Application of Improved NSGA-II to Multi-objective Optimization of a Coal-fired Boiler Combustion Electronical Systems in Green Food Bases

Tingfang Yu,Ran Liu,Chunhua Peng

doi:10.19026/ajfst.10.2187

Abstract

In this study, we have a research of a hybrid algorithm by combining BP neural network and improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve the multi-objective optimization problem of a nanoscale coal-fired boiler combustion electronical systems, the two objectives considered are minimization of overall heat loss and NOx emissions from coal-fired boiler. First, Back Propagation (BP) neural network was dopted to establish a mathematical model predicting the NOx emissions and overall heat loss of the coal-fired boiler with the inputs such as operational parameters of the nanoscale coal-fired boiler. Then, BP model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) were combined to gain the optimal operating parameters which lead to lower NOx emissions and overall heat loss boiler. According to the problems such as premature convergence and uneven distribution of Pareto solutions exist in the application of NSGA-II, corresponding improvements in the crowded-comparison operator and crossover operator were performed. The optimization results show that hybrid algorithm by combining BP neural network and improved NSGA-II can be a good tool to solve the problem of multi-objective optimization of a nanoscale coal-fired boiler combustion electronical systems in green food bases, which can reduce NOx emissions and overall heat loss effectively for the nanoscale coal-fired boiler combustion electronical systems in green food bases. Compared with original NSGA-II, the Pareto set obtained by the improved NSGA-II shows a better distribution and better quality.

Highlights

In China, the requirements for environmental protection are increasingly strict, especially for coalfired utility boiler
According to the problems such as premature convergence and uneven distribution of Pareto solutions exist in the application of Non-dominated Sorting Genetic Algorithm-II (NSGA-II), corresponding improvements in the crowded-comparison operator and crossover operator were performed in this study
NSGA-II to coal-fired boiler multi-objective optimization problem, we found that the distribution of the obtained Pareto-optimal solution set is not very satisfactory, a large number of the solutions often concentrated in some regions, this phenomenon of premature convergence of NSGA-II is the same mechanism as the conventional genetic algorithm15, Since the NSGA-II was developed from genetic algorithm

Summary

Introduction

In China, the requirements for environmental protection are increasingly strict, especially for coalfired utility boiler. Coal remains the primary energy resource in China and one of the major concerns associated with coal-fired power plants is the emission of pollutants, especially for NO2 and NO (collectively referred to as NOx). Many old-designed utility boilers in China emit the NOx pollutants above the limit and have posed terrible threat to the surrounding environment, coal-fired power plants face important challenges concerning the methods and technologies to meet these new environmental requirements. In addition to the developments in the plant construction and flue gas cleaners, the control of the boiler operating conditions through combustion optimization is an important and cost-effective way to affect NOx emissions (Xu et al, 2006; Liang et al, 2006; Gao et al, 2011). The conflict between low NOx emission and high boiler thermal efficiency encounters, the operation parameters suited to lower NOx emissions of coal-fired boiler always lead to a higher carbon content in fly ash and lower efficiency of the boiler, the above studies on optimization of coalfired boiler combustion mainly focus on the singleobjective optimization, for only on the emissions of NOx from the boilers or only boiler efficiency alone and can’t reach both lower NOx emissions and higher efficiency of boiler, it is imperative to find a good tool for multi-objective optimization of coal-fired boiler combustion

Objectives

Results

Conclusion