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Biofiltration is emerging as a promising cost effective technique for the Volatile Organic Compounds (VOCs) removal from industrial waste gases. In the present investigation a comparative modeling study has been carried out using Radial Basis Function Neural Network (RBFN) and Response Surface Methodology (RSM) to predict and optimize the performance of a biofilter system treating toluene (a model VOC). Experimental biofilter system performance data collected over a time period by daily measurement of inlet VOC concentration, retention time, pH, temperature and packing moisture content was used to develop the mathematical model. These independent variables acted as the inputs to the mathematical model developed using RSM and RBFN, while the VOC removal efficiency was the biofilter system performance parameter to be predicted. The data set was divided into two parts: 60% of data was used for training phase and remaining 40% of data was used for the testing phase. The average % error for RSM and RBFN were 7.76% and 3.03%, and R 2 value obtained were 0.8826 and 0.9755 respectively. The results indicated the superiority of RBFN in the prediction capability due to its ability to approximate higher degree of non-linearity between the input and output variables. The optimization of biofilter parameters was also done using RSM to optimize the biofilter performance. RSM being structured in nature enabled the study of interaction effect between the independent variables on biofilter performance.

The present work describes the biofiltration of mixture of n-propanol (as a model hydrophilic volatile organic compound (VOC)) and toluene (as a model hydrophobic VOC) in a biofilter packed with a compost-woodchip mixture. Initially, the biofilter was fed with toluene vapours at loadings up to 175 g m(-3) h(-1) and removal efficiencies of 70%-99% were observed. The biofilter performance when removing mixtures of toluene and n-propanol reached elimination capacities of up to 67g(toluene) m(-3) h(-1) and 85 g(n-propanol) m(-3) h(-1) with removal efficiencies of 70%-100% for toluene and essentially 100% for n-propanol. The presence of high n-propanol loading negatively affected the toluene removal; however, n-propanol removal was not affected by the presence of toluene and was effectively removed in the biofilter despite high toluene loadings. A model for toluene and n-propanol biofiltration could predict the cross-inhibition effect of n-propanol on toluene removal.

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