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- W2984961307 abstract "Abstract This study evaluated biodesulfuruization of sulfate containing wastewater using CO as the only carbon substrate in a gas lift bioreactor. The effect of hydraulic retention time (HRT), sulfate loading and CO loading rates on sulfate reduction and CO conversion was examined, and 72 h HRT proved to be best for achieving maximum sulfate reduction and CO utilization (97.2% and 88.9%, respectively). The CO utilization was nearly 80% at the beginning of the reactor operation, which reduced later due to increase in the inlet CO concentration in the third phase of bioreactor operation. Artificial neural network based model was successfully described to predict the performance of the system using Levenberg-Marquardt (LM) algorithm with twelve number of neurons. Steady state experimental values of sulfate reduction obtained using the gas lift bioreactor accurately matched well with the values predicted by the ANN model. Furthermore, addition of biologically synthesized iron nanoparticles using green tea extract significantly improved the bioreactor performance towards sulfate rich wastewater treatment with CO, particularly under high sulfate loading condition." @default.
- W2984961307 created "2019-11-22" @default.
- W2984961307 creator A5001003763 @default.
- W2984961307 creator A5055947342 @default.
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- W2984961307 date "2020-07-01" @default.
- W2984961307 modified "2023-10-15" @default.
- W2984961307 title "Process integration and artificial neural network modeling of biological sulfate reduction using a carbon monoxide fed gas lift bioreactor" @default.
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- W2984961307 doi "https://doi.org/10.1016/j.cej.2019.123518" @default.
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