Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Srinikethan, G.Subject: search.filters.subject.Degradation

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    Biological phenol removal using immobilized cells in a pulsed plate bioreactor: Effect of dilution rate and influent phenol concentration
    (2007) Shetty K, K.V.; Ramanjaneyulu, R.; Srinikethan, G.
    The continuous aerobic biodegradation of phenol in synthetic wastewater was carried out using Nocardia hydrocarbonoxydans immobilized over glass beads packed between the plates in a pulsed plate bioreactor at a frequency of pulsation of 0.5 s-1 and amplitude of 4.7 cm. The influence of dilution rate and influent phenol concentration on start up and steady state performance of the bioreactor was studied. The time taken to reach steady state has increased with increase in dilution rate and influent phenol concentration. It was found that, as the dilution rate is increased, the percentage degradation has decreased. Steady state percentage degradation was also reduced with increased influent phenol concentration. Almost 100% degradation of 300 and 500 ppm influent phenol could be achieved at a dilution rate of 0.4094 h-1 and more than 99% degradation could be achieved with higher dilution rates. At a higher dilution rate of 1.0235 h-1 and at concentrations of 800 and 900 ppm the percentage degradation has reduced to around 94% and 93%, respectively. The attached biomass dry weight, biofilm thickness and biofilm density at steady state were influenced by influent phenol concentration and dilution rate. © 2007 Elsevier B.V. All rights reserved.
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    Artificial neural networks model for the prediction of steady state phenol biodegradation in a pulsed plate bioreactor
    (2008) Shetty K, K.V.; Nandennavar, S.; Srinikethan, G.
    Background: A recent innovation in fixed film bioreactors is the pulsed plate bioreactor (PPBR) with immobilized cells. The successful development of a theoretical model for this reactor relies on the knowledge of several parameters, which may vary with the process conditions. It may also be a time-consuming and costly task because of their nonlinear nature. Artificial neural networks (ANN) offer the potential of a generic approach to the modeling of nonlinear systems. Results: A feedforward ANN based model for the prediction of steady state percentage degradation of phenol in a PPBR by immobilized cells of Nocardia hydrocarbonoxydans (NCIM 2386) during continuous biodegradation has been developed to correlate the steady state percentage degradation with the flow rate, influent phenol concentration and vibrational velocity (amplitude x frequency). The model used two hidden layers and 53 parameters (weights and biases). The network model was then compared with a Multiple Regression Analysis (MRA) model, derived from the same training data. Further these two models were used to predict the percentage degradation of phenol for blind test data. Conclusions: The performance of the ANN model was superior to that of the MRA model and was found to be an efficient data-driven tool to predict the performance of a PPBR for phenol biodegradation. © 2008 Society of Chemical Industry.
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    Modelling and simulation of steady-state phenol degradation in a pulsed plate bioreactor with immobilised cells of Nocardia hydrocarbonoxydans
    (2011) Shetty K, V.S.; Verma, D.K.; Srinikethan, G.
    A novel bioreactor called pulsed plate bioreactor (PPBR) with cell immobilised glass particles in the interplate spaces was used for continuous aerobic biodegradation of phenol present in wastewater. A mathematical model consisting of mass balance equations and accounting for simultaneous external film mass transfer, internal diffusion and reaction is presented to describe the steady-state degradation of phenol by Nocardia hydrocarbonoxydans (Nch.) in this bioreactor. The growth of Nch. on phenol was found to follow Haldane substrate inhibition model. The biokinetic parameters at a temperature of 30 ± 1 °C and pH at 7.0 ± 0.1 are ? m = 0.5397 h -1, K S = 6.445 mg/L and K I = 855.7 mg/L. The mathematical model was able to predict the reactor performance, with a maximum error of 2% between the predicted and experimental percentage degradations of phenol. The biofilm internal diffusion rate was found to be the slowest step in biodegradation of phenol in a PPBR. © 2010 Springer-Verlag.