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

Effect of toluene on siloxane biodegradation and microbial communities in biofilters

The removal of volatile methyl siloxanes (VMS) from landfill biogas is crucial for clean energy utilization. VMS are usually found together with aromatic compounds in landfill biogas of which toluene is the major representative. In the present study, two biofilters (BFs) packed with either woodchips and compost (WC) or perlite (PER) were used to study the (co-) removal of octamethyltrisiloxane (L3) and octamethylcyclotetrasiloxane (D4) from gas in presence and absence of toluene, used as a representative aromatic compound. The presence of low inlet toluene concentrations (315 ± 19 - 635 ± 80 mg toluene m-3) enhanced the VMS elimination capacity (EC) in both BFs by a factor of 1.8 to 12.6. The highest removal efficiencies for D4 (57.1 ± 1.1 %; EC = 0.12 ± 0.01 gD4 m-3 h-1) and L3 (52.0 ± 0.6 %; EC = 0.23 ± 0.01 gL3 m-3 h-1) were observed in the BF packed with WC. The first section of the BFs (EBRT = 9 min), where toluene was (almost) completely removed, accounted for the majority (87.7 ± 0.6 %) of the total VMS removal. Microbial analysis revealed the impact of VMS and toluene in the activated sludge, showing a clear selection for certain genera in samples influenced by VMS in the presence (X2) or absence (X1) of toluene, such as Pseudomonas (X1 = 0.91 and X2 = 12.0 %), Sphingobium (X1 = 0.09 and X2 = 4.04 %), Rhodococcus (X1 = 0.42 and X2 = 3.91 %), and Bacillus (X1 = 7.15 and X2 = 3.84 %). The significant maximum EC values obtained by the BFs (0.58 gVMS m-3 h-1) hold notable significance in a combined system framework as they could enhance the longevity of traditional physicochemical methods to remove VMS like activated carbon in diverse environmental scenarios.

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.

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.

Effect of gas components on the degradation mechanism of o-dichlorobenzene by non-thermal plasma technology with single dielectric barrier discharge

In this paper, the degradation of o-DCB under different gas-phase parameter conditions was investigated using the SDBD-NTP system. The results showed that the increase in initial and oxygen concentrations had opposite effects on the degradation of o-DCB. Among them, the increase of oxygen concentration promoted the degradation of o-DCB. Relative humidity promoted and then inhibited the degradation of o-DCB. The highest degradation efficiency of o-DCB was achieved at RH = 15%, reaching 91% at 29W. In the study of by-products, it was found that O3 and NOx were the main inorganic by-products, and that different oxygen levels and relative humidity conditions had a large effect on the production of O3 and NOx. In all of them, the concentration of O3 decreased with the increase of input power. NOx increased with increasing oxygen concentration, but the increase in relative humidity inhibited the production of NO and N2O and promoted the conversion of NO2. A study of organic by-products revealed this. In the absence of oxygen, a higher number of benzene products appeared. Whereas, with the addition of oxygen, only in the by-products under conditions where no relative humidity was introduced, benzene ring products were predominantly present in the by-products. However, when RH was added, n-hexane was found to be present in the by-products. This may be because the introduction of OH• favors the destruction of the benzene ring. Finally, the possible reaction pathways and reaction mechanisms of o-DCB under different gas-phase parameters are given. It provides a reference for future related scientific research as well as scientific problems in practical applications.

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.