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Unstructured kinetic models with time-averaged growth approach in the biodegradation of triclosan by activated sludge culture
Alexandria Engineering Journal ; 67:503-511, 2023.
Article in English | ScienceDirect | ID: covidwho-2164958
ABSTRACT
The concentration of triclosan in wastewater is expected to rise dramatically as a consequence of the COVID-19 pandemic. A modeling analysis of the growth kinetics of microbial culture during triclosan degradation was necessary in order to establish effective wastewater treatment. The kinetic parameters are used by engineers to aid in the design and process control of biological processes. Studies were conducted using triclosan-acclimated culture to examine biomass growth and associated substrate degradation at different initial substrate concentrations (0.35–4.9 mg L−1) to this end. Substrate inhibition was calculated from experimental growth parameters using unstructured kinetic models. Unlike other model studies, a time-averaged specific bacterial growth rate in the log phase was considered for kinetic models in this study. Overestimation of the conventional log phase calculation for unstructured kinetic model constants was eliminated when the slowdown growth part of the log growth phase was taken into account. The Haldane Model was more accurate to fitting experimental data in an excellent manner. In this case, the time-averaged specific growth rate, saturation constant, and inhibition constant were 0.56 h−1, 12.77 mg L−1, 0.52 mg L−1, respectively. A yield coefficient of 0.404 mgX.mgS−1 was calculated. The critical triclosan concentration was 2.57 mg L−1. Wastewater treatment plants can be more sensitive to the value of the critical triclosan concentration. The value for time-averaged critical specific growth rate was 0.051 h−1. Pre-or post-treatment requirements can be estimated using time-averaged critical growth rate values as a benchmarking tool in biological treatment systems.
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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Language: English Journal: Alexandria Engineering Journal Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ScienceDirect Language: English Journal: Alexandria Engineering Journal Year: 2023 Document Type: Article