Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion
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-, reg...
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| Veröffentlicht in: | Processes 2022-01, Vol.10 (1), p.158 |
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| creator | Cheon, Ain Sung, Jwakyung Jun, Hangbae Jang, Heewon Kim, Minji Park, Jungyu |
| 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. |
| doi_str_mv | 10.3390/pr10010158 |
| format | Article |
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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. 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| subjects | Accuracy Algorithms Alkalinity Anaerobic digestion Anaerobic processes Biogas Chemical oxygen demand Efficiency Food waste Learning algorithms Machine learning Mathematical models Neural networks Parameters Performance prediction Predictions Regression analysis Stability Statistical analysis Variables |
| title | Application of Various Machine Learning Models for Process Stability of Bio-Electrochemical Anaerobic Digestion |
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