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Artificial Neural Network Modeling of an Inverse Fluidized Bed Bioreactor
Rajasimman, M.; Govindarajan, L. & Karthikeyan, C.
Abstract
The application of neural networks to model a laboratory scale inverse fluidized bed
reactor has been studied. A Radial Basis Function neural network has been successfully employed
for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural
network represents the kinetics of biological decomposition of organic matters in the reactor. The
neural network has been trained with experimental data obtained from an inverse fluidized bed
reactor treating the starch industry wastewater. Experiments were carried out at various initial
substrate concentrations of 2250, 4475, 6730 and 8910 mg COD/L and at different hydraulic retention
times (40, 32, 24, 26 and 8h). It is found that neural network based model has been useful in predicting
the system parameters with desired accuracy.
Keywords
Artificial neural network, Inverse fluidized bed, Radial basis, Starch, Modeling
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