Analysis and Control Optimization of Furnace Pressure Based On Wavelet Transform

This work investigates a multi-scale furnace pressure signal correlation analysis and control optimization approach utilizing wavelet transform. The furnace pressure and primary air volume signals obtained from the entrance of each coal mill are decomposed using wavelet transformation to extract components across various frequency ranges. Subsequently, the signal components within each frequency band are reconstructed. The correlation coefficient between signals at identical frequencies is computed to quantify the degree of correlation at this scale, leveraging the similarity in their fluctuations. By employing multi-scale analysis techniques, the correlation between various signals across different scales is investigated. Subsequently, control and optimization are implemented for the inlet primary air volume exhibiting the strongest correlation with the cold and hot air doors of the coal mill. The experimental results indicate that the optimized control system can significantly eliminate abnormal fluctuations in furnace pressure.