State estimation for anaerobic digesters using the ADM1.

Area of Science:

• Biotechnology
• Renewable Energy Engineering
• Process Control

Background:

• Optimizing full-scale biogas plant operation is crucial for competitive renewable energy production from biomass.
• Implementing advanced control strategies like Nonlinear Model Predictive Control necessitates accurate online state estimation.
• Current methods lack efficient real-time state estimation for biogas plant optimization.

Purpose of the Study:

• To develop a state estimator for full-scale biogas plants using the Anaerobic Digestion Model No.1.
• To enable optimal control and operating decisions based on real-time plant conditions.
• To facilitate the application of state-based optimization and control algorithms.

Main Methods:

• Developed a calibrated simulation model of a full-scale biogas plant based on the Anaerobic Digestion Model No.1.
• Employed advanced pattern recognition and machine learning methods for state prediction.
• Trained and evaluated models using synthetic data simulating diverse operating conditions.

Main Results:

• Model states were predicted from basic online measurements including biogas production, CH4/CO2 content, pH, and substrate feed.
• Machine learning models achieved an overall prediction accuracy of approximately 90% for the operating state vector.
• The developed estimator successfully predicts key operational parameters.

Conclusions:

• The developed state estimator effectively predicts biogas plant operating states using readily available online measurements.
• This facilitates the implementation of advanced, state-based optimization and control strategies.
• The research promotes eco-friendly energy production from biomass by enhancing biogas plant efficiency.