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Retraining prior state performances of anaerobic digestion improves prediction accuracy of methane yield in various machine learning models
- Source :
- Applied Energy. 298:117250
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- The prediction of anaerobic digestion (AD) performance using numerical models, which are based on mathematics and kinetics, is being challenged by poor mechanistic understanding and the non-linear relationships between performance and operational parameters. This study demonstrated that various machine learning (ML) models using the 1-step ahead with the retraining method, which utilized AD performance data from prior states, can improve the prediction accuracy of ML models. For the four types of ML models studied, the 1-step ahead with the retraining method could improve the root mean square errors by 32–49% compared to the conventional multi-step ahead method, which was particularly noteworthy during the transition period when AD reactors were faced with loading shocks and showed inhibited methane yields. Moreover, the 1-step ahead with the retraining method showed the potential of achieving accurate predictions using a single input parameter, pH, which was considerably less labor-intensive to monitor than the other parameters often required in AD models (e.g., VSS). As such, the 1-step ahead with retraining method is suitable for efficient real-time prediction of AD performance in real-world operations.
- Subjects :
- Computer science
business.industry
020209 energy
Mechanical Engineering
Retraining
02 engineering and technology
Building and Construction
Numerical models
Management, Monitoring, Policy and Law
Machine learning
computer.software_genre
Methane yield
Anaerobic digestion
General Energy
020401 chemical engineering
0202 electrical engineering, electronic engineering, information engineering
Artificial intelligence
State (computer science)
0204 chemical engineering
business
computer
Subjects
Details
- ISSN :
- 03062619
- Volume :
- 298
- Database :
- OpenAIRE
- Journal :
- Applied Energy
- Accession number :
- edsair.doi...........c3b3bbe12a47e7639775d1b4716789f4
- Full Text :
- https://doi.org/10.1016/j.apenergy.2021.117250