Pollutant emission is becoming a serious environmental issue nowadays. Stringent legislations were introduced in several countries to limit the permissible levels of pollutant particle emission in major combustion systems such as burners and furnaces that have been widely used in industrial application. In this study, a numerical study of laminar coflow diffusion flame was performed in a model combustor using the commercial software ANSYS Fluent 19.1. The main focus of this study is to understand the effect of the variation of flow characteristics in the coflow diffusion flame on the prediction of NO x and soot emissions. A comparison study of the pollutant formation was performed with different hydrocarbon gaseous fuels (methane, ethylene, ethane, propane, and n-butane) with detailed high-temperature reaction mechanisms. In addition, the Moss−Brookes model was adopted to obtain the soot emission data. Variation of the flow characteristics on the pollutant formation was performed by examining the change in fuel inlet velocity, i.e., 0.5u ̅ 0 , u ̅ 0 , 1.5u ̅ 0 , 2u ̅ 0 with u ̅ 0 the mean fuel inlet velocity of baseline condition, and the effect of nozzle heating condition, i.e., 298 and 403 K. The results showed that ethylene flame produced higher NO x and soot compared to other hydrocarbon fuels. It was observed that the increase of the fuel inlet velocity promoted the formations of NO x and soot. Besides that, the nozzle heating condition increased the overall adiabatic temperature of the flame, where the relative effect was more pronounced on the alkane fuels, especially the lighter fuel compared to alkene fuel (ethylene).
Sorting and Classification of mango, there are different colors, weights, sizes, shapes and densities. Currently, classification based on the above features is being carried out mainly by manuals due to farmers' awareness of low accuracy, high costs, health effects and high costs, costly economically inferior. This study was conducted on three main commercial mango species of Vietnam to find out the method of classification of mango with the best quality and accuracy. World studies of mango classification according to color, size, volume and almost done in the laboratory but not yet applied in practice. The quality assessment of mango fruit has not been resolved. Application of image processing technology, computer vision combined with artificial intelligence in the problem of mango classification or poor quality. The goal of the study is to create a system that can classify mangoes in terms of color, volume, size, shape and fruit density. The classification system using image processing incorporates artificial intelligence including the use of CCD cameras, C language programming, computer vision and artificial neural networks. The system uses the captured mango image, processing the split layer to determine the mass, volume and defect on the mango fruit surface. Especially, determine the density of mangoes related to its maturity and sweetness and determine the percentage of mango defects to determine the quality of mangoes for export and domestic or recycled mangoes. This article is about the development of an automatic mango classification system to control and evaluate mango quality before packaging and exporting to the market. It is in the research, design and fabrication of mango classification model and the completion of an automatic mango classification system using machine vision combining artificial intelligence. Index Terms-The classification of mango, sorting of mangoes, image processing technology, artificial intelligence; computer vision, artificial neural networks.
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