PMID- 37189261
OWN - NLM
STAT- MEDLINE
DCOM- 20231218
LR  - 20231218
IS  - 1520-5851 (Electronic)
IS  - 0013-936X (Linking)
VI  - 57
IP  - 46
DP  - 2023 Nov 21
TI  - Machine Learning Models for Inverse Design of the Electrochemical Oxidation 
      Process for Water Purification.
PG  - 17990-18000
LID - 10.1021/acs.est.2c08771 [doi]
AB  - In this study, a machine learning (ML) framework is developed toward 
      target-oriented inverse design of the electrochemical oxidation (EO) process for 
      water purification. The XGBoost model exhibited the best performances for 
      prediction of reaction rate (k) based on training the data set relevant to 
      pollutant characteristics and reaction conditions, indicated by R(ext)(2) of 0.84 
      and RMSE(ext) of 0.79. Based on 315 data points collected from the literature, 
      the current density, pollutant concentration, and gap energy (E(gap)) were 
      identified to be the most impactful parameters available for the inverse design 
      of the EO process. In particular, adding reaction conditions as model input 
      features allowed provision of more available information and an increase in the 
      sample size of the data set to improve the model accuracy. The feature importance 
      analysis was performed for revealing the data pattern and feature interpretation 
      by using Shapley additive explanations (SHAP). The ML-based inverse design for 
      the EO process was generalized to a random case for tailoring the optimum 
      conditions with phenol and 2,4-dichlorophenol (2,4-DCP) serving as model 
      pollutants. The resulting predicted k values were close to the experimental k 
      values by experimental verification, accounting for the relative error lower than 
      5%. This study provides a paradigm shift from conventional trial-and-error mode 
      to data-driven mode for advancing research and development of the EO process by a 
      time-saving, labor-effective, and environmentally friendly target-oriented 
      strategy, which makes electrochemical water purification more efficient, more 
      economic, and more sustainable in the context of global carbon peaking and carbon 
      neutrality.
FAU - Sun, Ye
AU  - Sun Y
AD  - State Key Laboratory of Urban Water Resource and Environment, School of 
      Environment, Harbin Institute of Technology, Harbin 150090, P. R. China.
FAU - Zhao, Zhiyuan
AU  - Zhao Z
AD  - State Key Laboratory of Urban Water Resource and Environment, School of 
      Environment, Harbin Institute of Technology, Harbin 150090, P. R. China.
FAU - Tong, Hailong
AU  - Tong H
AD  - State Key Laboratory of Urban Water Resource and Environment, School of 
      Environment, Harbin Institute of Technology, Harbin 150090, P. R. China.
AD  - State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research 
      Institute, Chinese Academy of Agricultural Sciences, Harbin 150069, P. R. China.
FAU - Sun, Baiming
AU  - Sun B
AD  - State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research 
      Institute, Chinese Academy of Agricultural Sciences, Harbin 150069, P. R. China.
FAU - Liu, Yanbiao
AU  - Liu Y
AUID- ORCID: 0000-0001-8404-3806
AD  - College of Environmental Science and Engineering, Textile Pollution Controlling 
      Engineering Center of the Ministry of Ecology and Environment, Donghua 
      University, Shanghai 201620, China.
FAU - Ren, Nanqi
AU  - Ren N
AD  - State Key Laboratory of Urban Water Resource and Environment, School of 
      Environment, Harbin Institute of Technology, Harbin 150090, P. R. China.
FAU - You, Shijie
AU  - You S
AUID- ORCID: 0000-0001-8178-9418
AD  - State Key Laboratory of Urban Water Resource and Environment, School of 
      Environment, Harbin Institute of Technology, Harbin 150090, P. R. China.
LA  - eng
PT  - Journal Article
DEP - 20230515
PL  - United States
TA  - Environ Sci Technol
JT  - Environmental science & technology
JID - 0213155
RN  - 7440-44-0 (Carbon)
RN  - 0 (Environmental Pollutants)
RN  - 0 (Phenols)
SB  - IM
MH  - Carbon
MH  - *Environmental Pollutants
MH  - Machine Learning
MH  - Oxidation-Reduction
MH  - Phenols
MH  - *Water Purification
OTO - NOTNLM
OT  - SHAP
OT  - XGBoost
OT  - electrochemical oxidation
OT  - inverse design
OT  - machine learning
OT  - reaction rate
EDAT- 2023/05/16 06:42
MHDA- 2023/12/18 06:41
CRDT- 2023/05/16 00:23
PHST- 2023/12/18 06:41 [medline]
PHST- 2023/05/16 06:42 [pubmed]
PHST- 2023/05/16 00:23 [entrez]
AID - 10.1021/acs.est.2c08771 [doi]
PST - ppublish
SO  - Environ Sci Technol. 2023 Nov 21;57(46):17990-18000. doi: 
      10.1021/acs.est.2c08771. Epub 2023 May 15.