Simultaneous acidic air biofiltration of toluene and styrene mixture in the presence of rhamnolipids: Performance evaluation and neural model analysis

Application of a generalized hybrid machine learning model for the prediction of H2S and VOCs removal in a compact trickle bed bioreactor (CTBB)

This study presents a generalized hybrid model for predicting H2S and VOCs removal efficiency using a machine learning model: K-NN (K - nearest neighbors) and RF (random forest). The approach adopted in this study enabled the (i) identification of odor removal efficiency (K) using a classification model, and (ii) prediction of K <100%, based on inlet concentration, time of day, pH and retention time. Global sensitivity analysis (GSA) was used to test the relationships between the inputs and outputs of the K-NN model. The results from classification model simulation showed high goodness of fit for the classification models to predict the removal of H2S and VOCs (SPEC = 0.94-0.99, SENS = 0.96-0.99). It was shown that the hybrid K-NN model applied for the "Klimzowiec" WWTP, including the pilot plant, can also be applied to the "Urbanowice" WWTP. The hybrid machine learning model enables the development of a universal system for monitoring the removal of H2S and VOCs from WWTP facilities.

Biofiltration of toluene and ethyl acetate mixture by a fungal-bacterial biofilter: Performance and community structure analysis

The inhibitory effect of hydrophilic volatile organic compounds (VOCs) on hydrophobic VOCs removal was found to be efficiently reduced by the fungal-bacterial biofilters (F&B-BFs) developed in the present study. Overall, the toluene and ethyl acetate mixture removal efficiencies (REs) and elimination capacities (ECs) of F&B-BFs were superior to those of bacterial biofilters (B-BFs). The REs for toluene and ethyl acetate were 32.5 ± 0.8 % and 74.6 ± 1.0 %, respectively, for F&B-BFs, in comparison to 8.0 ± 0.3 % and 60 ± 1.3 % for B-BFs. The ECs for toluene and ethyl acetate were 13.0 g m-3 h-1 and 149.2 g m-3 h-1, respectively, for the F&B-BF, compared to 3.2 g m-3 h-1 and 119.6 g m-3 h-1 for the B-BFs. This was achieved at a constant empty bed residence time (EBRT) of 45 s. F&B-BFs exhibited a superior mineralization efficiencies (MEs) compared to B-BFs for a VOC mixture of toluene and ethyl acetate (≈36.1 % vs ~ 29.6 %). This is attributed to the direct capture of VOCs by the presence of fungi, increased the contact time between VOCs and VOCs-degrading bacteria, and even distribution of VOCs-degrading bacteria in the F&B-BFs. Moreover, compared with B-BFs, the coupling effect of genus Pseudomonas degradation, and unclassified_f_Herpotrichiellaceae and unclassified_p_Ascomycota adsorption of F&B-BF resulted in a reduction in the impact of the presence of hydrophilic VOCs on the removal of hydrophobic VOCs, thereby enhancing the biofiltration performance of mixtures of hydrophilic and hydrophobic VOCs.

Effect of toluene on m-xylene removal in a biotrickling filter: Performance, biofilm characteristics, and microbial analysis

Hydrophobic volatile organic compounds (VOCs) pose a challenge to the removal efficiency in biotrickling filters (BTFs). The addition of relatively hydrophilic substances presents a promising approach for enhancing the elimination of hydrophobic VOCs. In this study, toluene was introduced into the BTF system alongside m-xylene, and their mixing ratios were changed to explore the interactions and mechanisms under different conditions. The result showed that the most pronounced synergistic interaction occurred when the mixing concentration ratio of m-xylene and toluene was 2:1. The removal efficiency (RE) of m-xylene increased from 88% to 97%, and the elimination capacity (EC) of m-xylene changed from 64 to 72 g m-3 h-1. Under this condition, there was a notable increase in biomass, extracellular polymeric substance (EPS) content, and relative hydrophobicity. Microbial diversity was enhanced observably with Berkeleyces and Mycobacterium potentially playing a positive role in co-degradation. Meanwhile, microbial metabolic function prediction indicated a significant enhancement in metabolic functions. Therefore, the introduction of relatively hydrophilic VOCs represents an effective strategy for enhancing the removal of hydrophobic VOCs in the BTFs.

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.

Performance and Mechanism of Chlorine Dioxide on BTEX Removal in Liquid and Indoor Air

With the development of the chemical industry, benzene, toluene, ethylbenzene, and xylene (BTEX) have gradually become the major indoor air pollutants. Various gas treatment techniques are widely used to prevent the physical and mental health hazards of BTEX in semi-enclosed spaces. Chlorine dioxide (ClO₂) is an alternative to chlorine as a secondary disinfectant with a strong oxidation ability, a wide range of action, and no carcinogenic effects. In addition, ClO₂ has a unique permeability which allows it to eliminate volatile contaminants from the source. However, little attention has been paid to the removal of BTEX by ClO₂, due to the difficulty of removing BTEX in semi-enclosed areas and the lack of testing methods for the reaction intermediates. Therefore, this study explored the performance of ClO₂ advanced oxidation technology on both liquid and gaseous benzene, toluene, o-xylene, and m-xylene. The results showed that ClO₂ was efficient in the removal of BTEX. The byproducts were detected by gas chromatography-mass spectrometry (GC-MS) and the reaction mechanism was speculated using the ab initio molecular orbital calculations method. The results demonstrated that ClO₂ could remove the BTEX from the water and the air without causing secondary pollution.

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).