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Anaerobic tapered fluidized bed reactor for starch wastewater treatment andmodeling using multilayer perceptron neural network
Authors:RANGASAMY Parthiban  PVR Iyer  GANESAN Sekaran
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
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.
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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