Faculty Publications
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Item 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.Item Solar light mediated photocatalytic degradation of phenol using Ag core - TiO2 shell (Ag@TiO2) nanoparticles in batch and fluidized bed reactor(Elsevier Ltd, 2016) Shet, A.; Shetty K, K.V.Ag@TiO2 nanoparticles were synthesised using one pot method followed by calcination at 450 °C for 3 h and were tested for their photocatalytic efficacy in degradation of phenol both in free and immobilized form under solar light irradiation through batch experiments. Ag@TiO2 nanoparticles were found to be effective in solar photocatalytic degradation of phenol. The effect of factors such as pH, initial phenol concentration and catalyst loading on phenol degradation were evaluated and these factors were found to influence the process efficiency. The optimum values of these factors were determined to maximize the phenol degradation. The efficacy of nanoparticles immobilized on cellulose acetate film was inferior to that of free nanoparticles in solar photocatalysis due to light penetration problem and diffusional limitations. The performance of fluidized bed photocatalytic reactor operated under batch with recycle mode for solar photocatalysis of phenol with immobilized Ag@TiO2 nanoparticles was evaluated for large scale application. The performance was found to be dependent on catalyst loading and the optimum is governed by active catalyst sites and light penetration limitations. The photocatalytic degradation of phenol by Ag@TiO2 nanoparticles was only marginally influenced by the presence of small traces of chloride ions. Ag@TiO2 showed a better efficacy as solar photocatalyst than as UV photocatalyst in degradation of phenol. Solar light irradiation is recommended because solar energy, a readily available form of energy can be effectively harnessed for energy efficient, environment friendly and cost effective process. The kinetics of degradation of phenol was found to follow the nth order kinetics with order, n = 2.19 for solar photocatalysis. © 2016 Elsevier Ltd.