The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

Background: Abdominoplasty is one of the commonest surgical procedures that performed for those patients who had skin laxity, strive and muscle rectur diastasis. Combined using of liposuction and abdominoplasty it can give better result than traditional abdominoplasty with fear complications. Patient and Method: A total number of 25 female patients with age ranging between 27-55 years were underwent lipoabdominoplasty. With extensive liposuction of abdominal wall and selective undermining together with muscle plication. All of our patients had body mass index more than 30. Patients satisfaction and complication were documented postoperatively. Result: All of our patients had no Major complications and the postoperative period passed unevent

With growing global demand for hydrocarbons and decreasing conventional reserves, the gas industry is shifting its focus in the direction of unconventional reservoirs. Tight gas reservoirs have typically been deemed uneconomical due to their low permeability which is understood to be below 0.1mD, requiring advanced drilling techniques and stimulation to enhance hydrocarbons. However, the first step in determining the economic viability of the reservoir is to see how much gas is initially in place. Numerical simulation has been regarded across the industry as the most accurate form of gas estimation, however, is extremely costly and time consuming. The aim of this study is to provide a framework for a simple analytical method to esti

The current research discusses the topic of the formal data within the methodological framework through defining the research problem, limits and objectives and defining the most important terms mentioned in this research. The theoretical framework in the first section addressed (the concept of the Bauhaus school, the philosophy of the Bauhaus school and the logical bases of this school). The second section dealt with (the most important elements and structural bases of the Bauhaus school) which are considered the most important formal data of this school and their implications on the fabrics and costumes design. The research came up with the most important indicators resulting from the theoretical framework.
Chapter three defined the

Reactive Powder Concrete (RPC) could be considered as the furthermost significant modern high compressive strength concrete. In this study, an experimental investigation on the impact of micro steel fiber volume fraction ratio and gamma ray irradiation duration influence upon the compressive strength of RPC is presented. Three volume fraction ratios (0.0, 1.0 and 1.5) % was implemented. For each percentage of the adopted fiber ratios, six different irradiation duration was considered; these are (1, 2, 3, 4, 5 and 6) days. Gamma source (Cs-137) of energy (0.662) MeV and activity (6) mci was used. In a case of zero volume fraction ratio, the experimental results showed that gamma ray had a significant influence on the reducing of the

Reactive Powder Concrete (RPC) could be considered as the furthermost significant modern high compressive strength concrete. In this study, an experimental investigation on the impact of micro steel fiber volume fraction ratio and gamma ray irradiation duration influence upon the compressive strength of RPC is presented. Three volume fraction ratios (0.0, 1.0 and 1.5) % was implemented. For each percentage of the adopted fiber ratios, six different irradiation duration was considered; these are (1, 2, 3, 4, 5 and 6) days. Gamma source (Cs-137) of energy (0.662) MeV and activity (6) mci was used. In a case of zero volume fraction ratio, the experimental results showed that gamma ray had a significant influence on the reducing of the

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

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the