Data-driven modeling for SO2 mass concentration of CFB units under variable load conditions

doi:10.3969/j.issn.2097-0706.2022.03.010

Abstract

Abstract:

Due to the lack of effective guidance for pollutant generation and reduction modelling in the dynamic process of circulating fluidized bed（CFB）units,the load adjusting capacity is restricted by pollutant emission to a certain extent.A data-driven SO₂ concentration dynamic model for CFB units is established based on an extreme learning machine.According to the generation and reduction mechanisms of pollutants from CFBs,appropriate input variables are selected.The model improved by genetic algorithms is of higher accuracy and better modelling results under dynamic conditions.The model can provide effective guidance for SO₂ concentration control systems.At the same time,a parallel system integrating a real system with its virtual counterpart is made on the basis of the proposed model under the framework of intelligent parallel control theory.The proposed intelligent parallel control for the SO₂ control systems can provide references for the SO₂ emission control of the following CFBs for boosting their load adjusting capacities to a certain extent.

Key words: CFB, extreme learning machine, genetic algorithm, SO₂ concentration dynamic model, intelligent parallel control, data driven, intelligent power generation

CLC Number:

TK01

LI Caixia, ZHAO Jun, LI Jianwei, WANG Wei, WANG Jie, YU Haoyang. Data-driven modeling for SO₂ mass concentration of CFB units under variable load conditions[J]. Integrated Intelligent Energy, 2022, 44(3): 63-69.

Figures/Tables 9

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References 14

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变量名称	皮尔森系数	相关性
石灰石给料量	0.732 0	强
总风量	0.212 0	弱
一次风量	0.324 3	弱
二次风量	-0.297 3	弱
氧量	-0.482 7	中
给煤量	0.349 7	弱
负荷	0.482 7	中
床温	0.213 4	弱

参数	数量
ELM隐含层神经元个数	60
遗传算法迭代次数	4 000
种群数量	100

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Data-driven modeling for SO₂ mass concentration of CFB units under variable load conditions

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