Title
A comparative study of the multi-objective optimization algorithms for coal-fired boilers
Abstract
Combustion optimization has been proved to be an effective way to reduce the NOx emissions and unburned carbon in fly ash by carefully setting the operational parameters of boilers. However, there is a trade-off relationship between NOx emissions and the boiler economy, which could be expressed by Pareto solutions. The aim of this work is to achieve multi-objective optimization of the coal-fired boiler to obtain well distributed Pareto solutions. In this study, support vector regression (SVR) was employed to build NOx emissions and carbon burnout models. Thereafter, the improved Strength Pareto Evolutionary Algorithm (SPEA2), the new Multi-Objective Particle Swarm Optimizer (OMOPSO), the Archive-Based hYbrid Scatter Search method (AbYSS), and the cellular genetic algorithm for multi-objective optimization (MOCell) were used for this purpose. The results show that the hybrid algorithms by combining SVR can obtain well distributed Pareto solutions for multi-objective optimization of the boiler. Comparison of various algorithms shows MOCell overwhelms the others in terms of the quality of solutions and convergence rate.
Year
DOI
Venue
2011
10.1016/j.eswa.2010.12.042
Expert Syst. Appl.
Keywords
Field
DocType
mocell,multi-objective optimization,archive-based hybrid,unburned carbon,nox emission,omopso,combustion,spea2,pareto solution,carbon burnout model,comparative study,abyss,improved strength pareto evolutionary,coal-fired boiler,boiler economy,support vector regression,combustion optimization,multi-objective optimization algorithm,fly ash,multi objective optimization,hybrid algorithm,convergence rate
Pulverized coal-fired boiler,Combustion,Mathematical optimization,Evolutionary algorithm,Computer science,Support vector machine,Algorithm,Multi-objective optimization,Rate of convergence,Boiler (power generation),Pareto principle
Journal
Volume
Issue
ISSN
38
6
Expert Systems With Applications
Citations 
PageRank 
References 
2
0.37
14
Authors
4
Name
Order
Citations
PageRank
Feng Wu120.71
Hao Zhou2234.66
Jia Pei Zhao3161.06
Kefa Cen4193.54