Airless spray painting, widely adopted for its efficiency and cost-effectiveness, generates a significant amount of airborne paint particles that contribute to air pollution and pose health risks to workers and nearby...Airless spray painting, widely adopted for its efficiency and cost-effectiveness, generates a significant amount of airborne paint particles that contribute to air pollution and pose health risks to workers and nearby residents. In this study, a paint particle dispersion reduction device (PPDRD) utilizing an axial cyclone separator and a high efficiency particulate air (HEPA) filter was designed and evaluated to minimize the dispersion of paint particles during airless spray applications. The cyclone separator captured larger paint particles through centrifugal force, while the HEPA filter effectively removed smaller particles that escaped the cyclone separator. Computational fluid dynamics (CFD) simulations were conducted to optimize key design parameters, including the number and height of guide vanes and suction flow rate. Lab-scale and field experiments demonstrated that the PPDRD significantly reduced airborne paint particles, with respirable particles (4 μm or smaller) decreasing by 50–80 % depending on particle size, while maintaining coating quality. These findings indicate that the PPDRD effectively mitigates the inhalation risks associated with hazardous paint aerosols, providing a practical solution for improving workplace safety and environmental compliance. This technology is expected to be widely applicable in exterior building painting, shipbuilding, and the automotive industry.展开更多
The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for d...The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model.展开更多
基金the“Reduction Management Program of Fine Dust Blind-Spots”and supported by the Ministry of Environment as part of the Korea Environmental Industry and Technology Institute(grant No.2020003060010)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(grant No.RS-2024-00346834).
文摘Airless spray painting, widely adopted for its efficiency and cost-effectiveness, generates a significant amount of airborne paint particles that contribute to air pollution and pose health risks to workers and nearby residents. In this study, a paint particle dispersion reduction device (PPDRD) utilizing an axial cyclone separator and a high efficiency particulate air (HEPA) filter was designed and evaluated to minimize the dispersion of paint particles during airless spray applications. The cyclone separator captured larger paint particles through centrifugal force, while the HEPA filter effectively removed smaller particles that escaped the cyclone separator. Computational fluid dynamics (CFD) simulations were conducted to optimize key design parameters, including the number and height of guide vanes and suction flow rate. Lab-scale and field experiments demonstrated that the PPDRD significantly reduced airborne paint particles, with respirable particles (4 μm or smaller) decreasing by 50–80 % depending on particle size, while maintaining coating quality. These findings indicate that the PPDRD effectively mitigates the inhalation risks associated with hazardous paint aerosols, providing a practical solution for improving workplace safety and environmental compliance. This technology is expected to be widely applicable in exterior building painting, shipbuilding, and the automotive industry.
基金supported by the National Research and Development Project of China (2020YFB2008400).
文摘The hardness prediction model was established by support vector regression(SVR).In order to avoid exaggerating the contribution of very tiny alloying elements,a weighted fuzzy C-means(WFCM)algorithm was proposed for data clustering using improved Mahalanobis distance based on random forest importance values,which could play a full role of important features and avoid clustering center overlap.The samples were divided into two classes.The top 10 features of each class were selected to form two feature subsets for better performance of the model.The dimension and dispersion of features decreased in such feature subsets.Comparing four machine learning algorithms,SVR had the best performance and was chosen to modeling.The hyper-parameters of the SVR model were optimized by particle swarm optimization.The samples in validation set were classified according to minimum distance of sample to clustering centers,and then the SVR model trained by feature subset of corresponding class was used for prediction.Compared with the feature subset of original data set,the predicted values of model trained by feature subsets of classified samples by WFCM had higher correlation coefficient and lower root mean square error.It indicated that WFCM was an effective method to reduce the dispersion of features and improve the accuracy of model.
