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Modeling bioaugmentation with nitrifiers in membrane bioreactors.

Alberto Mannucci1, Giulio Munz2, Gualtiero Mori3

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Bioaugmentation efficiency in wastewater treatment is affected by temperature differences between bioreactors. Standard models overestimate efficiency when temperature is lower in seeded reactors, necessitating model adjustments for accurate predictions.

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Area of Science:

  • Environmental microbiology
  • Wastewater treatment technologies
  • Bioreactor engineering

Background:

  • Bioaugmentation introduces specialized microorganisms to enhance wastewater treatment processes.
  • Activated sludge models (ASMs) are crucial for simulating and optimizing biological wastewater treatment.
  • Understanding factors influencing bioaugmentation efficiency is key to improving treatment plant performance.

Purpose of the Study:

  • To evaluate the predictive capability of current activated sludge models (ASMs) for bioaugmentation efficiency.
  • To assess the impact of operating conditions, specifically temperature differences, on bioaugmentation success.
  • To identify limitations of standard ASMs in scenarios with significant temperature gradients.

Main Methods:

  • Utilized two pilot-scale membrane bioreactors for bioaugmentation experiments with nitrifying bacteria.
  • Systematically varied operating conditions, focusing on temperature differences between seeding and seeded reactors (ΔT).
  • Compared experimental bioaugmentation efficiency data against predictions from state-of-the-art ASMs.

Main Results:

  • Bioaugmentation efficiency was significantly influenced by the temperature difference (ΔT) between seeding and seeded reactors.
  • Standard ASMs accurately predicted experimental data when ΔT was up to 10 °C.
  • ASMs overestimated bioaugmentation efficiency when the seeded reactor was significantly cooler than the seeding reactor.

Conclusions:

  • Standard ASMs require modification to accurately predict bioaugmentation efficiency under high temperature gradients (ΔT).
  • Future models should incorporate the effect of temperature time gradients on nitrifying biomass.
  • A linear correlation between ΔT and the Arrhenius coefficient is proposed as a starting point for model enhancement.