A.M. Hatami; M.R. Sabour; A. Amiri
Abstract
Oil refining is an inevitable step in production of edible and industrial oil. Bleaching is the most important process among the refining processes. Bleaching adsorption is the most common method and clay is the most widely used adsorbent in this method. Disposal of bleaching clay, as a waste from re-refining ...
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Oil refining is an inevitable step in production of edible and industrial oil. Bleaching is the most important process among the refining processes. Bleaching adsorption is the most common method and clay is the most widely used adsorbent in this method. Disposal of bleaching clay, as a waste from re-refining plants, makes many environmental problems and economic losses. In the current study, the effects of possible factors such as solvent to clay ratio, temperature, time, aggregation size and rotation speed of the stirrer (degree of mixing) on the efficiency of extracted lubricating oil were investigated by solvent extraction method. By conducting experiments at different reaction times and rotation speeds, it was concluded that the most important factor in obtaining the appropriate output was solvent to clay ratio. The tests conducted to investigate the effect of grain size on the efficiency indicated that agglomerates size did not have a positive effect on efficiency. Finally, for the solvent to clay ratios ranging from 2.48-9.53 ml/g and a time period ranging from 5 to 40 minutes, the main tests designed by the response surface methodology. The best efficiency was obtained at the highest level of solvent to clay ratio (9.53 ml/g) and at the time of 22.5 minutes that led to 88.60% oil extraction from the clay. The accuracy of the model output was estimated to be 96%.
M. Memarianfard; A.M. Hatami; M. Memarianfard
Abstract
Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM2.5 ...
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Most parts of the urban areas are faced with the problem of floating fine particulate matter. Therefore, it is crucial to estimate the amounts of fine particulate matter concentrations through the urban atmosphere. In this research, an artificial neural network technique was utilized to model the PM2.5 dispersion in Tehran City. Factors which are influencing the predicted value consist of weather-related and air pollution-related data, i.e. wind speed, humidity, temperature, SO2, CO, NO2, and PM2.5 as target values. These factors have been considered in 19 measuring stations (zones) over urban area across Tehran City during four years, from March 2011 to March 2015. The results indicate that the network with hidden layer including six neurons at training epoch 113, has the best performance with the lowest error value (MSE=0.049438) on considering PM2.5 concentrations across metropolitan areas in Tehran. Furthermore, the “R” value for regression analysis of training, validation, test, and all data are 0.65898, 0.6419, 0.54027, and 0.62331, respectively. This study also represents the artificial neural networks have satisfactory implemented for resolving complex patterns in the field of air pollution.