10th U6+ Consortium of African Universities International Conference

Anaerobic digestion is a proven technology for renewable energy production. However, its efficiency is often limited by poor hydrodynamic mixing within the digester, which results in dead zones, reduced mass transfer, and uneven substrate distribution, thus ultimately lowering methane production. This study therefore investigates the influence of hydrodynamics on mixing efficiency and bio-methane production potential using an approach of mathematical modelling. A two-dimensional incompressible flow model based on the Navier–Stokes equations was adopted and coupled with a tracer advection–diffusion equation to simulate slurry flow and mixing behavior. The two governing equations were non-dimensionalised using Reynolds and Péclet numbers respectively there after a discretization was done using the finite difference method.
Numerical simulations were then implemented using MATLAB. An optimization framework was integrated into the system of equations, by treating inlet velocity as the control variable and evaluating mixing efficiency through concentration distribution and dead zone analysis. The simulation results indicate that at a Reynolds number of 2100, the flow exhibits transitional characteristics with incomplete mixing and a significant dead zone fraction of approximately 31.6%. Velocity contours reveal limited circulation, while tracer distribution shows non-uniform concentration patterns across the domain. As the Reynolds number increases, flow circulation improves, resulting in enhanced mixing, reduced stagnation regions, and more uniform tracer distribution. The results further demonstrate that transport is predominantly advection-driven at higher Péclet numbers, leading to improved dispersion of tracer within the digester. In conclusion, the study confirms that hydrodynamic conditions play a critical role in determining mixing efficiency and, consequently, bio-methane production potential. Increasing inlet velocity enhances mixing performance by reducing dead zones and improving substrate distribution. The developed model provides a computationally efficient framework for analyzing and optimizing anaerobic digester performance and offers a foundation for future integration with biochemical reaction models.