Gas hydrate formation poses a significant threat to the production, processing, and transportation of natural gas. Accurate predictions of gas hydrate equilibrium conditions are essential for designing the gas production systems at safe operating conditions and mitigating the problems caused by hydrates formation. A new hydrate correlation for predicting gas hydrate equilibrium conditions was obtained for different gas mixtures containing methane, nitrogen and carbon dioxide. The new correlation is proposed for a pressure range of 1.7-330 MPa, a temperature range of 273-320 K, and for gas mixtures with specific gravity range of 0.553 to 1. The nonlinear regression technique was applied to develop the correlation based on 142 experimental data points collected from literature, validated with 85 data points not used for developing the correlation. The statistical parameters analysis showed an average absolute error (AAPE) of 0.2183, a squared correlation coefficient (R2) of 0.9978 and standard deviation (SD) of 0.2483. In addition, comparing the new correlation results with the experimental data and with those calculated by other correlations show an excellent performance for the investigated range.
In this paper, a single-phase boost type ac-dc converter with power factor correction (PFC) technique is designed and implemented. A current mode control at a constant switching frequency is used as a control strategy for PFC converter. The PFC converter is a single-stage singleswitch boost converter that uses a current shaping technique to reshape the non-sinusoidal input current drawn by the bulky capacitor in the conventional rectifier. This technique should provide an input current with almost free-harmonics, comply with the IEC61000-3-2 limits, and a system operates with near unity power factor. The other function of the boost converter that should be
accomplished is to provide a regulated DC output voltage. The complete designed
A theoretical and experimental investigation was carried out to study the behavior of a two-phase closed thermosyphon loop (TPCTL) during steady-state operation using different working fluids. Three working fluids were investigated, i.e., distilled water, methanol, and ethanol. The TPCTL was constructed from an evaporator, condenser, and two pipelines (riser and downcomer). The driving force is the difference in pressure between the evaporator and condenser sections and the fluid returns to the heating section by gravity. In this study, the significant parameters used in the experiments were filling ratios (FR%) of 50%, 75%, and 100% and heat-input range at the evaporator section of 215-860.2 W. When the loop reached to
... Show MoreThe optimization of artificial gas lift techniques plays a crucial role in the advancement of oil field development. This study focuses on investigating the impact of gas lift design and optimization on production outcomes within the Mishrif formation of the Halfaya oil field. A comprehensive production network nodal analysis model was formulated using a PIPESIM Optimizer-based Genetic Algorithm and meticulously calibrated utilizing field-collected data from a network comprising seven wells. This well group encompasses three directional wells currently employing gas lift and four naturally producing vertical wells. To augment productivity and optimize network performance, a novel gas lift design strategy was proposed. The optimization of
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The adoption of many mathematical concepts contributes to the construction of models of sports and the population can be interpreted to explain the movement and growth of the population lead to proper planning to manage the requirements of the population and meet their needs of providing education or providing medical services, health and others. In this study, the number of births in the Governorate of Basrah for the period (1998-2050) is estimated to be based on the assumption that the population of the visually impaired is a stable society. If the rate of growth is (0.0492), some demographic indicators are important for maintaining the average age of women at pregnancy (27.817). Each woman will give birth (3.74) female birth d
... Show MoreThe aim of this work is to detect the best operating conditions that effect on the removal of Cu2+, Zn2+, and Ni2+ ions from aqueous solution using date pits in the batch adsorption experiments. The results have shown that the Al-zahdi Iraqi date pits demonstrated more efficient at certain values of operating conditions of adsorbent doses of 0.12 g/ml of aqueous solution, adsorption time 72 h, pH solution 5.5 ±0.2, shaking speed 300 rpm, and smallest adsorbent particle size needed for removal of metals. At the same time the particle size of date pits has a little effect on the adsorption at low initial concentration of heavy metals. The adsorption of metals increases with increas
... Show MoreVarious theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp
... Show MoreDeep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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