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

doi:10.1007/s11783-023-1697-x

Front. Environ. Sci. Eng.

2023, Vol. 17

Issue (8) : 97 https://doi.org/10.1007/s11783-023-1697-x

RESEARCH ARTICLE

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

Xinwan Zhang¹, Guangyuan Meng¹, Jinwen Hu¹, Wanzi Xiao¹, Tong Li², Lehua Zhang^1,^3,⁴, Peng Chen¹(

)

¹. National Engineering Laboratory for Industrial Wastewater Treatment, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai 200237, China
². Continuing Education Center, Bozhou University, Bozhou 236800, China
³. State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, East China University of Science and Technology, Shanghai 200237, China
⁴. Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China

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Abstract

● 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.

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.

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

Corresponding Author(s): Peng Chen

About author:

Changjian Wang and Zhiying Yang contributed equally to this work.

Issue Date: 01 March 2023

Cite this article:

Xinwan Zhang,Guangyuan Meng,Jinwen Hu, et al. Electroreduction of hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a back propagation neural network[J]. Front. Environ. Sci. Eng., 2023, 17(8): 97.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-023-1697-x
https://academic.hep.com.cn/fese/EN/Y2023/V17/I8/97

Fig.1 (a) Schematic of experimental setup for electroreduction of Cr(VI) using flow-through electrode system; (b) schematic of effluent modes.

Tab.1 Parameters for the back propagation neural network (BPNN) model

Fig.2 Study on Cr(VI) electroreduction by Ti electrode. (a) Kinetic study on Cr(VI) reduction in flow-through mode and parallel-plate mode. Electrolysis conditions: applied voltage of 5.0 V, initial pH 2.0, 106 mg/L Cr(VI), 50 mmol/L Na₂SO₄; (b) CV for Ti electrode in pH 2.0 and 50 mmol/L Na₂SO₄ solution. Black line refers to solution without the addition of Cr(VI); red line refers to solution with the addition of Cr(VI) (106 mg/L); (c) CV for Ti electrode in pH 2.0 and 106 mg/L Cr(VI) solution containing 50 mmol/L Na₂SO₄; (d) LSV for porous Ti electrode in flow-through mode at a scan rate of 5.0 mV/s and flux of 0–127 L/(h·m²): test solution 18 mg/L Cr(VI) in 0.1 mol/L H₂SO₄.

Fig.3 Cr(VI) reduction mechanism by flow-through electrode. (a) CV for Ti electrode in 106 mg/L Cr(VI) and 50 mmol/L Na₂SO₄ solution at pH 1.0, pH 2.0, pH 3.0: pH varied by additions of H₂SO₄; (b) CV for Ti electrode in 106 mg/L Cr(VI) solution at pH 1.0: pH created by additions of either H₂SO₄, HNO₃, or HCl; (c) relationship between the flux and rate constant for mass transfer in flow-through electrode system; (d) schematic for Cr(VI) electroreduction by parallel-plate and flow-through modes.

Tab.2 Species distribution of Cr(VI) in Cr(VI) solution (106 mg/L) at various pH and in the presence of various anions

Fig.4 Single-pass flow-through test for Cr(VI) reduction using flow-through electrode. Effect of experimental parameters on the reduction performance for Cr(VI) at different (a) effluent modes, (b) initial pH, (c) flux, (d) applied voltage, (e) initial Cr(VI) concentration, and (f) long-term stability.

Fig.5 (a) Schematic representation of a back propagation neural network; (b) Measured and BPNN-predicted reduction efficiencies for Cr(VI); (c) Correlation of measured and BPNN-predicted reduction efficiencies for Cr(VI) in this study; Effect of pH (d), effect of applied voltage (e), effect of influent flux (f) predicted by BPNN.

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