O_(3)oxidation combined with semi-dry method for simultaneous desulfurization and denitrification of sintering/pelletizing flue gas

With the vigorous development of China's iron and steel industry and the introduction of ultra-low emission policies,the emission of pollutants such as SO_(2)and NO x has received unprecedented attention.Considering the increase of the proportion of semi-dry desulfurization technology in the desulfurization process,several semi-dry desulphurization technologies such as flue gas circulating fluidized bed(CFB),dense flow absorber(DFA)and spray drying absorption(SDA)are briefly summarized.Moreover,a method for simultaneous treatment of SO_(2)and NOx in sintering/pelletizing flue gas by O_(3)oxidation combined with semidry method is introduced.Meantime,the effects of key parameters such as O_(3)/NO molar ratio,Ca SO_(3),SO_(2),reaction temperature,Ca/(S+2 N)molar ratio,droplet size and approach to adiabatic saturation temperature(AAST)on denitrification and desulfurization are analyzed.Furthermore,the reaction mechanism of denitrification and desulfurization is further elucidated.Finally,the advantages and development prospects of the new technology are proposed.

GLCM Based Extraction of Flame Image Texture Features and KPCA-GLVQ Recognition Method for Rotary Kiln Combustion Working Conditions

According to the pulverized coal combustion flame image texture features of the rotary-kiln oxide pellets sintering process,a combustion working condition recognition method based on the generalized learning vector(GLVQ) neural network is proposed.Firstly,the numerical flame image is analyzed to extract texture features,such as energy,entropy and inertia,based on grey-level co-occurrence matrix(GLCM) to provide qualitative information on the changes in the visual appearance of the flame.Then the kernel principal component analysis(KPCA) method is adopted to deduct the input vector with high dimensionality so as to reduce the GLVQ target dimension and network scale greatly.Finally,the GLVQ neural network is trained by using the normalized texture feature data.The test results show that the proposed KPCA-GLVQ classifer has an excellent performance on training speed and correct recognition rate,and it meets the requirement for real-time combustion working condition recognition for the rotary kiln process.