A novel mechanistic modelling approach for microbial selection dynamics: Towards improved design and control of raceway reactors for purple bacteria


Reference:
A. Alloul, A. Moradvandi, D. Puyol, R. Molina, G. Gardella, S.E. Vlaeminck, B. De Schutter, E. Abraham, R.E.F. Lindeboom, and D.G. Weissbrodt, "A novel mechanistic modelling approach for microbial selection dynamics: Towards improved design and control of raceway reactors for purple bacteria," Bioresource Technology, vol. 390, p. 129844, Dec. 2023.

Abstract:
Purple phototrophic bacteria (PPB) show an underexplored potential for resource recovery from wastewater. Raceway reactors offer a more affordable full-scale solution on wastewater and enable useful additional aerobic processes. Current mathematical models of PPB systems provide useful mechanistic insights, but do not represent the full metabolic versatility of PPB and thus require further advancement to simulate the process for technology development and control. In this study, a new modelling approach for PPB that integrates the photoheterotrophic, and both anaerobic and aerobic chemoheterotrophic metabolic pathways through an empirical parallel metabolic growth constant was proposed. It aimed the modelling of microbial selection dynamics in competition with aerobic and anaerobic microbial community under different operational scenarios. A sensitivity analysis was carried out to identify the most influential parameters within the model and calibrate them based on experimental data. Process perturbation scenarios were simulated, which showed a good performance of the model.


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Bibtex entry:

@article{AllMor:23-006,
        author={A. Alloul and A. Moradvandi and D. Puyol and R. Molina and G. Gardella and S.E. Vlaeminck and B. {D}e Schutter and E. Abraham and R.E.F. Lindeboom and D.G. Weissbrodt},
        title={A novel mechanistic modelling approach for microbial selection dynamics: Towards improved design and control of raceway reactors for purple bacteria},
        journal={Bioresource Technology},
        volume={390},
        pages={129844},
        month=dec,
        year={2023},
        doi={10.1016/j.biortech.2023.129844}
        }



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