@article{4c85f769ecc545b98cd484c62b84638c,
title = "Optimization of thermal efficiency and unburned carbon in fly ash of coal-fired utility boiler via grey wolf optimizer algorithm",
abstract = "This paper focuses on improving thermal efficiency and reducing unburned carbon in fl ash by optimizing operating parameters via a novel high-efficient swarm intelligence optimization algorithm (grey wolf optimizer algorithm,GWO) for coal-fired boiler. Mathematical models for thermal efficiency and unburned carbon in fl ash of the discussed boiler are established by artificial neural network (ANN). Based on the ANN models, the grey wolf optimizer algorithm is used to obtain higher thermal efficiency and lower unburned carbon by optimizing the operating parameters. Meanwhile, the comparisons between GWO and particle swarm optimization (PSO) and genetic algorithm (GA) show thatGWO has superior performance to GA and PSO regarding the boiler combustion optimization. The proposed method can accurately optimize the boiler combustion performance, and its validity and feasibility have been experimentally validated. Additionally, a run of optimization takes a less time period, which is suitable for the real-time optimization.",
keywords = "Coal-fired utility boiler, Grey wolf optimizer, Thermal efficiency, Unburned carbon in fl ash",
author = "Yiding Zhao and Qinghe Wu and Heng Li and Shuhua Ma and Ping He and Jun Zhao and Yangmin Li",
note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 11705122, in part by the Department of Building and Real Estate of The Hong Kong Polytechnic University, in part by the General Research Fund entitled {\textquoteleft}{\textquoteleft}Proactive Monitoring of Work-Related MSD Risk Factors and Fall Risks of Construction Workers Using Wearable Insoles{\textquoteright}{\textquoteright} under Grant BRE/PolyU 152099/18E, in part by the General Research Fund entitled {\textquoteleft}{\textquoteleft}In search of a suitable tool for proactive physical fatigue assessment: an invasive to non-invasive approach{\textquoteright}{\textquoteright} under Grant PolyU 15204719/18E, in part by the Natural Science Foundation of The Hong Kong Polytechnic University under Grant G-YW3X, in part by the Research Foundation of Department of Education of Sichuan Province under Grant 17ZA0271 and Grant 18ZA0357, in part by the Sichuan Science and Technology Program of China under Grant 19ZDZX0037, Grant 2019YFSY0045, Grant 2018JY0197, and Grant 2016SZ0074, in part by the Sichuan Key Provincial Research Base of Intelligent Tourism under Grant ZHZJ18-01, in part by the Open Foundation of Artificial Intelligence Key Laboratory of Sichuan Province under Grant 2018RZJ01 and Grant 2017RZJ02, and in part by the Nature Science Foundation of Sichuan University of Science and Engineering under Grant 2017RCL52, Grant 2018RCL18, and Grant 2017RCL12. Publisher Copyright: {\textcopyright} 2020 Cambridge University Press. All rights reserved.",
year = "2019",
doi = "10.1109/ACCESS.2019.2935300",
language = "English",
volume = "7",
pages = "114414--114425",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "IEEE",
}