This paper proposed a near-infrared (NIR) spectra quantitative analysis method for flue gas of thermal power plant based on wavelength selection. For the proposed method, the self-adaptive accelerated particle swarm optimization is presented for determining the most representative wavelengths of NIR spectral signals and is combined with partial least square for predicting the various contents of the real flue gas dataset. The proposed method chooses the current own optimal or the current global optimal as the reference state randomly and accelerated updates of the flight velocity by the reference state, then the particle state is updated based on the new velocity self-adaptively. The experimental results of a real flue gas dataset verified that the proposed method has higher predictive ability and could overcome the premature convergence.
Key words: Thermal power plant, fuel gas, near-infrared spectrum, wavelength selection, self-adaptive accelerated discrete particle swarm optimization.
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