An investigation was conducted for the improvement of viscosity index of light lubricating oil fraction (40 stock)
obtained from vacuum distillation unit of lube oil plant of Daura Refinery, using solvent extraction process.
In this study furfural solvent was used to extract the undesirable materials which reduce the viscosity index of raw
lubricating oil fraction.
The studied effecting variables of extraction were extraction temperature range from 70 to 110°C, and solvent to oil
ratio range from 1:1 to 4:1 (wt/wt).
The n-d-M method was used for calculation of carbon distribution and structural group analysis of the raffinate
produced from furfural extraction.
Also the three component phase diagram for a mixed-base oil and furfural at different temperature was drawn.
The results of this investigation show that the viscosity index of lubricating oil fraction increases with increasing
extraction temperature and increasing the solvent to oil ratio and reaches 115.6 at extraction temperature 110°C and
solvent to oil ratio 4:1.
Further more, the results show that the viscosity, refractive index, density, sulfur content and percentage yield of
produced raffinate were decreased as the extraction temperature or solvent to oil ratio increases for extraction process.
Thin films of the blended solution of (NiPc/C60) on glass substrates were prepared by spin-coated method for three different ratios (100/1, 100/10 and 100/100). The effects of annealing temperature and C60 concentration on the optical properties of the samples were studied using the UV-Vis absorption spectroscopy and FTIR spectra. The optical absorption spectrum consists of two main bands, Q and B band, with maxima at about (602-632) nm and (700-730) nm for Q1 and Q2 respectively, and (340-375) nm for B band. The optical energy gap were determined from optical absorption spectra, The variation of optical energy gap with annealing temperature was nonsystematic and this may be due to the improvement in crystal structure for thin films. Whi
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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