Simulation of integrated anaerobic digestion-gasification systems using machine learning models.

Area of Science:

• Biomass energy conversion
• Sustainable waste management
• Environmental engineering

Background:

• Biomass anaerobic digestion (AD) and gasification (GS) are key sustainable energy technologies.
• Integrating AD and GS offers synergistic benefits for waste valorization and energy production.
• Carbon emission is a critical factor in evaluating the efficiency of integrated biomass processes.

Purpose of the Study:

• To develop and validate integrated models for biomass anaerobic digestion and gasification (AD-GS) technology.
• To evaluate the impact of anaerobic digestion duration on carbon emission reduction in the AD-GS process.
• To identify optimal operating conditions for maximizing carbon emission reduction and energy generation.

Main Methods:

• Integration of the M-ADM1 anaerobic digestion model with the T-ANN gasification model.
• Simulation of the AD-GS process using biogas slurry and solid residue as feedstocks.
• Analysis of carbon emission reduction and FEAG (Full Energy Added Gasification) rate changes over varying anaerobic digestion durations.

Main Results:

• The integrated AD-GS model efficiently simulates biomass AD-GS integration technology.
• Carbon emission reduction increased rapidly between 0 and 15 days of anaerobic digestion, peaking at 0.1828 gCO2eq on day 15.
• The maximum FEAG value was observed at 15 days, after which the FEAG rate change began a significant decreasing trend.

Conclusions:

• A 15-day anaerobic digestion duration is optimal for maximizing carbon emission reduction in the integrated AD-GS process.
• The integrated AD-GS model provides a valuable tool for optimizing biomass energy systems.
• This research contributes to developing more sustainable and efficient biomass conversion pathways.