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Modelling bioaugmentation: Engineering intervention in anaerobic digestion

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  • Lovato, Giovanna
  • Kovalovszki, Adam
  • Alvarado-Morales, Merlin
  • Arjuna Jéglot, Arnaud Tristan
  • Rodrigues, José Alberto Domingues
  • Angelidaki, Irini

Abstract

Bioaugmentation – the addition of specific microorganisms to microbial consortia to obtain specific functions - is a promising method to improve the performance of sub-optimal anaerobic digestion processes and recover methane production. The present study implements for the first time a model with bioaugmentation functionalities, enabling the dynamic microbiological study of augmented anaerobic systems. The model is based on a previous advanced mathematical model focused on ammonia inhibition, which was extended. Three inhibited anaerobic digestion experiments were simulated, where bioaugmentation involved the addition of: (i) hydrogenotrophic methanogenic archaea; (ii) a combination of hydrogenotrophs and syntrophic acetate oxidizing bacteria; and (iii) acetoclastic methanogenic archaea to the inhibited reactors. Methane productivity, pH, and ammonia were correctly predicted by the model, with weighted absolute percentage errors below 10%. Trends in volatile fatty acids accumulation and consumption showed good agreement between in silico and in vivo data. Simulations revealed that bioaugmentation will result in process improvement when specialized hydrogenotrophs are used: not only do they establish themselves in the inhibited environment, but also help in the recovery of acetoclastic methanogens. Overall, the mathematical approach adopted to describe the bioaugmentation phenomena resulted in a robust computational tool for simulation of general bioaugmentation strategies.

Suggested Citation

  • Lovato, Giovanna & Kovalovszki, Adam & Alvarado-Morales, Merlin & Arjuna Jéglot, Arnaud Tristan & Rodrigues, José Alberto Domingues & Angelidaki, Irini, 2021. "Modelling bioaugmentation: Engineering intervention in anaerobic digestion," Renewable Energy, Elsevier, vol. 175(C), pages 1080-1087.
    • Handle: RePEc:eee:renene:v:175:y:2021:i:c:p:1080-1087
    DOI: 10.1016/j.renene.2021.04.096
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    References listed on IDEAS

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    1. Shah, Fayyaz Ali & Mahmood, Qaisar & Rashid, Naim & Pervez, Arshid & Raja, Iftikhar Ahmad & Shah, Mohammad Maroof, 2015. "Co-digestion, pretreatment and digester design for enhanced methanogenesis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 627-642.
    2. Tsai, Sang-Bing & Xue, Youzhi & Zhang, Jianyu & Chen, Quan & Liu, Yubin & Zhou, Jie & Dong, Weiwei, 2017. "Models for forecasting growth trends in renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1169-1178.
    3. Yuan, Haiping & Zhu, Nanwen, 2016. "Progress in inhibition mechanisms and process control of intermediates and by-products in sewage sludge anaerobic digestion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 429-438.
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    Cited by:

    1. Maria-Athina Tsitsimpikou & Sotirios D. Kalamaras & Antonios A. Lithourgidis & Anastasios Mitsopoulos & Lars Ellegaard & Irini Angelidaki & Thomas A. Kotsopoulos, 2023. "Simulation of the Working Volume Reduction through the Bioconversion Model (BioModel) and Its Validation Using Biogas Plant Data for the Prediction of the Optimal Reactor Cleaning Period," Sustainability, MDPI, vol. 15(23), pages 1-19, November.
    2. Emebu, Samuel & Pecha, Jiří & Janáčová, Dagmar, 2022. "Review on anaerobic digestion models: Model classification & elaboration of process phenomena," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Tsapekos, Panagiotis & Lovato, Giovanna & Domingues Rodrigues, José Alberto & Alvarado-Morales, Merlin, 2024. "Amendments to model frameworks to optimize the anaerobic digestion and support the green transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    4. Kegl, Tina, 2024. "Anaerobic digestion BioModel upgraded by various inhibition types," Renewable Energy, Elsevier, vol. 226(C).

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