Performance of a biofilter with compost and activated carbon based packing material for gas-phase toluene removal under extremely high loading rates.

The main aim of this work was to evaluate the performance of a biofilter packed with a mixture of compost and activated carbon, for gas-phase toluene removal under very high loading rates. Plaster of Paris was used as a binder to improve the mechanical strength and durability of the packing media. The biofilter was operated continuously for a period of ∼110 days and at four different flow rates (0.069, 0.084, 0.126 and 0.186 m-3 h-1), corresponding to toluene loading rates of 160-8759 g m-3 h-1. The maximum elimination capacity (EC) achieved in this study was 6665 g m-3 h-1, while the removal efficiency (RE) varied from ∼70 to >95% depending on the loading rate tested. The biofilter was able to remove >99% of toluene using Pseudomonas sp. RSST (MG 279053) as the dominant toluene degrading biocatalyst.

Effects of concentration and gas flow rate on the removal of gas-phase toluene and xylene mixture in a compost biofilter.

The aim of this work was to study the performance of a compost/ceramic bead biofilter (6:4 v/v) for the removal of gas-phase toluene and xylene at different inlet loading rates (ILR). The inlet toluene (or) xylene concentrations were varied from 0.1 to 1.5gm-3, at gas flow rates of 0.024, 0.048 and 0.072m3h-1, respectively, corresponding to total ILR varying between 7 and 213gm-3h-1. Although there was mutual inhibition, xylene removal was severely inhibited by the presence of toluene than toluene removal by the presence of xylene. The biofilter was also exposed to transient variations such as prolonged periods of shutdown (30days) and shock loads to envisage the response and recuperating ability of the biofilter. The maximum elimination capacity (EC) for toluene and xylene were 29.2 and 16.4gm-3h-1, respectively, at inlet loads of 53.8 and 43.7gm-3h-1.

Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks.

A two-stage biological waste gas treatment system consisting of a first stage biotrickling filter (BTF) and second stage biofilter (BF) was tested for the removal of a gas-phase methanol (M), hydrogen sulphide (HS) and α-pinene (P) mixture. The bioreactors were tested with two types of shock loads, i.e., long-term (66h) low to medium concentration loads, and short-term (12h) low to high concentration loads. M and HS were removed in the BTF, reaching maximum elimination capacities (ECmax) of 684 and 33 gm-3h-1, respectively. P was removed better in the second stage BF with an ECmax of 130 gm-3h-1. The performance was modelled using two multi-layer perceptrons (MLPs) that employed the error backpropagation with momentum algorithm, in order to predict the removal efficiencies (RE, %) of methanol (REM), hydrogen sulphide (REHS) and α-pinene (REP), respectively. It was observed that, a MLP with the topology 3-4-2 was able to predict REM and REHS in the BTF, while a topology of 3-3-1 was able to approximate REP in the BF. The results show that artificial neural network (ANN) based models can effectively be used to model the transient-state performance of bioprocesses treating gas-phase pollutants.

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

The performance of a compost biofilter inoculated with mixed microbial consortium was optimized for treating a gas-phase mixture of benzene and toluene. The biofilter was acclimated to these VOCs for a period of ∼18d. The effects of concentration and flow rate on the removal efficiency (RE) and elimination capacity (EC) were investigated by varying the inlet concentration of benzene (0.12-0.95g/m(3)), toluene (0.14-1.48g/m(3)) and gas-flow rate (0.024-0.072m(3)/h). At comparable loading rates, benzene removal in the mixture was reduced in the range of 6.6-41% in comparison with the individual benzene degradation. Toluene removal in mixture was even more affected as observed from the reductions in REs, ranging from 18.4% to 76%. The results were statistically interpreted by performing an analysis of variance (ANOVA) to elucidate the main and interaction effects.

Transient-state studies and neural modeling of the removal of a gas-phase pollutant mixture in a biotrickling filter.

