Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77482
Title: Modeling thermal efficiency of a 300 MW coal-fired boiler by Online Least Square Fast Learning Network
Authors: Li, GQ 
Chen, B
Chan, KCC 
Qi, XB
Keywords: Coal-fred boiler
Modeling
Online least
Square fast learning network
Thermal efciency
Issue Date: 2018
Publisher: Society of Chemical Engineers, Japan
Source: Journal of chemical engineering of Japan, 2018, v. 51, no. 1, p. 100-106 How to cite?
Journal: Journal of chemical engineering of Japan 
Abstract: Improving boiler thermal efficiency plays a very important role in the economic development of power plants. In order to implement a real-time improvement in the boiler thermal efficiency, a precise and rapid online model of the thermal efficiency is required. The present paper presents an effective machine learning method called the Online Least Square Fast Learning Network (OLSFLN) to build a prediction model for 300 MW coal-fired boiler thermal efficiency. Experimental results demonstrate that the proposed OLSFLN could predict the boiler thermal efficiency with high accuracy and outperform in learning ability, generalization ability and repeatability under various boiler operating conditions than other state-of-the-art algorithms.
URI: http://hdl.handle.net/10397/77482
ISSN: 0021-9592
EISSN: 1881-1299
DOI: 10.1252/jcej.17we114
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