Total suspended particulate matter (SPM) in the urban atmosphere of Dhaka city was collected using a high volume sampling technique for a period of one month (August 2017-September 2017). Chemical characterization of ...Total suspended particulate matter (SPM) in the urban atmosphere of Dhaka city was collected using a high volume sampling technique for a period of one month (August 2017-September 2017). Chemical characterization of particulate matter (PM) was investigated, and characterized concerning the size distribution, morphological features such as count, total area, average size, perimeter, circularity, aspect ratio (AR), roundness;equivalent spherical diameter (ESD), surface area and volume of PM. The results of elemental analysis showed that the presence of heavy metal in PM was ten to hundred times higher than the standard value prescribed by WHO and USEPA. Several morphological analysis indicated that particles varying in shape from nearly spherical to various irregular shape, had higher surface energy, higher content of Cl and S bearing particles and had a large surface area which can cause greater damage to lungs. The presence of various organic compounds containing functional groups like alcohols, ketones aldehydes, carboxylic acids as well as unsaturated and saturated carbon bonds was observed by FT-IR analysis. Scanning Electron Microscopy (SEM) was used to characterize the morphology of the PM. Agglomerates and shoots type particles were mostly seen in SPM.展开更多
Accurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost.Dissolved gas analysis(DGA)is one of the proven and widely accepted tools for incipie...Accurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost.Dissolved gas analysis(DGA)is one of the proven and widely accepted tools for incipient fault diagnosis in power transformers.To improve the accuracy and solve the cases that cannot be classified using Rogers’Ratios,IEC ratios and Duval triangles methods,a novel DGA technique based on Parzen window estimation have been presented in this study.The model uses the concentrations of five combustible hydrocarbon gases:methane,ethane,ethylene,acetylene and hydrogen to compute the probability of transformers fault categories.Performance of the proposed method has been evaluated against different conventional techniques and artificial intelligence-based approaches such as support vector machines,artificial neural networks,rough sets analysis and extreme learning machines for the same set of transformers.A comparison with other soft computing approaches shows that the proposed method is reliable and effective for incipient fault diagnosis in power transformers.展开更多
文摘Total suspended particulate matter (SPM) in the urban atmosphere of Dhaka city was collected using a high volume sampling technique for a period of one month (August 2017-September 2017). Chemical characterization of particulate matter (PM) was investigated, and characterized concerning the size distribution, morphological features such as count, total area, average size, perimeter, circularity, aspect ratio (AR), roundness;equivalent spherical diameter (ESD), surface area and volume of PM. The results of elemental analysis showed that the presence of heavy metal in PM was ten to hundred times higher than the standard value prescribed by WHO and USEPA. Several morphological analysis indicated that particles varying in shape from nearly spherical to various irregular shape, had higher surface energy, higher content of Cl and S bearing particles and had a large surface area which can cause greater damage to lungs. The presence of various organic compounds containing functional groups like alcohols, ketones aldehydes, carboxylic acids as well as unsaturated and saturated carbon bonds was observed by FT-IR analysis. Scanning Electron Microscopy (SEM) was used to characterize the morphology of the PM. Agglomerates and shoots type particles were mostly seen in SPM.
文摘Accurate faults diagnosis in power transformers is important for utilities to schedule maintenance and minimises the operation cost.Dissolved gas analysis(DGA)is one of the proven and widely accepted tools for incipient fault diagnosis in power transformers.To improve the accuracy and solve the cases that cannot be classified using Rogers’Ratios,IEC ratios and Duval triangles methods,a novel DGA technique based on Parzen window estimation have been presented in this study.The model uses the concentrations of five combustible hydrocarbon gases:methane,ethane,ethylene,acetylene and hydrogen to compute the probability of transformers fault categories.Performance of the proposed method has been evaluated against different conventional techniques and artificial intelligence-based approaches such as support vector machines,artificial neural networks,rough sets analysis and extreme learning machines for the same set of transformers.A comparison with other soft computing approaches shows that the proposed method is reliable and effective for incipient fault diagnosis in power transformers.
