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Research Article Biofiltration of Hexane Vapor: Experimental and Neural Model AnalysisBiofiltration is a commonly practiced biological technique to remove volatile compounds from waste gas streams. From an industrial view-point, biofilter (BF) operation should be flexible to handle temperatures and inlet load (IL) variations. A compost BF was operated at different temperatures (30-458C) and at various inlet loading rates (ILR; 8-598 g m À3 h À1 ) under intermittent loading conditions. Complete removal of n-hexane was observed at 30 and 358C at ILRs up to 330 g m À3 h À1 . Besides, 20-75% of the pollutant was removed at 408C, corresponding to the different ILs applied to the BF. Increasing the temperature to 458C decreased the removal efficiency (RE) significantly. A feed forward neural network was used to predict the RE of BF with temperature and ILR as the input variables. The experimental data was divided into training (2/3) and test datasets (1/3). The best structure of neural network was obtained by trial and error on the basis of the least differences between predicted and experimental values, as ascertained from their coefficient of regression (R 2 ) values. The modeling resultsshowed that a multilayer network with the topology 2À10À1 was able to predict BF performance effectively with R 2 -value of 0.995 for the test data. The results from this study showed the predicting capability of ANNs which can be considered as an alternative for conventional knowledge-based models.

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