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

Air biofilters for a mixture of organic gaseous pollutants: an approach for industrial applications

Hazardous airborne pollutants are frequently emitted to the atmosphere in the form of a gaseous mixture. Air biofilters as the primary biotechnological choice for waste gas treatment (low inlet concentration and high gas flow rate) should run properly when the feed contains multiple pollutants. Simultaneous removal of pollutants in biofilters has been extensively studied over the last 10 years. In this review, the results and findings of the mentioned studies including different groups of pollutants, such as methane (CH4) and volatile organic compounds (VOCs) are discussed. As the number of pollutants in a mixture increases, their elimination might become more complicated due to interactions between the pollutants. Parallel batch studies might be helpful to better understand these interaction effects in the absence of mass transfer limitations. Setting optimum operating conditions for removal of mixtures in biofilters is challenging because of opposing properties of pollutants. In biofilters, concerns, such as inlet gas composition variation and stability while dealing with abrupt inlet load and concentration changes, must be managed especially at industrial scales. Biofilters designed with multi-layer beds, allow tracking the fate of each pollutant as well as analyzing the diversity of microbial culture across the filter bed. Certain strategies are recommended to improve the performance of biofilters treating mixtures. For example, addition of (bio)surfactants as well as a second liquid phase in biotrickling filters might be considered for the elimination of multiple pollutants especially when hydrophobic pollutants are involved.

Neural network-based performance assessment of one- and two-liquid phase biotrickling filters for the removal of a waste-gas mixture containing methanol, α-pinene, and hydrogen sulfide

The performance of one- and two-liquid phase biotrickling filters (OLP/TLP-BTFs) treating a mixture of gas-phase methanol (M), α-pinene (P), and hydrogen sulfide (H) was assessed using artificial neural network (ANN) modeling. The best ANN models with the topologies 3-9-3 and 3-10-3 demonstrated an exceptional capacity for predicting the performance of O/TLP-BTFs, with R2 > 99%. The analysis of causal index (CI) values for the model of OLP-BTF revealed a negative impact of M on P removal (CI = -2.367), a positive influence of P and H on M removal (CI = +7.536 and CI = +3.931) and a negative effect of H on P removal (CI = -1.640). The addition of silicone oil in TLP-BTF reduced the negative impact of M and H on P degradation (CI = -1.261 and CI = -1.310, respectively) compared to the OLP-BTF. These findings suggested that silicone oil had the potential to improve P availability to the biofilm by increasing the concentration gradient of P between the air/gas and aqueous phases. Multi-objective particle swarm optimization (MOPSO) suggested an optimum operational condition, i.e. inlet M, P, and H concentrations of 1.0, 1.1, and 0.3 g m-3, respectively, with elimination capacities (ECs) of 172.1, 26.5, and 0.025 g m-3 h-1 for OLP-BTF. Likewise, one of the optimum operational conditions for TLP-BTF is achievable at inlet concentrations of 4.9, 1.7, and 0.8 g m-3, leading to the optimum ECs of 299.7, 52.9, and 0.072 g m-3 h-1 for M, P, and H, respectively. These results provide important insights into the treatment of complex waste gas mixtures, addressing the interactions between the pollutant removal characteristics in OLP/TLP-BTFs and providing novel approaches in the field of biological waste gas treatment.

Comparative assessment of the performance of one- and two-liquid phase biotrickling filters for the simultaneous abatement of gaseous mixture of methanol, α-pinene, and hydrogen sulfide

A gaseous mixture of methanol (M), α-pinene (P), and hydrogen sulfide (H) was treated in one/two-liquid phase biotrickling filters (OLP/TLP-BTFs) at varying inlet concentrations and at an empty bed residence time (EBRT) of 57 s. The performance of TLP-BTF [BTF (A)] improved significantly in terms of M and P removal due to the presence of silicone oil at 5% v/v. The maximum elimination capacities (ECs) of M, P, and H in BTF (A) were obtained as 309, 73, and 56 g m-3 h-1, respectively. While, the maximum ECs achieved in the BTF operated without silicone oil [BTF (B)] were 172, 28, and 21 g m-3 h-1 for M, P, and H removal, respectively. Increasing the inlet concentration of H from 32 to 337 ppm inhibited P removal in both the BTFs. The presence of silicone oil enhanced gas-liquid mass transfer, prevented the BTF from experiencing substrate inhibition effects and allowed reaching high ECs for M and P. The experiments showed promising results for the long-term operation of removal of M, P, and H mixture in a one-stage TLP-BTF with the decreasing negative effects of M and H on P degradation.

