Mathematical Model-Based Optimization of Trace Metal Dosage in Anaerobic Batch Bioreactors

Area of Science:

• Biotechnology
• Environmental Engineering
• Chemical Engineering

Background:

• Anaerobic digestion (AD) is a key waste-to-energy technology, but its complexity necessitates accurate modeling for optimization.
• Existing AD models often involve numerous parameters, posing calibration challenges.
• Trace metals play a crucial role in AD biochemical and physicochemical processes.

Purpose of the Study:

• To develop and validate an enhanced AD model incorporating trace metal influences.
• To investigate the impact of trace metal activities on AD process simulation and optimization.
• To determine optimal trace metal concentrations for improved biogas production.

Main Methods:

• Enhancement of the BioModel to include trace metal activities (Ca, K, Mg, Na, Co, Cr, Cu, Fe, Ni, Pb, Zn).
• Inclusion of trace metal-related parameters in model calibration.
• Numerical simulation and validation against experimental data for a batch bioreactor.
• Sensitivity analysis to assess the impact of parameter perturbations.

Main Results:

• Model validation confirmed reliability, with 5% parameter perturbations increasing simulation-experiment discrepancies up to threefold.
• Optimized trace metal concentrations significantly enhanced biogas and methane (CH4) production by 5.4% and 13.5%, respectively.
• Optimal conditions led to substantial reductions in H2, H2S, and NH3 by 28.2%, 43.6%, and 42.5%, respectively.

Conclusions:

• The enhanced BioModel accurately simulates AD processes, highlighting the critical role of trace metals.
• Precise optimization of trace metal additives is crucial for maximizing biogas quantity and quality.
• This approach offers a pathway to more efficient and effective waste-to-energy conversion through AD.