During coronavirus disease 2019 pandemic, the exponential increase in clinical waste (CW) generation has caused immense burden to CW treatment facilities. Co-incineration of CW in municipal solid waste incinerator (MSWI) is an emergency treatment method. A material flow model was developed to estimate the change in feedstock characteristics and resulting acid gas emission under different CW co-incineration ratios. The ash contents and lower heating values of the feedstocks, as well as HCl concentrations in flue gas showed an upward trend. Subsequently, 72 incineration residue samples were collected from a MSWI performing co-incineration (CW ratio <10 wt%) in Wuhan city, China, followed by 20 incineration residues samples from waste that were not co-incineration. The results showed that the contents of major elements and non-volatile heavy metals in the air pollution control residues increased during co-incineration but were within the reported ranges, whereas those in the bottom ashes revealed no significant changes. The impact of CW co-incineration at a ratio <10 wt% on the distribution of elements in the incineration residues was not significant. However, increase in alkali metals and HCl in flue gas may cause potential boiler corrosion. These results provide valuable insights into pollution control in MSWI during pandemic.
Traditional methods for analyzing
the biogenic and fossil
carbon
shares in solid waste are time-consuming and labor-intensive. A novel
approach was developed to directly classify the carbon group and predict
carbon content using the hyperspectral imaging (HSI) spectra of solid
waste in conjunction with state-of-the-art tree-based machine learning
models, including random forest (RF), extreme gradient boost, and
light gradient boost machine (LGBM). All of the classifiers and regressors
were able to achieve an accuracy above 0.95 and an R
2 of 0.96 in the test set, respectively. In addition,
two model interpretation approaches, the Shapley additive explanation
and model explainer, were applied. The results showed that the predictions
of the developed models were based on a reasonable understanding of
the overtone and shake of the functional groups (C–H, N–H,
and O–H). Furthermore, the developed models were validated
by an external test set, which did not overlap with the data used
for model construction. The RF and LGBM showed robust performance
with a 0.790 accuracy for carbon group classification and a 0.806 R
2 for carbon content prediction. Overall, the
optimal models provided a rapid method for characterizing the biogenic
carbon share in solid waste based on raw HSI spectra without preprocessing.
Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixelwise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification.
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