چكيده لاتين :
Biofiltration has shown to be a promising technique for handling malodours arising from process
industries. The present investigation pertains to the removal of hydrogen sulphide in a lab scale biofilter packed with
biomedia, encapsulated by sodium alginate and poly vinyl alcohol. The experimental data obtained under both steady
state and shock loaded conditions were modelled using the basic principles of artificial neural networks. Artificial neural
networks are powerful data driven modelling tools which has the potential to approximate and interpret complex input!
output relationships based on the given sets of data matrix. A predictive computerised approach has been proposed to
predict the performance parameters namely, removal efficiency and elimination capacity using inlet concentration,
loading rate, flow rate and pressure drop as the input parameters to the artificial neural network model. Earlier,
experiments from continuous operation in the biofilter showed removal efficiencies from 50 to 100 % at inlet loading
rates varying up to 13 g H2S/m
3h. The internal network parameter of the artificial neural network model during
simulation was selected using the 2k factorial design and the best network topology for the model was thus estimated.
The results showed that a multilayer network (4-4-2) with a back propagation algorithm was able to predict biofilter
performance effectively with R2 values of 0.9157 and 0.9965 for removal efficiency and elimination capacity in the test
data. The proposed artificial neural network model for biofilter operation could be used as a potential alternative for
knowledge based models through proper training and testing of the state variables.
