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.
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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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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Leaching process applied for the extraction of bio active compounds from dried roots of (Elecampane) Inula helenium. Ethanol, hexane and distillated water were used as solvents. Roots were soaked with ethanol (5% w/v) with various concentration of ethanol (30 to 98%) at one day to know effect concentration of the solvent with concentration of bio active compound in Inula helenium. The same procedure was done using hexane as solvent. Also distilled water was used as solvent for extraction 5%(w/v) where plant material was soaked in water at different temperatures (25, 40, 65, 80, and 90) C. In all solvents undertaken, the effect of time duration on active ingredient (Thymol, Isoalatolactone, Alatolactone, 10-isobutyryl-oxy 8-9-epoxy thymol is
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The development of economic and environmentally friendly extractants to recover cobalt metal is required due to the increasing demand for this metal. In this study, solvent extraction of Co(II) from aqueous solution using a mixture of N,N0-carbonyl difatty amides (CDFAs) synthesised from palm oil as the extractant was carried out. The effects of various parameters such as acid, contact time, extractant concentration, metal ion concentration and stripping agent and the separation of Co(II) from other metal ions such as Fe(II), Ni(II), Zn(III) and Cd(II) were investigated. It was found that the extraction of Co(II) into the organic phase involved the formation of 1:1 complexes. Co(II) was successfully separated from commonly associated metal
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ولاء طارق حميد, Mustansiriyah Journal of Sports Science, 2021
The education, especially higher education, is an essentially factor in the progress of any society, if we consider the higher education, represents the top of the education`s pyramid which take part in developing the human resources and provide the human staff to raise the productive efficiency, and improve the social , economic level
In order to face the increasing importance of higher education, great capabilities and expenditures must be available in a continous way, such expe
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This study investigates the potential of biogas recovery from used engine oil (UEO) by co-digestion with animals’ manure, including cow dung (CD), poultry manure (PM), and cattle manure (CM). The experimental work was carried out in anaerobic biodigesters at mesophilic conditions (37°C). Two groups of biodigesters were prepared. Each group consisted of 4 digesters. UEO was the main component in the first group of biodigesters with and without inoculum, whereby a mix of UEO and petroleum refinery oily sludge (ROS) was the component in the second group of biodigesters. The results revealed that for UEO-based biodigesters, maximum biogas production was 0.98, 1.23, 1.93, and 0 ml/g VS from UEO±CD, UEO±CM, UEO±PM, and U
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