Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Wind Energy

Effect of Some Vegetables (Carrots, Onion, Parsley, and Red radish) on Corrosion Behavior of Amalgam Dental Filling in Artificial Saliva

Slafa Ismael Ibrahim; Nemir Ahmed Al-Azzawi; Shatha Mizhir Hasan; Hussein H. Karim; Ammar M. M. Al-Qaissi; Ahmed Chyad Kadhim; Mehdi Munshid Shellal; Sinan Majid Abdul Satar; Wahid S. Mohammad; Assad Oda Jassim; Khalid salem Shibib; Karema Assi Hamad; Haqui Ismael Qatta; Hayder Hadi Abbas; Kanaan A. Jalal; Hussain Kassim Ahmad; Makram A. Fakhri; Mohanned M.H. AL-Khafaji; Hussam Lefta Alwan; Baraa M.H. Albaghdadi

Engineering and Technology Journal, 2014, Volume 32, Issue 5, Pages 1216-1226

This work involves study corrosion behavior of amalgam in presence of some vegetables including (Carrots, Onion, Parsley, and Red radish) which were chosen because they require mastication process by teeth and taking enough time that make them in a contact with amalgams filling in artificial saliva.
The corrosion parameters were interpreted in artificial saliva at pH (5.1) and (37±1oC) by adding (50 ml/l) of vegetable juice to artificial saliva, which involve corrosion potential (Ecorr), corrosion current density (icorr), Cathodic and anodic Tafel slopes (bc & ba ) and polarization resistance, the results of (Ecorr) and (icorr) indicate that the medium of saliva and (50 ml/l) onion is more corrosive than the other media. Cathodic and anodic tafel slopes were used to calculate the polarization resistance (Rp) to know which medium more effective on amalgam of dental filling, this study shows that the increasing in polarization resistance through the decreasing in corrosion rate values, the results of (Rp) take the sequence:
Rp:( saliva+ parsley) >( saliva+ red radish)> saliva>(saliva+ carrots) >(saliva+ onion).
While corrosion rates (CR ) take the sequence:
CR: (Saliva+Parsley) Keywords

Corrosion in saliva
Potentiostatic measurements

Optimal Identification of Doubly Fed Induction Generator Parameters in Wind Power System using Particle Swarm Optimizationand Artificial Neural Network

Kanaan A. Jalal; Hussain Kassim Ahmad

Engineering and Technology Journal, 2014, Volume 32, Issue 5, Pages 1308-1322

Wind energy became one of the techniques that attracted much attention worldwide. The induction generator is used in the exploitation of this energy and converts it into electrical energy because of the advantages that distinguish it from other types of generators. In this paper, an optimal identification of induction generator parameters is proposed. Particle Swarm Optimization technique (PSO) trained using Artificial Neural Network (ANN) is used to identify the main parameters of the induction generator in cases of wind speed change, load change and fault cases.
The simulation results obtained indicate that the particle swarm optimization is suitable for neural networks training for controlling of the voltage, frequency and generated power. The simulation programming is implemented using MATLAB.