Study on Multi-Objective Optimization of High-Efficiency and Low-NO x Emissions of Power Station Boilers Based on Least Squares Support Vector Machines

doi:10.12096/j.2096-4528.pgt.22108

Abstract

Abstract:

Aiming at the multi-objective optimization of boiler combustion system, on the basis of the established prediction model of boiler combustion system, the weighted-particle swarm algorithm and the multi-objective particle swarm optimization (MOPSO) algorithm were used to optimize the adjustable operating parameters of the boiler, which can realize the operating state of the boiler with high efficiency and low NO_x emission. The analysis shows that the operating parameters obtained by the two optimization algorithms are similar, and the trend is consistent with the combustion characteristics analysis and combustion adjustment test results. It indicates that the intelligent algorithm is effective and feasible to optimize the combustion system of the power plant boiler. However, the weighted-particle swarm optimization algorithm has serious subjective dependence. It is difficult to select appropriate weights, and the optimization time is long and the results are few. However, the optimization time of the MOPSO algorithm is far less than the optimization time of the weighted-particle swarm optimization algorithm, the optimization results are more, and the optimization efficiency is higher. Therefore, the MOPSO algorithm is more beneficial to guide the actual operation of the boiler.

Key words: power station boiler, multi-objective optimization, weighted-particle swarm optimization, multi-objective particle swarm optimization (MOPSO)

CLC Number:

TK 227

Zhongrong LIANG, Maowei LAN, Guo ZHENG, Rongqiang HE, Keyang QU, Yunhua GAN. Study on Multi-Objective Optimization of High-Efficiency and Low-NO _x Emissions of Power Station Boilers Based on Least Squares Support Vector Machines[J]. Power Generation Technology, 2023, 44(6): 809-816.

Figures/Tables 10

References 24

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变量	氧量/%	燃尽风风门开度前墙A、B侧/%	燃尽风风门开度后墙A、B侧/%	外二次风风门(1—8)开度/%
下限	2	10	10	10
上限	7	100	100	100

变量	氧量/%	燃尽风风门开度前墙A、B侧/%	燃尽风风门开度后墙A、B侧/%	外二次风风门(1—8)开度/%
下限	2	10	10	10
上限	7	100	100	100

优化算法	优化结果数量	优化用时/s
加权-粒子群	9	345.62
MOPSO	37	58.99

优化算法	优化结果数量	优化用时/s
加权-粒子群	9	345.62
MOPSO	37	58.99

优化算法	优化结果数量	优化用时/s
加权-粒子群	9	260.19
MOPSO	30	72.16