Dynamic simulation and optimization of anaerobic digestion processes using MATLAB.

Area of Science:

• Biochemical Engineering
• Process Modeling
• Renewable Energy

Background:

• Time series modeling is crucial for understanding anaerobic digestion (AD) but is underrepresented in current literature.
• Accurate predictive models for biomethane production are needed for process optimization and industrial application.

Purpose of the Study:

• To develop a dynamic, time series-based model for predicting biomethane production in anaerobic digestion.
• To validate the model's accuracy and robustness across various conditions and operational modes (batch and continuous).
• To demonstrate the model's utility in identifying optimal loading rates for stable methane yield.

Main Methods:

• Development of a dynamic model using a modified Hill's model in MATLAB.
• Time series analysis to predict biomethane production.
• Validation against literature data and statistical analysis to confirm model accuracy.

Main Results:

• The developed model accurately predicts biomethane production for both batch and continuous AD processes.
• Model predictions showed a deviation of less than ±7.6% compared to literature data.
• Statistical analysis confirmed no significant difference between model simulations and existing literature, supporting model robustness.

Conclusions:

• The developed dynamic time series model offers a reliable tool for predicting biomethane production in anaerobic digestion.
• The model facilitates the identification of stable and optimal loading rates, reducing the need for extensive experimental trials.
• This approach enhances the industrial applicability of anaerobic digestion by enabling data-driven process optimization.