Using hypothesis testing on the mass-transfer effect with sulfate removal as an application | JoVE Visualize

Area of Science:

Environmental Engineering
Bioreactor Technology
Water Treatment

Background:

Acid mine drainage (AMD) poses significant environmental challenges, necessitating effective treatment strategies.
Existing models for AMD treatment in anaerobic reactors are often overly complex, hindering practical application.
Sulfate and chemical oxygen demand (COD) are key parameters in AMD, requiring efficient removal.

Purpose of the Study:

To evaluate the effectiveness of simplified mathematical models for describing sulfate and COD removal in an anaerobic batch reactor treating AMD.
To compare three models of increasing complexity: single planktonic population, double competing planktonic populations, and double competing granule-bound populations.
To assess the influence of mass transfer limitations on model performance over time.

Main Methods:

Application of maximum likelihood and chi-squared hypothesis tests to analyze experimental data from a 120-day AMD treatment operation.
Modeling sulfate-reducing bacteria and fermentative bacteria dynamics using Malthus-Monod kinetics.
Incorporation of Fick's law to account for inner granule diffusion, particularly in the granule-bound model.

Main Results:

A single planktonic population model was insufficient to describe the observed sulfate reduction dynamics.
The double competing planktonic population model failed to accurately represent the system at later stages (119 days).
The granule-bound double-competing population model, incorporating Fick's law, provided a fundamental description of the trend, especially as mass transfer effects became significant.

Conclusions:

Simplified models can effectively describe AMD treatment in anaerobic reactors, but complexity must increase to account for operational factors.
Mass transfer limitations, modeled using Fick's law, become increasingly important for accurate predictions in later stages of reactor operation.
The study highlights the need for models that balance simplicity with the ability to capture essential physical and biological processes in bioreactors.