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Performance prediction of a RPF‐fired boiler using artificial neural networks
Author(s) -
Behera Shishir Kumar,
Rene Eldon R.,
Kim Min Choul,
Park HungSuck
Publication year - 2013
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.3108
Subject(s) - boiler (water heating) , artificial neural network , mass flow rate , incineration , mass flow , volumetric flow rate , engineering , process engineering , boiler feedwater , approximation error , waste management , computer science , mathematics , artificial intelligence , statistics , mechanics , physics
SUMMARY In order to provide adequate engineering assistance and to improve the energy efficiency in process industries, it is crucial to evaluate the operational performance of a boiler in terms of its practical requirements, viz. temperature, pressure, and mass flow rate of steam. This study was aimed at assessing and optimizing the performance of a refuse plastic fuel‐fired boiler using artificial neural networks. A feed‐forward back propagation neural network model was developed and trained using existing plant data (5 months), to predict temperature, pressure, and mass flow rate of steam, using the following input parameters: feed water pressure, feed water temperature, conveyor speed, and incinerator exit temperature. The predictive capability of the model was evaluated in terms of mean absolute percentage error between the model fitted and actual plant data, while sensitivity analysis was performed on the input parameters by determining the absolute average sensitivity values. The higher absolute average sensitivity value of the incinerator exit temperature in comparison to that of feed water pressure, feed water temperature and conveyor speed suggested that the change of incineration exit temperature has a significant influence on the selected outputs (steam properties). Overall, the good results observed from this work demonstrate the fact that artificial neural networks can efficiently predict the data on steam properties and could serve as a good tool to monitor boiler behavior under real‐time conditions. Copyright © 2013 John Wiley & Sons, Ltd.