Biofiltration of Hexane Vapor: Experimental and Neural Model Analysis

Abstract

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.

Effect of a Non‐Ionic Surfactant on Xylene Removal in a Scoria‐Compost‐Based Biofilter

“…Among the conventional air pollution control technologies, biofiltration is an attractive process for the elimination of volatile organic compounds (VOCs) from waste air streams . In a biofilter, contaminated air flows through a packed bed, fixed‐film bioreactor, where microorganisms biodegrade VOCs into environmentally harmless end products such as carbon dioxide, water, and biomass .…”

Applying an ANFIS-based Algorithm in Comparison with Mechanistic Modelling in a Biofilter Treating Hexane

“…An error back propagation with momentum multilayer neural network (topology 2-4-1) is applied by Rene et al and Ravi and Philip to predict RE of the biofilter (with the concentration and unit flow as the input variables) [16,17]. A feed forward multilayer neural network (topology 2-10-1) was presented by Zamir et al [18] (with temperature and ILR as input variables). Deshmukh et al [19] compared the Radial Basis Function Neural Network (RBFN) and response surface methodology for prediction and performance optimization of a biofilter system treating toluene and revealed the superior ability of RBFN for approximate higher degree of non-linearity that was between input and output variables.…”

Influence of Nonionic Surfactants on Fungal and Bacterial Degradation of Hexane

“…Inhalation of a high level of hexane has been linked to nervous system damage and cancer development . To reduce the risks to humans and the environment, biological treatment technologies (e.g., biotrickling filters, bioscrubbers, and two‐liquid phase systems) have emerged as environmentally friendly and economic options for VOC removal in polluted air . However, the biological removal of VOCs is challenged by high hydrophobicity and limited mass transfer of hexane in aqueous phase, where biodegradation by microconsortia occurs .…”