- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
DRUM BOILER LEVEL CONTROL USING NEURAL NETWORK | ||
| AL-TAQANI | ||
| Article 1, Volume 18, Issue 2, 2005, Pages 18-26 | ||
| Authors | ||
| Saad Ali Ahmed; Saad Shakir Tawfiq; Sami Abunoun Ajeel | ||
| Abstract | ||
| This paper deals with the control of water level in the drum boilers which are obviously used in electrical power plants. Efficient techniques are applied as a kind of artificial intelligence known as neural networks. Identification networks are trained offline using real process data recorded at Al-Daura power plant / unit no.4. Drum level is selected as an output of identification network while pressure, feed water flow, steam flow are selected as input. The first step of work was to find network with the least number of hidden neurons and the least value of mean square error. The network is simulated and its output is plotted with the real data. Two types of networks are tested feed forward neural networks and recurrent neural networks. The predicted mean square error is more than 1.0 E-04 for feed forward networks and less than 1.0 E-06 for recurrent networks. The second stage is the prediction of the inverse model. A MIMO system network was used for this purpose. Computer programs are implemented using neural network tool box which is available in MATLAB version 6.5. | ||
|
Statistics Article View: 155 PDF Download: 62 |
||