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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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Industrial Water Treatment
2025-01-01
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| Series: | Gongye shui chuli |
| Subjects: | |
| Online Access: | https://www.iwt.cn/CN/10.19965/j.cnki.iwt.2023-1270 |
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| Summary: | 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. |
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| ISSN: | 1005-829X |