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In relation to this article, we declare that there is no conflict of interest.
Publication history
Received August 28, 2006
Accepted March 9, 2007
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Modeling and coordinative optimization of NOx emission and efficiency of utility boilers with neural network

Key laboratory for Thermal Science and Power Engineering of Ministry of Education,Department of Thermal Engineering, Tsinghua University, Beijing 100084, China
Korean Journal of Chemical Engineering, November 2007, 24(6), 1118-1123(6), 10.1007/s11814-007-0131-6
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Abstract

An empirical model to predict the boiler efficiency and pollutant emissions was developed with artificial neural networks based on the experimental data on a 360MW W-flame coal fired boiler. The temperature of the furnace was selected as an intermediate variable in the hybrid model so that the predictive precision of NOx emissions was enhanced. The predictive precision of the hybrid model was improved compared with the direct model. Three optimal operational objects were proposed in order to minimize the fuel and environmental costs. Based on the neural network model and optimal objects, a genetic algorithm was employed to seek real-time solution every 30 seconds. Optimum manipulated variables such as excess air, primary air and secondary air were obtained under different optimal objects. The above algorithm interconnected with a distributed control system (DCS) formed the supervisory control and achieved real-time coordinated optimization control of utility boilers.

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