithm based on the genetic algorithm and particle swarm algorithm has obvious superiority in optimizing the prediction accuracy of the model.The WNN neural network has certain advantages over BP neural network in terms of fitting degree as well as error accuracy and is an effective means of simulation prediction.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.08.001WNN neural networkwavelet transformsoft measurement |
| spellingShingle | LIU Yuhui MAI Wenjie LI Xiaoyong ZHAO Yinzhong HE Xinzhong HUANG Mingzhi Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN Renmin Zhujiang WNN neural network wavelet transform soft measurement |
| title | Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN |
| title_full | Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN |
| title_fullStr | Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN |
| title_full_unstemmed | Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN |
| title_short | Soft Measurement of Wastewater Treatment System Based on PSOGA-WNN |
| title_sort | soft measurement of wastewater treatment system based on psoga wnn |
| topic | WNN neural network wavelet transform soft measurement |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2023.08.001 |
| work_keys_str_mv | AT liuyuhui softmeasurementofwastewatertreatmentsystembasedonpsogawnn AT maiwenjie softmeasurementofwastewatertreatmentsystembasedonpsogawnn AT lixiaoyong softmeasurementofwastewatertreatmentsystembasedonpsogawnn AT zhaoyinzhong softmeasurementofwastewatertreatmentsystembasedonpsogawnn AT hexinzhong softmeasurementofwastewatertreatmentsystembasedonpsogawnn AT huangmingzhi softmeasurementofwastewatertreatmentsystembasedonpsogawnn |