Predicting VFA formation by dark fermentation of particulate substrates.

Area of Science:

• Biochemical Engineering
• Environmental Science
• Renewable Energy

Background:

• Dark fermentation converts biomass into volatile fatty acids (VFAs) and hydrogen.
• Complex particulate substrates require hydrolysis before fermentation.
• Predictive models are crucial for optimizing VFA production processes.

Purpose of the Study:

• To develop and validate a mathematical model for predicting VFA production from complex particulate biomass during dark fermentation.
• To incorporate hydrolysis and fermentation kinetics into a unified model.
• To assess the model's accuracy using experimental data from cattle manure fermentation.

Main Methods:

• A surface-limiting rate equation was used to model the hydrolysis of cellulose and hemicellulose.
• End-product formation was modeled based on the Anaerobic Digestion Model 1 (ADM1).
• The model was calibrated and validated against experimental Chemical Oxygen Demand (COD) and VFA data from six batch reactors using cattle manure.

Main Results:

• The model demonstrated good agreement with the temporal trends of experimental data.
• High correlation coefficients were achieved for COD (r²=0.85) and acetic acid (r²=0.82).
• Satisfactory correlations were observed for butyric acid (r²=0.72), with moderate accuracy for propionic acid (r²=0.42).

Conclusions:

• The developed mathematical model effectively predicts VFA production from dark fermentation of particulate biomass.
• The model provides a valuable tool for understanding and optimizing VFA generation from lignocellulosic substrates.
• Further refinement may improve predictions for specific VFAs like propionic acid.