Abstract
The Rankine cycle plays a very important role in most steam power plants; much of this analysis needs to be done, especially by undergraduate thermodynamics students. Therefore, it is very necessary to obtain the Rankine cycle efficiencies quickly and accurately. In the present work, we have done this by using a neural network - a branch of artificial intelligence. A wide range of the Rankine cycle efficiencies were calculated using the classic efficiency formula with data excerpted from a Mollier Chart and, a neural network was set up to learn from these known facts. Then, a test data group was chosen to check the predictions made by the trained neural network and, it was found that all the predictions by the trained network matched with the theoretical analysis. The training time was not long and, the prediction accuracy could be improved by adjusting the network operational parameters. After the neural network was trained, tedious calculations and table searching become unnecessary because trained network acts as an expert; if a student inputs the temperature and pressures, the network predicts the efficiency. The whole system is user friendly and easy to understand. Besides, the present work shows that the neural network approach is not only suitable for analysis of simple ideal Rankine cycles, but also could be modified for practical Rankine cycles (i.e. reheat, etc.). In the classroom, the trained network might also be used to find the optimum Rankine cycle operational parameters under constraints of pressure and temperature ranges.
| Original language | English |
|---|---|
| Title of host publication | American Society of Mechanical Engineers, Advanced Energy Systems Division (Publication) AES |
| Place of Publication | New York, NY, United States |
| Publisher | Publ by ASME |
| Pages | 199-204 |
| Number of pages | 6 |
| Volume | 27 |
| ISBN (Print) | 0791810739 |
| State | Published - Dec 1 1992 |
| Event | Winter Annual Meeting of the American Society of Mechanical Engineers - Anaheim, CA, USA Duration: Nov 8 1992 → Nov 13 1992 |
Other
| Other | Winter Annual Meeting of the American Society of Mechanical Engineers |
|---|---|
| City | Anaheim, CA, USA |
| Period | 11/8/92 → 11/13/92 |
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ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Mechanical Engineering
Cite this
Rankine cycle analysis using a neural network. / Wong, Kaufui; Yao, Kang.
American Society of Mechanical Engineers, Advanced Energy Systems Division (Publication) AES. Vol. 27 New York, NY, United States : Publ by ASME, 1992. p. 199-204.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Rankine cycle analysis using a neural network
AU - Wong, Kaufui
AU - Yao, Kang
PY - 1992/12/1
Y1 - 1992/12/1
N2 - The Rankine cycle plays a very important role in most steam power plants; much of this analysis needs to be done, especially by undergraduate thermodynamics students. Therefore, it is very necessary to obtain the Rankine cycle efficiencies quickly and accurately. In the present work, we have done this by using a neural network - a branch of artificial intelligence. A wide range of the Rankine cycle efficiencies were calculated using the classic efficiency formula with data excerpted from a Mollier Chart and, a neural network was set up to learn from these known facts. Then, a test data group was chosen to check the predictions made by the trained neural network and, it was found that all the predictions by the trained network matched with the theoretical analysis. The training time was not long and, the prediction accuracy could be improved by adjusting the network operational parameters. After the neural network was trained, tedious calculations and table searching become unnecessary because trained network acts as an expert; if a student inputs the temperature and pressures, the network predicts the efficiency. The whole system is user friendly and easy to understand. Besides, the present work shows that the neural network approach is not only suitable for analysis of simple ideal Rankine cycles, but also could be modified for practical Rankine cycles (i.e. reheat, etc.). In the classroom, the trained network might also be used to find the optimum Rankine cycle operational parameters under constraints of pressure and temperature ranges.
AB - The Rankine cycle plays a very important role in most steam power plants; much of this analysis needs to be done, especially by undergraduate thermodynamics students. Therefore, it is very necessary to obtain the Rankine cycle efficiencies quickly and accurately. In the present work, we have done this by using a neural network - a branch of artificial intelligence. A wide range of the Rankine cycle efficiencies were calculated using the classic efficiency formula with data excerpted from a Mollier Chart and, a neural network was set up to learn from these known facts. Then, a test data group was chosen to check the predictions made by the trained neural network and, it was found that all the predictions by the trained network matched with the theoretical analysis. The training time was not long and, the prediction accuracy could be improved by adjusting the network operational parameters. After the neural network was trained, tedious calculations and table searching become unnecessary because trained network acts as an expert; if a student inputs the temperature and pressures, the network predicts the efficiency. The whole system is user friendly and easy to understand. Besides, the present work shows that the neural network approach is not only suitable for analysis of simple ideal Rankine cycles, but also could be modified for practical Rankine cycles (i.e. reheat, etc.). In the classroom, the trained network might also be used to find the optimum Rankine cycle operational parameters under constraints of pressure and temperature ranges.
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UR - http://www.scopus.com/inward/citedby.url?scp=0026987602&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0026987602
SN - 0791810739
VL - 27
SP - 199
EP - 204
BT - American Society of Mechanical Engineers, Advanced Energy Systems Division (Publication) AES
PB - Publ by ASME
CY - New York, NY, United States
ER -