paper of Adsorption

Personal Information:
Name: Nabeel Abdul-Fattah Abdul Rahman Abu Shaban
د.نبيل عبد الفتاح عبد الرحمن ابو شعبان
Publications:
-M. S. Ashhab, N. Abu Shaban and A. Olimat, Neural Net Based Optimization of Wet Thermal Lateral Oxidation Rates, Sensors & Transducers, Vol. 133, No. 10, 2011, pp. 8-17
-M. S. Ashhab, A. Oimat and N. Abu Shaban, Prediction of the Surface Oxidation Process of
AlCuFe Quasicrystals by Using Artificial Neural Network Techniques, Sensors & Transducers,Vol. 128, No. 5, 2011, pp. 55-65.
-PERFORMANCE AND EVALUATION OF ADSORPTION CHILLERS POWERED BY SOLAR ENERGY BY MEANS OF PTC’S IN JORDANN. ABU SHABAN, A.MAAITAH,A. SALAYMEH, 5th. JIIRCRAC’15, Aqaba-Jordan, 11 – 14 March.2015

Neural Net Based Optimization of Wet Thermal Lateral Oxidation Rates
MOH’D SAMI S. ASHHAB, NABEEL ABU SHABAN and ABDULLA N. OIMAT
Department of Mechanical Engineering
The Hashemite University
The University of Jordan
Zarqa, Amman, Jordan
E-mail: sami@hu.edu.jo, aboshaban65@yahoo.com, olimat2008@yahoo.com,
Received: 29 September 2011 /Accepted: 25 October 2011 /Published: 31 October 2011
Abstract: Critical parameters, AlAs mole fraction, temperature of the sample and the carrier gas flow must be controlled to establish a repeatable and uniform oxidation process. Modeling and simulation of these parameters has enabled the compilation of oxidation rate data for AlGaAs which exhibits Arrhenius rate dependence. The output is related to the inputs of the process by an artificial neural net model which is trained with historical input-output data. The data is originally extracted and manipulated from experimental laboratories measurements. The proposed method is tested through computer simulation and the results demonstrate the effectiveness of the code and the algorithm. The objective of this study is the prediction of lateral oxidation rates at variances of temperature and mole fraction for different compositions. This is done through optimization techniques.
Copyright © 2011 IFSA.
Keywords: Experimental measurements, Neural networks, Optimization, Modeling, MEMS lateral oxidation.

Prediction of the Surface Oxidation Process of AlCuFe Quasicrystals by Using Artificial Neural Network Techniques

MOH’D SAMI S. ASHHAB, ABDULLA N. OIMAT and NABEEL ABU SHABAN
Department of Mechanical Engineering
The Hashemite University
The University of Jordan
Zarqa, Amman, Jordan
E-mail: sami@hu.edu.jo, olimat2008@yahoo.com, aboshaban65@yahoo.com

Received: 23 March 2011 /Accepted: 20 May 2011 /Published: 28 May 2011

Abstract: In this paper, we present a method to determine the inputs of a manufacturing process used in Microelectromechanical System (MEMS) that will drive its output to desired targets. This method uses a combination of artificial neural network (ANN) modeling and the inverse control together with optimization techniques in order to obtain the minimum error between the neural net results and the desired values. The problem aims to find the depth of thin film layer that we needed for the surface oxidation for the preparation of i-AlCuFe quasicrystals, which is the output of the process, by giving the percentage of oxygen concentration and temperature, which are the inputs of the process. The outputs are related to the inputs of the process by an artificial neural net model which is trained and tested with historical input-output data. The final results of the developed neural net model and the inverse control techniques show high level of the accuracy of the results.

Keywords: : Artificial Neural Network, MEMS, Oxidation, Optimization, Quasicrystals, i-AlCuFe, Inverse control.

CV

Dr.Nabeel Abu Shaban

Phd. in mechanical engineering (Thermal systems , Energy)

Courses taught:

Ac

Heat transfer

Fluid dynamics

Solar Energy

Engineering drawing

Machine drawing