Performance and neural modeling of a compost-based biofilter treating a gas-phase mixture of benzene and xylene

Biofilter (BF) has been regarded as a versatile gas treatment technology for removing volatile organic compounds (VOCs) from contaminated gas streams. In order for BF to be utilized in the industrial setting, it is essential to conduct research aimed at removing VOC mixtures under different inlet loading conditions, i.e. as a function of the gas flow rate and inlet VOC concentrations. The main aim of this study was to apply artificial neural networks (ANN) and determine the relationship between flow rate (FR), pressure drop (PD), inlet concentration (C), and removal efficiency (RE) in the BF treating gas-phase benzene and xylene mixtures. The ANN model was trained and tested to assess the removal efficiency of benzene (RE_B) and xylene (RE_X) under the influence of different FR, PD and C. The model's performance was assessed using a cross-validation method. The RE_b varied from 20% to >60%, while the RE_x varied from 10% to 70% during the different experimental phases of BF operation. The causal index (CI) technique was used to determine the sensitivity of the input parameters on the output variables. The ANN model with a topology of 4-4-2 performed the best in terms of predicting the RE profiles of both the pollutants. Furthermore, the effect was more pronounced for xylene because an increase in the benzene concentration reduced xylene removal (CI = -25.7170) more severely than benzene removal. An increase in the xylene concentration had a marginally positive effect on the benzene removal (CI = +0.1178).

Effects of Water Content and Irrigation of Packing Materials on the Performance of Biofilters and Biotrickling Filters: A Review

Biofilters (BFs) and biotrickling filters (BTFs) are two types of bioreactors used for treatment of volatile organic compounds (VOCs). Both BFs and BTFs use packing materials in which various microorganisms are immobilised. The water phase in BFs is stationary and used to maintain the humidity of packing materials, while BTFs have a mobile liquid phase. Optimisation of irrigation of packing materials is crucial for effective performance of BFs and BTFs. A literature review is presented on the influence of water content of packing materials on the biofiltration efficiency of various pollutants. Different configurations of BFs and BTFs and their influence on moisture distribution in packing materials were discussed. The review also presents various packing materials and their irrigation control strategies applied in recent biofiltration studies. The sources of this review included recent research articles from scientific journals and several review articles discussing BFs and BTFs.

Performance assessment of gas-phase toluene removal in one- and two-liquid phase biotrickling filters using artificial neural networks

The main aim of this work is to study gas-phase toluene removal in one- and two-liquid phase biotrickling filters (O/TLP-BTF) and model the BTF performance using artificial neural networks (ANNs). The TLP-BTF was operated for 60 d in the presence of silicone oil at empty bed residence times (EBRTs) of 120, 60, and 45 s, respectively, and toluene concentrations in the range of 0.9-3.1 g m^-3. A t-test analysis indicated that increasing the silicone oil volume ratio from 5 to 10% v/v, did not significantly improve the TLP-BTF performance (p-value = 0.65 > 0.05). The results from ANN modeling showed that toluene removal was more negatively affected by the inlet concentration (casual index, CI = -5.63) due to the kinetic limitation. The CI values for inlet concentration (+4.01) and liquid trickling rate (-2.45) indicated that the diffusion-limited regime controlled the removal process in the OLP-BTF.

Methane biofiltration in the presence of ethanol vapor under steady and transient state conditions: an experimental study

Methane (CH₄) removal in the presence of ethanol vapors was performed by a stone-based bed and a hybrid packing biofilter in parallel. In the absence of ethanol, a methane removal efficiency of 55 ± 1% was obtained for both biofilters under similar CH₄ inlet load (IL) of 13 ± 0.5 g_CH4 m^-3 h^-1 and an empty bed residence time (EBRT) of 6 min. The results proved the key role of the bottom section in both biofilters for simultaneous removal of CH₄ and ethanol. Ethanol vapor was completely eliminated in the bottom sections for an ethanol IL variation between 1 and 11 g_ethanol m^-3 h^-1. Ethanol absorption and accumulation in the biofilm phase as well as ethanol conversion to CO₂ contributed to ethanol removal efficiency of 100%. In the presence of ethanol vapor, CO₂ productions in the bottom section increased almost fourfold in both biofilters. The ethanol concentration in the leachate of the biofilter exceeding 2200 g_ethanol m^-3_leachate in both biofilters demonstrated the excess accumulation of ethanol in the biofilm phase. The biofilters responded quickly to an ethanol shock load followed by a starvation with 20% decrease of their performance. The return to normal operations in both biofilters after the transient conditions took less than 5 days. Unlike the hybrid packing biofilter, excess pressure drop (up to 1.9 cmH₂O m^-1) was an important concern for the stone bed biofilter. The biomass accumulation in the bottom section of the stone bed biofilter contributed to 80% of the total pressure drop. However, the 14-day starvation reduced the pressure drop to 0.25 cmH₂O m^-1.