Biofiltration of Hexane Vapor: Experimental and Neural Model Analysis

Abstract

AbstractBiofiltration 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 (30–45°C) and at various inlet loading rates (ILR; 8–598 g m−3 h−1) under intermittent loading conditions. Complete removal of n‐hexane was observed at 30 and 35°C at ILRs up to 330 g m−3 h−1. Besides, 20–75% of the pollutant was removed at 40°C, corresponding to the different ILs applied to the BF. Increasing the temperature to 45°C 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 (R2) values. The modeling results showed that a multilayer network with the topology 2−10−1 was able to predict BF performance effectively with R2‐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.