Abstract
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 R2 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 nonlinearity 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.
Highlights
Biofiltration is a promising cost effective technique for the treatment of waste gases containing Volatile Organic Compounds (VOCs), especially at low concentration and high flow rate [1,2]
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
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
Summary
Biofiltration is a promising cost effective technique for the treatment of waste gases containing VOCs, especially at low concentration and high flow rate [1,2]. Biofilter performance has been modeled using process based models that are based on reaction kinetics, mass balance principles and plug flow in air stream [2,3,4]. This depends on numerous model parameters and obligates information on specific growth rate of micro-organisms, biofilm thickness, half saturation constant, diffusion coefficient, partition coefficient, yield etc. In the experimental process optimization, one parameter is varied at a time and keeping the other constant. This technique is time-consuming and does not depict the complete effects of the parameters in the process and ignores the combined interactions between the phys-
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