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
Hauptverfasser:	Cheon, Ain, Sung, Jwakyung, Jun, Hangbae, Jang, Heewon, Kim, Minji, Park, Jungyu
Format:	Artikel
Sprache:	eng
Schlagworte:	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
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