RESUMO
Copper-containing sludge is a common by-product of industrial activities, particularly electroplating and metal processing. This type of sludge contains high concentrations of heavy metals such as copper, which can pose a potential threat to the environment. Therefore, its treatment and disposal require special attention. Due to its efficient mass and heat transfer characteristics, the suspended state technology has shown significant potential for application in a number of key processes, including the drying, decomposition, and reduction of copper-containing sludge. This paper presents an in-depth analysis of the current status of the application of the suspended state technology in the treatment of copper-containing sludge. Based on this analysis, a device for the treatment of copper-containing sludge in the suspended state was designed, through which the characteristics of copper-containing sludge in the oxidative decomposition and reduction phases are investigated. The research objects were gas concentration, temperature, contact state, and particle size time. Orthogonal experiments were initially employed to investigate the relationship between the influencing factors and the conversion rate of copper oxides. This was followed by a single-factor influence study, which led to the determination of the optimal process parameters for the decomposition experiments of the Cu-containing sludge in an oxidizing atmosphere. The 100 µm Cu-containing sludge was reacted with 10% O2 gas at a flow rate of 1 m/s for 3 min under the condition of 900 °C. The process parameters were then determined as follows: The research objects were gas concentration, temperature, contact state, and particle size time. Orthogonal experiments were employed to investigate the relationship between the influencing factors and the copper conversion rate. This was followed by a single-factor influence study, which determined the optimal process parameters for the copper-containing sludge reduction experiments. The 200 µm copper-containing sludge was reacted for 5 min at a flow rate of 7% carbon monoxide at a flow rate of 1.5 m/s under the condition of 800 °C.
RESUMO
Purpose: Limited investigation is available on the correlation between environmental phenols' exposure and estimated glomerular filtration rate (eGFR). Our target is established a robust and explainable machine learning (ML) model that associates environmental phenols' exposure with eGFR. Methods: Our datasets for constructing the associations between environmental phenols' and eGFR were collected from the National Health and Nutrition Examination Survey (NHANES, 2013-2016). Five ML models were contained and fine-tuned to eGFR regression by phenols' exposure. Regression evaluation metrics were used to extract the limitation of the models. The most effective model was then utilized for regression, with interpretation of its features carried out using shapley additive explanations (SHAP) and the game theory python package to represent the model's regression capacity. Results: The study identified the top-performing random forest (RF) regressor with a mean absolute error of 0.621 and a coefficient of determination of 0.998 among 3,371 participants. Six environmental phenols with eGFR in linear regression models revealed that the concentrations of triclosan (TCS) and bisphenol S (BPS) in urine were positively correlated with eGFR, and the correlation coefficients were ß = 0.010 (p = 0.026) and ß = 0.007 (p = 0.004) respectively. SHAP values indicate that BPS (1.38), bisphenol F (BPF) (0.97), 2,5-dichlorophenol (0.87), TCS (0.78), BP3 (0.60), bisphenol A (BPA) (0.59) and 2,4-dichlorophenol (0.47) in urinary contributed to the model. Conclusion: The RF model was efficient in identifying a correlation between phenols' exposure and eGFR among United States NHANES 2013-2016 participants. The findings indicate that BPA, BPF, and BPS are inversely associated with eGFR.