Combustion performance of pulverized coal(PC)in blast furnace(BF)process is regarded as a criteria parameter to assess the prop-er injection dosage of PC.In this paper,effects of two kinds of additives,Fe_(2)O_(3) and...Combustion performance of pulverized coal(PC)in blast furnace(BF)process is regarded as a criteria parameter to assess the prop-er injection dosage of PC.In this paper,effects of two kinds of additives,Fe_(2)O_(3) and CaO,on PC combustion were studied using the thermo-gravimetric method.The results demonstrate that both the Fe_(2)O_(3) and CaO can promote combustion performance index of PC including igni-tion index(C_(i)),burnout index(D_(b)),as well as comprehensive combustibility index(S_(n)).The S_(n) increases from 1.37×10^(−6) to 2.16×10^(−6)%2·min^(−2)·℃^(−3) as the Fe_(2)O_(3) proportion increases from 0 to 5.0wt%.Additionally,the combustion kinetics of PC was clarified using the Coats-Redfern method.The results show that the activation energy(E)of PC combustion decreases after adding the above additives.For instance,the E decreases from 56.54 to 35.75 kJ/mol when the Fe_(2)O_(3) proportion increases from 0 to 5.0wt%,which supports the improved combustion per-formance.Moreover,it is uneconomic to utilize pure Fe_(2)O_(3) and CaO in production.Based on economy analysis,we selected the iron-bearing dust(IBD)which contains much Fe_(2)O_(3) and CaO component to investigate,and got the same effects.Therefore,the IBD is a potential option for catalytic PC combustion in BF process.展开更多
A thermal power plant of Sinopec has 9 boilers, which generally have problems of high exhaust gas temperature and high flying ash carbon content. In order to improve the adaptability of coals, the stability of coal po...A thermal power plant of Sinopec has 9 boilers, which generally have problems of high exhaust gas temperature and high flying ash carbon content. In order to improve the adaptability of coals, the stability of coal powder ignition, the burn-off rate of pulverized coals and the boiler efficiency, a series of renovation projects about importing hot air into mill exhauster are proposed. For the sake of verifying the renovation effects, an efficiency performance test is conducted on the renovated #5 boiler. The test result shows that the boiler heat efficiency has improved by 0.4% and it operates more safely and reliably after the renovation. At last, this paper recommends an optimized operation mode.展开更多
An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image s...An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52074086,51974073,52074072,52074074)the Fundamental Research Funds for the Central Universities,China(No.N2225039)the Liaoning Provincial Natural Science Foundation of China(No.2019-MS-132)。
文摘Combustion performance of pulverized coal(PC)in blast furnace(BF)process is regarded as a criteria parameter to assess the prop-er injection dosage of PC.In this paper,effects of two kinds of additives,Fe_(2)O_(3) and CaO,on PC combustion were studied using the thermo-gravimetric method.The results demonstrate that both the Fe_(2)O_(3) and CaO can promote combustion performance index of PC including igni-tion index(C_(i)),burnout index(D_(b)),as well as comprehensive combustibility index(S_(n)).The S_(n) increases from 1.37×10^(−6) to 2.16×10^(−6)%2·min^(−2)·℃^(−3) as the Fe_(2)O_(3) proportion increases from 0 to 5.0wt%.Additionally,the combustion kinetics of PC was clarified using the Coats-Redfern method.The results show that the activation energy(E)of PC combustion decreases after adding the above additives.For instance,the E decreases from 56.54 to 35.75 kJ/mol when the Fe_(2)O_(3) proportion increases from 0 to 5.0wt%,which supports the improved combustion per-formance.Moreover,it is uneconomic to utilize pure Fe_(2)O_(3) and CaO in production.Based on economy analysis,we selected the iron-bearing dust(IBD)which contains much Fe_(2)O_(3) and CaO component to investigate,and got the same effects.Therefore,the IBD is a potential option for catalytic PC combustion in BF process.
文摘A thermal power plant of Sinopec has 9 boilers, which generally have problems of high exhaust gas temperature and high flying ash carbon content. In order to improve the adaptability of coals, the stability of coal powder ignition, the burn-off rate of pulverized coals and the boiler efficiency, a series of renovation projects about importing hot air into mill exhauster are proposed. For the sake of verifying the renovation effects, an efficiency performance test is conducted on the renovated #5 boiler. The test result shows that the boiler heat efficiency has improved by 0.4% and it operates more safely and reliably after the renovation. At last, this paper recommends an optimized operation mode.
基金Supported by the Research and Development Project of Experimental Technology,China University of Mining and Technology(Study on mineral occurrence in coal based on SEM and EDS,S2023Y018)the National Natural Science Foundations of China under Grant 62371451.
文摘An important index to evaluate the process efficiency of coal preparation is the mineral liberation degree of pulverized coal,which is greatly influenced by the particle size and shape distribution acquired by image segmentation.However,the agglomeration effect of fine powders and the edge effect of granular images caused by scanning electron microscopy greatly affect the precision of particle image segmentation.In this study,we propose a novel image segmentation method derived from mask regional convolutional neural network based on deep learning for recognizing fine coal powders.Firstly,an atrous convolution is introduced into our network to learn the image feature of multi-sized powders,which can reduce the missing segmentation of small-sized agglomerated particles.Then,a new mask loss function combing focal loss and dice coefficient is used to overcome the false segmentation caused by the edge effect.The final comparative experimental results show that our method achieves the best results of 94.43%and 91.44%on AP50 and AP75 respectively among the comparison algorithms.In addition,in order to provide an effective method for particle size analysis of coal particles,we study the particle size distribution of coal powders based on the proposed image segmentation method and obtain a good curve relationship between cumulative mass fraction and particle size.
