In this work, fluid catalytic cracking of vacuum gas oil to produce gasoline over prepared faujasite type Y zeolite was investigated using experimental laboratory plant scale of fluidized bed reactor.
The catalytic activity of prepared faujasite type NaY, NaNH4Y and NaHY zeolites was investigated. The cracking process was carried out in the temperature range 440 to 500 oC, weight hourly space velocity (WHSV) range 10 to 25 h-1 ,and atmospheric pressure . The catalytic activities of the prepared faujasite type NaY , NaNH4Y and NaHY zeolites were determined in terms of vacuum gas oil (VGO) conversion, and gasoline yield . The conversion at 500oC and WHSV10 hr-1 by using faujasite type NaY, NaNH4Y and NaHY zeolite were 50.2%, 64.1% and 69.5wt% respectively. The gasoline yield using the same operating conditions were 24.8%, 30.5% and 36.8wt% respectively. Gas chromatographic analysis of produced gasoline shows that the paraffin, olefin, and aromatic content change considerably with the end point temperature of gasoline fraction.
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In present study the effect of soil extracts of different types of soil on ability of two clinical isolates, Pseudomonas aeruginosa and Staphylococcus aureus to form biofilm. The extract of soil was done by using sterile phosphate buffer saline and analyzed by Fourier Transform Infrared Spectroscopic (FTIR). Spectrophotometric method was used to check ability of the studied isolated bacteria to form biofilm on polystyrene microtiter plates. The data of FTIR showed very little difference was observed among extracts of three types of soil (soil contaminated with hydrocarbons; garden soil collected from gardens of al-jadrea, Baghdad and containers soil), but the highest difference was observed in the extract obtained from peat moss clay soil.
... Show MoreThis review examines how artificial intelligence (AI) including machine learning (ML), deep learning (DL), and the Internet of Things (IoT) is transforming operations across exploration, production, and refining in the Middle Eastern oil and gas sector. Using a systematic literature review approach, the study analyzes AI adoption in upstream, midstream, and downstream activities, with a focus on predictive maintenance, emission monitoring, and digital transformation. It identifies both opportunities and challenges in applying AI to achieve environmental and economic goals. Although adoption levels vary across the region, countries such as Saudi Arabia, the UAE, and Qatar are leading initiatives that align with global sustainability targets.
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In the present study the performance of drying process of dffirent solid materials by batch fluidized bed drying
under vacuum conditions was investigated. Three, different solid materials, namely; ion exchange resin-8528,
aspirin and paracetamol were used. The behavior of the drying curves as well as the rate of drying of these
materials had been studied. The experiments were caried out in a 0.0381 m column diameter fluidized by hot
air under yacuum conditions. Four variables affecting on the rate of drying were studied' these variables are
vacuum pressure (100 - 500 mm Hg), air temperature (303-323 K), particle size (0.3-0.8 mm) and initial
moisture content (0.35-0.55 g/g solid)-for resin and (0.1-0.2 g/g soltid) for a
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Amaranthus viridis (F. Amaranthaceae), usually known as slender amaranth. It is a multifunctional vegetable that grows in tropical and subtropical areas, sustaining stressful conditions, especially drought, producing a wide array of secondary metabolites: phenolics, terpenes, carotenoids and others, which are responsible for various therapeutic effects. Aerial parts were defatted with hexane, then the defatted residue was extracted in the Soxhlet using 80 % aqueous ethanol. Ethanolic extract was sequentially and repeatedly partitioned with solvents of different polarities: chloroform, ethyl acetate and n-butanol. Then, the n-butanol fraction was hydrolyzed using reflex with 10 % aqueous HCl for 6 hrs. The unhydrolyzed and hydrolyz
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Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables
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Gas lift is one of the most important artificial lift methods for increasing oil production, as wells often require this method after the reservoir's energy has decreased. In this research, an optimal gas lift system is designed for five horizontal wells in the Ahdab oil field, which suffers from low production. At the same time, water cut in some of these wells reaches 66%, while the productivity index is low in others, which makes the challenges clear, and a deep analysis is needed to find an optimal system. The Pipesim program is used to design the optimal gas lift system, which contains features that facilitate the implementation of the appropriate design and provide the ability to analyze and determine the optimal design v
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