Electroreduction of hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a back propagation neural network

Highlights

● Titanium-based flow-through electrode achieved high Cr(VI) reduction efficiency.

● Flow-through pattern enhanced the mass transfer and reduced cathodic polarization.

● BPNN predicted the optimal electroreduction conditions of flow-through cell.

Abstract

Flow-through electrodes have been demonstrated to be effective for electroreduction of Cr(VI), but shortcomings are tedious preparation and short lifetimes. Herein, porous titanium available in the market was studied as a flow-through electrode for Cr(VI) electroreduction. In addition, the intelligent prediction of electrolytic performance based on a back propagation neural network (BPNN) was developed. Voltametric studies revealed that Cr(VI) electroreduction was a diffusion-controlled process. Use of the flow-through mode achieved a high limiting diffusion current as a result of enhanced mass transfer and favorable kinetics. Electroreduction of Cr(VI) in the flow-through system was 1.95 times higher than in a parallel-plate electrode system. When the influent (initial pH 2.0 and 106 mg/L Cr(VI)) was treated at 5.0 V and a flux of 51 L/(h·m²), a reduction efficiency of ~99.9% was obtained without cyclic electrolysis process. Sulfate served as the supporting electrolyte and pH regulator, as reactive CrSO₇²⁻ species were formed as a result of feeding HSO₄⁻. Cr(III) was confirmed as the final product due to the sequential three-electron transport or disproportionation of the intermediate. The developed BPNN model achieved good prediction accuracy with respect to Cr(VI) electroreduction with a high correlation coefficient (R² = 0.943). Additionally, the electroreduction efficiencies for various operating inputs were predicted based on the BPNN model, which demonstrates the evolutionary role of intelligent systems in future electrochemical technologies.

Graphical abstract

Keywords

Flow-through electrode / Hexavalent chromium / Heavy metals / Neural network / Artificial intelligence

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2019YFC0408202) and the National Natural Science Foundation of China (No. 21876050).

Data Accessibility Statement

The data and code that support the findings of this study are available from the corresponding author, Peng Chen, upon reasonable request.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1697-x and is accessible for authorized users.

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