This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

The performance of sewage pumps stations affected by many factors through its work time which produce undesired transportation efficiency. This paper is focus on the use of artificial neural network and multiple linear regression (MLR) models for prediction the major sewage pump station in Baghdad city. The data used in this work were obtained from Al-Habibia sewage pump station during specified records- three years in Al-Karkh district, Baghdad. Pumping capability of the stations was recognized by considering the influent input importance of discharge, total suspended solids (TSS) and biological oxygen demand (BOD).  In addition, the chemical oxygen demands (COD), pH and chloride (Cl). The proposed model performanc

In this work, a convex lens concentrating solar collector is designed and manufactured locally by using 10 convex lenses (concentrator) of a diameter 10cm and one Copper absorber tube of a diameter 12.5mm and 1mm in thickness 1m length. Two axes manual Tracking system also constructed to track the sun continuously in two directions. The experiments are made on 17^th of May 2015 in climatic conditions of Baghdad. The experimental data are fed to a computer program to solve the thermal performing equation, to find efficiency and actual useful energy. Then this data is used in numerical CFD software for three different absorber diameters (12.5 mm, 18.75 mm and 25 mm). From the results that obtained the maximum the

A new method, simple and sensitive was utilized in determining mebeverine – HCl (MB-HCl) (3, 4-Dimethoxy benzoic acid ethyl 2, 4 methoxy4-phenyl-1-methyl ethyl amino-butyl ester) in pure and pharmaceutical formulations via utilization this multiple continuous flow cell. The method is dependent on genesis for complex of ion pair(4-((3, 4-dimethoxybenzoyl) oxy)-N-ethyl-N-(1-(4-methoxyphenyl) propan-2-yl) butan-1-aminium-2-hydroxy-3,5- dinitrobenzoate) among mebeverine–HCl (MB-HCl) and 3,5-Dinitrosalicylic acid (3,5-DNSA) in ammonium acetate middle to configure a whiteish yellow precipitate compound via utilizing multiple continuous flow cell that works as a solo flow cell with 4S×3 – 3D analyzer. Optimum parameters were studied to rai

Diyala river is the most important tributaries in Iraq, this river suffering from pollution, therefore, this research aimed to predict organic pollutants that represented by biological oxygen demand BOD, and inorganic pollutants that represented by total dissolved solids TDS for Diyala river in Iraq, the data used in this research were collected for the period from 2011-2016 for the last station in the river known as D17, before the river meeting Tigris river in Baghdad city. Analysis Neural Network ANN was used in order to find the mathematical models, the parameters used to predict BOD were seven parameters EC, Alk, Cl, K, TH, NO3, DO, after removing the less importance parameters. While the parameters that used to predict TDS were fourte

KE Sharquie, AA Noaimi, Journal of the Saudi Society of Dermatology & Dermatologic Surgery, 2012 - Cited by 36