Removal of toluene vapors using a fungal biofilter under intermittent loading

Zamir, Seyed Morteza; Halladj, Rouein; Nasernejad, Bahram

doi:10.1016/j.psep.2010.10.001

Cited by 49 publications

(20 citation statements)

References 22 publications

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“…This increase was attributed to desorption effects during restart. During periods of non-use, most of the microbes can enter a transient quiescent phase, where they can persist, but not multiply (Zamir et al, 2011;Rene et al, 2012). This could normally produce numerous physiological changes within the cell body, owing to which the microorganisms prefer to survive with nutrients from non-preferred sources.…”

Section: Stability Of Biofilter Operationmentioning

confidence: 99%

Start-up, performance and optimization of a compost biofilter treating gas-phase mixture of benzene and toluene

Rene

Kar

Krishnan

et al. 2015

Bioresource Technology

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Section: Stability Of Biofilter Operationmentioning

confidence: 99%

Start-up, performance and optimization of a compost biofilter treating gas-phase mixture of benzene and toluene

Rene

Kar

Krishnan

et al. 2015

Bioresource Technology

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“…The increase in the IL to 600 g m À3 h À1 at 358C just reduced the RE to about 82%. The EC on day 157 was 491 g m À3 h À1 which was significantly higher than prior biofiltration studies when intermittent loading was applied [9,31,32]. It is noteworthy to mention that the BF performed efficiently at both 35 and 308C.…”

Section: Resultsmentioning

confidence: 67%

“…Large numbers of studies have formulated mathematical models to illustrate the BF performance on the basis of different parameters which led to solve complex nonlinear equations of momentum, heat, and mass transfer, simultaneously [5][6][7][8]. Moreover, experimental determination of these parameters such as biological kinetic constants, diffusion of pollutants in biofilm, oxygen consumption and CO 2 production rates, probable change in the elemental composition of the attached biofilm, and mass transfer coefficients are so difficult, whereas, various points of views are discussed in literatures about batch or continuous determination of them [9][10][11][12]. A solution to this problem is to use a nonlinear modeling method that focuses on the simulation and prediction of the BF performance exclusively based on available data [13].…”

Section: Introductionmentioning

confidence: 99%

Biofiltration of Hexane Vapor: Experimental and Neural Model Analysis

Zamir

Halladj

Saber

et al. 2011

CLEAN Soil Air Water

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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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“…The kinetic model of H 2 S biodegradation could be expressed by a MichaeliseMenten relationship assuming that oxygen was not limited and the reaction was kinetically controlled. Under steady-state microbial growth conditions, the rate of biomass accumulation in the system was negligible and could be ignored C aceres et al, 2012;Zamir et al, 2011). The MichaeliseMenten constants were also determined by studying the kinetics under different EBRTs.…”

Section: Kinetic Studiesmentioning

confidence: 99%