Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75775
Title: Deep bidirectional learning machine for predicting NOx emissions and boiler efficiency from a coal-fired boiler
Authors: Li, GQ 
Qi, XB
Chan, KCC 
Chen, B
Issue Date: 2017
Publisher: American Chemical Society
Source: Energy & fuels : an American Chemical Society journal, 2017, v. 31, no. 10, p. 11471-11480 How to cite?
Journal: Energy & fuels : an American Chemical Society journal 
Abstract: Combustion optimization is one of the effective techniques to enhance boiler efficiency and reduce nitrogen oxide (NOx) emissions from coal-fired boilers. A precise NOx emission model and a boiler efficiency model are the basis of implementing real-time combustion optimization and are required. In this study, to obtain very precise models and make full use of abundant real-time operational data easily collected from supervisory information systems (SIS), a novel deep learning algorithm called a deep bidirectional learning machine (DBLM) is proposed to set up the correlation between NOx, emissions, boiler efficiency, and operational parameters from a 300 MW circulating fluidized bed boiler (CFBB). Experimental results indicate that, in comparison to other recently published state-of-the-art modeling methods, the models built by DBLM could own much better generalization performance and high repeatability, which may be a better choice for modeling NOx emissions and efficiency in achieving boiler combustion optimization and improving power plant performance.
URI: http://hdl.handle.net/10397/75775
ISSN: 0887-0624
EISSN: 1520-5029
DOI: 10.1021/acs.energyfuels.7b01415
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