| Title |
Ensemble machine learning approach for examining critical process parameters and scale-up opportunities of microbial electrochemical systems for hydrogen peroxide production |
| ID_Doc |
8109 |
| Authors |
Chung, TH; Shahidi, M; Mezbahuddin, S; Dhar, BR |
| Title |
Ensemble machine learning approach for examining critical process parameters and scale-up opportunities of microbial electrochemical systems for hydrogen peroxide production |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.chemosphere.2023.138313 |
| Abstract |
Hydrogen peroxide (H2O2) production in microbial electrochemical systems (MESs) is an attractive option for enabling a circular economy in the water/wastewater sector. Here, a machine learning algorithm was developed, using a meta-learning approach, to predict the H2O2 production rates in MES based on the seven input variables, including various design and operating parameters. The developed models were trained and cross-validated using the experimental data collected from 25 published reports. The final ensemble meta-learner model (combining 60 models) demonstrated a high prediction accuracy with very high R2 (0.983) and low root-mean -square error (RMSE) (0.647 kg H2O2 m- 3 d-1) values. The model identified the carbon felt anode, GDE cathode, and cathode-to-anode volume ratio as the top three most important input features. Further scale-up analysis for small-scale wastewater treatment plants indicated that proper design and operating conditions could increase the H2O2 production rate to as high as 9 kg m- 3 d-1. |
| Author Keywords |
Machine learning; Meta -learning; Microbial electrochemical system; Microbial electrochemical technology; Hydrogen peroxide |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000962579900001 |
| WoS Category |
Environmental Sciences |
| Research Area |
Environmental Sciences & Ecology |
| PDF |
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