Removal of odors and VOCs in municipal solid waste comprehensive treatment plants using a novel three-stage integrated biofilter: Performance and bioaerosol emissions

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.

Mechanisms of N, N-dimethylacetamide-facilitated n-hexane removal in a rotating drum biofilter packed with bamboo charcoal-polyurethane composite

n-Hexane and N, N-dimethylacetamide (DMAC) are two major volatile organic compounds (VOCs) discharged from the pharmaceutical industry. To enhance DMAC-facilitated n-hexane removal, we investigated the simultaneous removal of multiple pollutants in a rotating drum biofilter packed with bamboo charcoal-polyurethane composite. After adding 800 mg·L^-1 DMAC, the n-hexane removal efficiency increased from 59.4 % to 83.1 % under the optimized conditions. The maximum elimination capacity of 10.0 g·m^-3·h^-1n-hexane and 157 g·m^-3·h^-1 DMAC were obtained. The biomass of bamboo charcoal-polyurethane and the ratio of protein-to-polysaccharide in extracellular polymeric substances were significantly increased compared with the non-DMAC stage, which is attributed to increased carbon utilization. In addition, Na⁺ K⁺-ATPase was positively correlated with increasing electron transport system activity, which was 1.98 and 1.36 times greater. Hydrophilic DMAC improved the bioavailability of hydrophobic n-hexane and benefited bacterial metabolism. Co-degradation of n-hexane and DMAC system can be used for other volatile organic pollutants.

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting

An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions.

ELECTRONIC SUPPLEMENTARY MATERIAL

Supplementary material is available in the online version of this article at 10.1007/s11783-023-1677-1 and is accessible for authorized users.

Odors Emitted from Biological Waste and Wastewater Treatment Plants: A Mini-Review

In recent decades, a new generation of waste treatment plants based on biological treatments (mainly anaerobic digestion and/or composting) has arisen all over the world. These plants have been progressively substituted for incineration facilities and landfills. Although these plants have evident benefits in terms of their environmental impact and higher recovery of material and energy, the release into atmosphere of malodorous compounds and its mitigation is one of the main challenges that these plants face. In this review, the methodology to determine odors, the main causes of having undesirable gaseous emissions, and the characterization of odors are reviewed. Finally, another important topic of odor abatement technologies is treated, especially those related to biological low-impact processes. In conclusion, odor control is the main challenge for a sustainable implementation of modern waste treatment plants.