Intelligent system for automated monitoring of biomaterial quality for digestion in a biogas plant in Ukrainian

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An intelligent monitoring system enhances anaerobic digestion in biogas plants, indicating significant improvements in predictive model accuracy.

The automated monitoring system significantly improved predictive accuracy, with R² increasing from 0.85 to 0.91 after retraining.
During a 6-month testing period, the system collected 98.6% of planned measurements, showcasing its high reliability.
Data analysis utilized various technologies, including AWS cloud services and machine learning models like XGBoost and LSTM.
The predictive model showcased stability with a 42.6% reduction in RMSE after integrating 100 new operator-validated records.

Pavlov et al. (2025) studied this question.

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View Full Paper

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Authors

An intelligent monitoring system enhances anaerobic digestion in biogas plants, indicating significant improvements in predictive model accuracy.

The automated monitoring system significantly improved predictive accuracy, with R² increasing from 0.85 to 0.91 after retraining.
During a 6-month testing period, the system collected 98.6% of planned measurements, showcasing its high reliability.
Data analysis utilized various technologies, including AWS cloud services and machine learning models like XGBoost and LSTM.
The predictive model showcased stability with a 42.6% reduction in RMSE after integrating 100 new operator-validated records.

Pavlov et al. (2025) studied this question.

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