Electrochemical mechanism of synchronous ammonia and nitrate removal based on multi-objective optimization by coupling random forest with genetic algorithm

Ye Tian; Shuo Wang; Luowei Pei; Kaisheng Zhang; Songming Zhu; Hao Xu; Zhangying Ye

doi:10.1016/j.scitotenv.2023.166039

Electrochemical mechanism of synchronous ammonia and nitrate removal based on multi-objective optimization by coupling random forest with genetic algorithm

Sci Total Environ. 2023 Nov 25:901:166039. doi: 10.1016/j.scitotenv.2023.166039. Epub 2023 Aug 4.

Authors

Ye Tian¹, Shuo Wang¹, Luowei Pei¹, Kaisheng Zhang¹, Songming Zhu², Hao Xu³, Zhangying Ye⁴

Affiliations

¹ College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China.
² College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Ocean Academy, Zhejiang University, Zhoushan 316021, PR China.
³ Department of Environmental Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.
⁴ College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, PR China; Ocean Academy, Zhejiang University, Zhoushan 316021, PR China. Electronic address: yzyzju@zju.edu.cn.

PMID: 37543319
DOI: 10.1016/j.scitotenv.2023.166039

Abstract

In this work, an electrochemical system was constructed for the simultaneous elimination of ammonia and nitrate using the prepared Ti foam/SnO₂-Sb anode and a Cu foam cathode. The hybrid RF-GA method is proposed as a tool for the analysis and optimization of the simultaneous removal of ammonia and nitrate. The influence of independent variables including NaCl concentration, time, and current densities was studied. Results showed that the random forest (RF) model could successfully predict the behavior of electrochemical systems (R² = 0.9751, RMSE = 0.4567 for the ammonia prediction model; R² = 0.9772, RMSE = 0.0436 for the nitrate prediction model). The variable importance measures (VIM) analysis reveals that time has the maximum influence on the degradation rate of ammonia and nitrate. The RF model is used as an objective function for the genetic algorithm (GA) to determine the optimum conditions in combination with the calculated specific energy consumption. Based on the optimization results, the removal rates of ammonia and nitrate reach 94.4 % and 74.7 %, respectively, with a minimum specific energy consumption of 0.181 kwh·g^-1. The electrochemical reaction mechanism of the composite pollutants in the Ti foam/SnO₂-Sb and Cu foam electrode system is further elucidated. The results indicate that nitrate is reduced to nitrite, ammonia, or nitrogen gas at the cathode, accompanied by the mutual transformation of Cu⁽⁰⁾, Cu^(I), and Cu^(II) on the Cu electrode. Ammonia is oxidized to nitrogen gas or nitrate at the anode. Ultimately, the nitrogen-containing composite pollutant is decomposed and discharged as nitrogen gas by cyclic redox reactions.

Keywords: Ammonia; Genetic algorithm; Multi-objective optimization; Nitrate; Random forest.