文章基本信息

标题：PREDICTING POLLUTANT EMISSIONS OF A 300MW CFBB USING BIDIRECTIONAL LEARNING MACHINE
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作者：Guo-Qiang Li ; Lu Zhang ; Keith C C Chan 等
期刊名称：Fresenius Environmental Bulletin
印刷版ISSN：1018-4619
出版年度：2017
卷号：26
期号：12A
页码：7906-7913
语种：English
出版社：PSP Publishing
摘要：With the rapid development of economy in these years, the condition has become more and more severe that human being have polluted environment. In order to implement and reduce pollutant emissions for a power plant, an accurate model is required. This study proposed an improved Extreme Learning Machine (ELM) called Bidirectional learning machine (BLM) to establish precise and rapid models of pollutant emissions involving NOx emissions and Dust content in flue gas for a 300MW Circulating fluidized bed boiler (CFBB). Experimental results show, compared with two recently published state-of-the-art algorithms Extreme Learning Machine (ELM) and Least Square Fast Learning Network (LSFLN), BLM with less running time could achieve better generalization performance and more highly repeatability and reproducibility for predicting pollutant emissions.
关键词：Circulating fluidized bed boiler (CFBB);NOx emissions;Dust content in flue gas;Extreme Learning Machine