A set of hydro treating experiments are carried out on vacuum gas oil in a trickle bed reactor to study the hydrodesulfurization and hydrodenitrogenation based on two model compounds, carbazole (non-basic nitrogen compound) and acridine (basic nitrogen compound), which are added at 0–200 ppm to the tested oil, and dibenzotiophene is used as a sulfur model compound at 3,000 ppm over commercial CoMo/ Al2O3 and prepared PtMo/Al2O3. The impregnation method is used to prepare (0.5% Pt) PtMo/Al2O3. The basic sites are found to be very small, and the two catalysts exhibit good metal support interaction. In the absence of nitrogen compounds over the tested catalysts in the trickle bed reactor at temperatures of 523 to 573 K, liquid hourly space velocity 1 to 3 hr−1 , and a pressure range of 16 to 20 bar, the results show an increase in conversion from 0.2214 to 0.6748 and 0.2920 to 0.7341 for CoMo and PtMo, respectively, with the increase of temperature, a little positive effect on conversions when pressure increases, and a significant decrease in conversion: 0.6748 to 0.3284 and 0.7341 to 0.3734 for CoMo and PtMo, respectively, when liquid hourly space velocity increases. The results showed a first-order kinetic of Dibenzothiphene (DBT) hydrodesulphurization. The activation energies are 75.399 and 67.983 kJ/mol for hydrodesulphurization of DBT over CoMo and PtMo, respectively.
Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
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Thin films of Zinc Selenide ZnSe have been prepared by using thermal evaporation in vacuum technique (10-5Torr) with thickness (1000, 2700, 4000) A0 and change electrode material and deposited on glass substrates with temperature (373K) and study some electrical properties at this temperature . The graphs shows linear relation between current and voltage and the results have shown increases in the value of current and electrical conductivity with increase thickness and change electrode material from Aluminum to Copper
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Background: Pain due to muscular cramp during hemodialysis is one of the most common problems experienced by patient undergoing hemodialysis, and is associated with poor outcomes of patients. The main aim of this study was to comparing the effects of lavender oil and olive oil massage on Pain due to muscular cramp during hemodialysis.
Methods: In this random clinical trial, 60 hemodialysis patients were enrolled randomly and allocated to two groups with 30 members in Lordegan and Brojen hospitals, Shahrekord, Iran. The intervention included flora massage on the lower leg muscles so that the first group received olive oil massage (10 drops) and the second group received lavender oil massage (10 dr
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This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul
... Show MorePatients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated
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The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model
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In this research, non-thermal plasma system of argon gas is designed to work at normal atmospheric pressure and suitable for work in medical and biotechnological applications. This technique is applied in the treatment of the Staphylococcus epidermidis bacteria and show the role of the flow rate of Argon gas on the killing rate of bacteria, and it obtained a 100 % killing rate during the time of 5 minutes at the flow Argon gas of 5 liters/ min.
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