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Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion
| Content Provider | MDPI |
|---|---|
| Author | Cheon, Ain Sung, Jwakyung Jun, Hangbae Jang, Heewon Kim, Minji Park, Jun Gyu |
| Copyright Year | 2022 |
| Description | The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models. |
| Starting Page | 158 |
| e-ISSN | 22279717 |
| DOI | 10.3390/pr10010158 |
| Journal | Processes |
| Issue Number | 1 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-01-14 |
| Access Restriction | Open |
| Subject Keyword | Processes Machine Learning Bio-electrochemical Anaerobic Digestion Methane Yield Process Stability |
| Content Type | Text |
| Resource Type | Article |
