Novel mathematical models for prediction of microbial growth kinetics and contaminant degradation in bioremediation process

    Azadeh Khoshdel Info
    Behrooz Mahmoodzadeh Vaziri Info
DOI: https://doi.org/10.3846/16486897.2016.1142446

Abstract

Bioremediation is defined as a process, which involves decomposition of organic pollutant compounds available in soil and water resources into safe and eco-friendly materials, like water and CO2, by the microorganisms.  In the present article, mathematical modeling of the bioremediation process was conducted comprehensively, and new models proposed for the microbial growth kinetics and substrate consumption (contaminant degradation). Accordingly, six kinetic models were suggested for the biomass growth and six models for the substrate consumption. Moreover, two models were considered for specific growth rate constant of the microorganisms. Then, model predictions were compared to and validated by the available experimental data in the literature. According to the obtained results, the microbial growth kinetic model, entitled as “MVKH2”, the substrate (contaminant) consumption model, entitled as “MVKH2s”, and the Aiba specific growth rate constant model had the best performance and the least error value in predicting the bioremediation process. Results achieved from this study are a promising beginning for practical and experimental works.

Keywords:

bioremediation, environmental processes modeling, simulation, microbial growth kinetic, contaminant degradation

How to Cite

Khoshdel, A., & Vaziri, B. M. (2016). Novel mathematical models for prediction of microbial growth kinetics and contaminant degradation in bioremediation process. Journal of Environmental Engineering and Landscape Management, 24(3), 157-164. https://doi.org/10.3846/16486897.2016.1142446

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August 19, 2016
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2016-08-19

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How to Cite

Khoshdel, A., & Vaziri, B. M. (2016). Novel mathematical models for prediction of microbial growth kinetics and contaminant degradation in bioremediation process. Journal of Environmental Engineering and Landscape Management, 24(3), 157-164. https://doi.org/10.3846/16486897.2016.1142446

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