Volume 10 Issue 1
Feb.  2025
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Article Navigation >  Journal of Bioresources and Bioproducts  > 2025  >  10(1): 32-50
S M Hasan Shahriar Rahat, Oluwatunmise Israel Dada, Liang Yu, Helmut Kirchhoff, Shulin Chen. Anaerobic digestion bacteria algae (ADBA): A mathematical model of mixotrophic algal growth with indigenous bacterial inhibition in anaerobic digestion effluent[J]. Journal of Bioresources and Bioproducts, 2025, 10(1): 32-50. doi: 10.1016/j.jobab.2024.12.004
Citation: S M Hasan Shahriar Rahat, Oluwatunmise Israel Dada, Liang Yu, Helmut Kirchhoff, Shulin Chen. Anaerobic digestion bacteria algae (ADBA): A mathematical model of mixotrophic algal growth with indigenous bacterial inhibition in anaerobic digestion effluent[J]. Journal of Bioresources and Bioproducts, 2025, 10(1): 32-50. doi: 10.1016/j.jobab.2024.12.004
shu

Anaerobic digestion bacteria algae (ADBA): A mathematical model of mixotrophic algal growth with indigenous bacterial inhibition in anaerobic digestion effluent

doi: 10.1016/j.jobab.2024.12.004
  • a.

    Department of Biological Systems Engineering, Washington State University, Pullman, WA 99164-6120, USA

  • b.

    Institute of Biological Chemistry, Washington State University, Pullman, WA 99164-7411, USA

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  • Corresponding author: E-mail address: yuliang08@wsu.edu (L. Yu); E-mail address: chens@wsu.edu (S. Chen)
  • Available Online: 2024-12-20
  • Publish Date: 2025-02-01
    Abstract
  • A comprehensive kinetic model called anaerobic digestion bacteria algae (ADBA) was developed to describe and predict the complex algae-bacterial system in anaerobic digestion (AD) wastewater under mixotrophic growth conditions. The model was calibrated and validated using the experimental growth data from cultivating the algae (Chlorella vulgaris CA1) with its indigenous bacteria in Blue Green 11 (BG-11) media and different combinations of sterilized, diluted, and raw AD effluent. Key parameters were obtained, including the distinct maximum growth rate of algae on CO2 (µa, CO2, 1.23 per day) and organic carbon (µa, OC, 3.30 per day), the maximum growth rate of bacteria (µb, 1.20 per day), along with two noble parameters, i.e., the algae-bacteria interaction exponent (n, 0.03) and the growth inhibition coefficient (ae = 30 000 mg/L per AU) due to effluent turbidity. The model showed a good fit with an average R2 = 0.90 in all cases adjusted with 25 kinetic parameters. This was the first model capable of predicting algal and bacterial growth in AD effluent with their competitive interactions, assuming shifting growth modes of algae on organic and inorganic carbon under light. It could also predict the removal rate of substrate and nutrients from effluent, light inhibition due to biomass shading and effluent turbidity, mass transfer rate of O2 and CO2 from gas phase to liquid phase, and pH-driven equilibrium between dissolved inorganic carbon components (CO2, HCO3–, and CO32–). Algal growth in the strongly buffered AD effluent resulted in odor removal, turbidity reduction, and the removal of ~80% of total ammonium-nitrogen and 90% of organic carbon. In addition to process parameter prediction, this study offered a practical solution to wastewater treatment, air pollution, and nutrient recycling, ensuring a holistic and practical approach to ecological balance.

     

    • The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
    Declaration of competing interest
    Supplementary materials
    Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jobab.2024.12.004.
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