文献类型：期刊文献

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

作者：Zhang, Xinwan[1];Meng, Guangyuan[1];Hu, Jinwen[1];Xiao, Wanzi[1];Li, Tong[2];Zhang, Lehua[1,3,4];Chen, Peng[1]

机构：[1]East China Univ Sci & Technol, Sch Resources & Environm Engn, Natl Engn Lab Ind Wastewater Treatment, Shanghai 200237, Peoples R China;[2]Bozhou Univ, Continuing Educ Ctr, Bozhou 236800, Peoples R China;[3]East China Univ Sci & Technol, State Environm Protect Key Lab Environm Risk Asses, Shanghai 200237, Peoples R China;[4]Shanghai Inst Pollut Control & Ecol Secur, Shanghai 200092, Peoples R China

年份：2023

卷号：17

期号：8

外文期刊名：FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING

收录：;EI(收录号：20231113740340);WOS:【SCI-EXPANDED(收录号:WOS:000943531100001)】；

基金：AcknowledgementsThis 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).

语种：英文

外文关键词：Flow-through electrode; Hexavalent chromium; Heavy metals; Neural network; Artificial intelligence

摘要：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 center dot m(2)), a reduction efficiency of similar to 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 (R-2 = 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.