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Electroreduction of hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a back propagation neural network 被引量:1
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作者 Xinwan Zhang guangyuan meng +4 位作者 Jinwen Hu Wanzi Xiao Tong Li Lehua Zhang Peng Chen 《Frontiers of Environmental Science & Engineering》 SCIE EI CSCD 2023年第8期69-79,共11页
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 ... 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. 展开更多
关键词 Flow-through electrode Hexavalent chromium Heavy metals Neural network Artificial intelligence
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