The removal efficiency (RE) of gas-phase hydrogen sulfide (H), methanol (M) and α-pinene (P) in a biotrickling filter (BTF) was modeled using artificial neural networks (ANNs). The inlet concentrations of H, M, P, unit flow and operation time were used as the model inputs, while the outputs were the RE of H, M and P, respectively. After testing and validating the results, an optimal network topology of 5-8-3 was obtained. The model predictions were analyzed using Casual index (CI) values. M removal in the BTF was influenced positively by the inlet concentration of M in mixture (CI=3.79), while the removal of P and H were influenced more by the time of BTF operation (CI=25.36, 15.62). The BTF was subjected to different types of short-term shock-loads: 5-h shock-load of HMP mixture simultaneously, and 2.5-h shock-load of either H, M, or P, individually. It was observed that, short-term shock-loads of individual pollutants (M or H) did not significantly affect their own removal, but the removal of P was affected by 50%. The results from this study also show the sensitiveness of the well-acclimated BTF to handle sudden load variations and also revival capability of the BTF when pre-shock conditions were restored.

Back propagation neural network model for predicting the performance of immobilized cell biofilters handling gas-phase hydrogen sulphide and ammonia.

Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs) for removing hydrogen sulphide (H2S) and ammonia (NH3) from gas phase. The removal efficiencies (REs) of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs) varying up to 13 g H2S/m(3) ·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m(3) ·h. An application of the back propagation neural network (BPNN) to predict the performance parameter, namely, RE (%) using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min) and inlet concentrations (ppmv), respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R (2)) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently.

One-stage biotrickling filter for the removal of a mixture of volatile pollutants from air: performance and microbial community analysis.

The biodegradation of gas-phase mixtures of methanol, α-pinene and H2S was examined in a biotrickling filter (BTF), inoculated with a microbial consortium composed of an autotrophic H2S-degrading culture, and pure strains of Candida boidinii, Rhodococcus erythropolis, and Ophiostoma stenoceras. The inlet concentrations of methanol, α-pinene and H2S varied from 0.05 to 3.3 gm(-3), 0.05 to 2.7 gm(-3), and 0.01 to 1.4 gm(-3), respectively, at empty bed residence times (EBRT) of either 38 or 26s. The maximum elimination capacities (ECmax) of the BTF were 302, 175, and 191 gm(-3)h(-1), with 100%, 67%, and >99% removal of methanol, α-pinene and H2S, respectively. The presence of methanol showed an antagonistic removal pattern for α-pinene, but the opposite did not occur. For α-pinene, inlet loading rates (ILRs) >150 gα-pinenem(-3)h(-1) affected its own removal in the BTF. The presence of H2S did not show any declining effect on the removal of both methanol and α-pinene.

Performance of a fungal monolith bioreactor for the removal of styrene from polluted air.

Gas-phase styrene removal using the fungus, Sporothrix variecibatus was evaluated in a novel monolith bioreactor, receiving a continuous supply of nutrients from the trickling liquid phase. During the start-up process, the monolith reactor was operated for 22 days with relatively low styrene concentrations in the gas-phase (<0.4 g m(-3)). Afterwards, continuous experiments were carried out at different inlet styrene concentrations, ranging between 0.06 and 2.5 g m(-3), and at two different flow rates corresponding to empty bed residence times (EBRTs) of 77 and 19 s, respectively. A maximum elimination capacity of 67.4 g m(-3) h(-1) was observed at an inlet styrene load of 73.5 g m(-3) h(-1). However, it was observed that the critical loading rates to the monolith bioreactor were a strong function of the gas residence time. The critical load, with greater than 95% styrene removal was 74 g m(-3) h(-1) at an EBRT of 77 s, while it was only 37.2 g m(-3) h(-1) for an EBRT of 19 s. After 92 days of continuous operation, due to excess biomass growth on the surface of the monolith, the biodegradation efficiency decreased significantly. To ascertain the instantaneous response of the attached fungus, to withstand fluctuations in loading conditions, two dynamic shock loads were conducted, at EBRTs of 77 and 19 s, respectively. It was observed that, the performance of the monolith bioreactor decreased significantly at low residence times, when subjected to high shock loads. The recovery times for high performance, in both cases, did not exceed more than 1 h.