Anaerobic tapered fluidized bed reactor for starch wastewater treatment andmodeling using multilayer perceptron neural network |
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Authors: | RANGASAMY Parthiban PVR Iyer GANESAN Sekaran |
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Affiliation: | 1. Department of Chemical Engineering, Sri Venkateswara College of Engineering, Sriperumbudur 602 105, Tamilnadu, India 2. Department of Environmental Technology, Central Leather Research Institute, Chennai 600 025, Tamilnadu, India |
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Abstract: | Anaerobic treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor (ATFBR) with a mesoporous granular activated carbon (GAC) as a support material. The experimental protocol was defined to examine the effect of the maximum organic loading rate (OLR), hydraulic retention time (HRT), the efficiency of the reactor and to report on its steady-state performance. The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/(m3·d). The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m3/(m3·d) of the reactor. With the increase of OLR from 83.7 kg COD/(m3·d), the COD removal efficiency decreases. Also an artificial neural network (ANN) model using multilayer perceptron (MLP) has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data, the experimental values obtained have been used. The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data. The mean square error (MSE) was found to be only 0.0146. |
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Keywords: | anaerobic digestion tapered fluidized bed reactor organic loading rate biogas mesoporous granular activated carbon modeling artificial neural network artificial neural network multilayer perceptron modeling wastewater treatment starch fluidized bed reactor mean square error parameters experimental values used training data system input variables output dependent decreases increase |
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