Accurate prediction for electro-oxidation regeneration of chromium- containing waste acid based on artificial neural network
The waste acid generated in the production of expanded graphite by “chromium method” has the characteristics of high acid concentration and high chromium content. The Cr(Ⅲ) can be oxidized to Cr(Ⅵ) by electro-oxidation in membrane system to realize the regeneration of waste acid containing chromium....
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Editorial Office of Industrial Water Treatment
2025-01-01
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| Series: | Gongye shui chuli |
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| Online Access: | https://www.iwt.cn/CN/10.19965/j.cnki.iwt.2023-1270 |
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| author | SHI Yaqi MENG Guangyuan CHEN Peng ZHANG Xinwan FU Tao YANG Zhengwu ZHANG Liansheng ZHANG Lehua |
| author_facet | SHI Yaqi MENG Guangyuan CHEN Peng ZHANG Xinwan FU Tao YANG Zhengwu ZHANG Liansheng ZHANG Lehua |
| author_sort | SHI Yaqi |
| collection | DOAJ |
| description | The waste acid generated in the production of expanded graphite by “chromium method” has the characteristics of high acid concentration and high chromium content. The Cr(Ⅲ) can be oxidized to Cr(Ⅵ) by electro-oxidation in membrane system to realize the regeneration of waste acid containing chromium. Since it was difficult to achieve real-time detection of Cr(Ⅵ) content in this highly acidic system, a study based on artificial neural network was conducted to accurately predict the electro-oxidation regeneration effect of chromium-containing waste acid. Based on the regeneration of chromium-containing waste acid experiments, the key characteristic parameters of hexavalent chromium regeneration including time, sulfuric acid concentration, and electrolyte volume were determined by correlation analysis. Then, through hyperparameter optimization, the relatively optimal topology structure of the artificial neural network was obtained as follows: Neurons=35, Batch size=30, Layers=4. The coefficient of determination(R2) between predicted value and experimental value was greater than 0.97, and the root-mean-square error(RMSE) was less than 0.04. Finally, the average relative error between predicted value and experimental value was 0.14%, which indicated that the model had good generalization ability. The artificial neural network model overcame the difficulty of predicting electrochemical processes due to multi-parameter, nonlinearity and time variability, and could realize the prediction of Cr(Ⅵ) regeneration under complex mapping conditions, which was of great significance for the optimization and control of electrochemical processes. |
| format | Article |
| id | doaj-art-fa73f8984ae645a1b87efa864983bf96 |
| institution | Kabale University |
| issn | 1005-829X |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Industrial Water Treatment |
| record_format | Article |
| series | Gongye shui chuli |
| spelling | doaj-art-fa73f8984ae645a1b87efa864983bf962025-01-24T07:59:03ZzhoEditorial Office of Industrial Water TreatmentGongye shui chuli1005-829X2025-01-0145113113810.19965/j.cnki.iwt.2023-12701005-829X（2025）01-0131-08Accurate prediction for electro-oxidation regeneration of chromium- containing waste acid based on artificial neural networkSHI Yaqi0MENG Guangyuan1CHEN Peng2ZHANG Xinwan3FU Tao4YANG Zhengwu5ZHANG Liansheng6ZHANG Lehua7National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, East China University of Science and Technology, Shanghai200237, ChinaNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, East China University of Science and Technology, Shanghai200237, ChinaSchool of Biology, Food, and Environment, Hefei University, Hefei230601, ChinaNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, East China University of Science and Technology, Shanghai200237, ChinaNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, East China University of Science and Technology, Shanghai200237, ChinaNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, East China University of Science and Technology, Shanghai200237, ChinaHeilongjiang Guangshengda New Material Technology Co., Ltd., Harbin158100, ChinaNational Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery, East China University of Science and Technology, Shanghai200237, ChinaThe waste acid generated in the production of expanded graphite by “chromium method” has the characteristics of high acid concentration and high chromium content. The Cr(Ⅲ) can be oxidized to Cr(Ⅵ) by electro-oxidation in membrane system to realize the regeneration of waste acid containing chromium. Since it was difficult to achieve real-time detection of Cr(Ⅵ) content in this highly acidic system, a study based on artificial neural network was conducted to accurately predict the electro-oxidation regeneration effect of chromium-containing waste acid. Based on the regeneration of chromium-containing waste acid experiments, the key characteristic parameters of hexavalent chromium regeneration including time, sulfuric acid concentration, and electrolyte volume were determined by correlation analysis. Then, through hyperparameter optimization, the relatively optimal topology structure of the artificial neural network was obtained as follows: Neurons=35, Batch size=30, Layers=4. The coefficient of determination(R2) between predicted value and experimental value was greater than 0.97, and the root-mean-square error(RMSE) was less than 0.04. Finally, the average relative error between predicted value and experimental value was 0.14%, which indicated that the model had good generalization ability. The artificial neural network model overcame the difficulty of predicting electrochemical processes due to multi-parameter, nonlinearity and time variability, and could realize the prediction of Cr(Ⅵ) regeneration under complex mapping conditions, which was of great significance for the optimization and control of electrochemical processes.https://www.iwt.cn/CN/10.19965/j.cnki.iwt.2023-1270membrane systemelectro-oxidationchromium containing waste acidartificial neural networkresource regeneration |
| spellingShingle | SHI Yaqi MENG Guangyuan CHEN Peng ZHANG Xinwan FU Tao YANG Zhengwu ZHANG Liansheng ZHANG Lehua Accurate prediction for electro-oxidation regeneration of chromium- containing waste acid based on artificial neural network Gongye shui chuli membrane system electro-oxidation chromium containing waste acid artificial neural network resource regeneration |
| title | Accurate prediction for electro-oxidation regeneration of chromium- containing waste acid based on artificial neural network |
| title_full | Accurate prediction for electro-oxidation regeneration of chromium- containing waste acid based on artificial neural network |
| title_fullStr | Accurate prediction for electro-oxidation regeneration of chromium- containing waste acid based on artificial neural network |
| title_full_unstemmed | Accurate prediction for electro-oxidation regeneration of chromium- containing waste acid based on artificial neural network |
| title_short | Accurate prediction for electro-oxidation regeneration of chromium- containing waste acid based on artificial neural network |
| title_sort | accurate prediction for electro oxidation regeneration of chromium containing waste acid based on artificial neural network |
| topic | membrane system electro-oxidation chromium containing waste acid artificial neural network resource regeneration |
| url | https://www.iwt.cn/CN/10.19965/j.cnki.iwt.2023-1270 |
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