A mathematical framework to model the heat transfer efficiency of cooking pots is proposed and exploited in this paper. The model consists of combining the experimental results and the statistical data of Residential Energy Consumption Survey (RECS) of Iran with a soft-computing concept such as neural network. Using neural network results, the variations of the efficiency with various parameters have been studied. It is shown that Group Method of Data Handling (GMDH)-type neural network can effectively model and predict thermal efficiency, as a function of important input parameters for a conventional cooking pot. Results show that efficiency increases with increasing diameter to flame ratio, bottom wall curvature, pot wall slope, and overall conductivity. With increasing edge radius and pot height to pot diameter ratio, efficiency decreases. Occupied volume percentage does not have a significant effect on efficiency.
Bibliographical noteFunding Information:
The present work is conducted as part of a project funded by IFCO, Iran. The authors of this paper wish to express their sincere gratitude towards Professor Narimanzadeh from Guilan University, Iran, for his fruitful discussions. Part of this paper is inspired from the thesis of third author under the supervision of Professor Narimanzadeh.
- Cooking pots
- Energy saving
- Neural network
- Thermal efficiency