“…In the roasting process, a stable rotary kiln temperature is usually required to ensure smooth production [44]. However, for pelletizing plants with unstable ore sources, the composition of iron concentrate changes frequently, forcing operators to adjust the temperature of the rotary kiln to improve the performance of roasting pellets [57]. Importantly, the melting point of the deposit-forming silicate is close to the roasting temperature range of iron ore pellets.…”
The deposit-forming problem is one of the main bottlenecks restricting the yield and production benefit of iron ore pellets produced by coal-fired rotary kilns. In order to implement measures to ensure the efficient production of pellets by coal-fired rotary kilns, the mechanism and influencing factors on the deposit formation were reviewed. The pellet powder and coal ash come together to form the material base of the deposit. Meanwhile, the local reducing atmosphere caused by the continued combustion of residual carbon increases the FeO content, resulting in the formation of low-melting-point silicates. Moreover, alkali metal elements in coal ash can also promote liquid phase formation to cause serious deposit aggregation problems. During high-temperature roasting, the liquid phase corrodes the surface of the refractory brick to form the initial deposit, whereafter, it binds the pellet powder and coal ash from the material layer, which causes the deposit to accumulate continuously. The deposit formation of coal-fired rotary kilns is the result of interaction between many factors, which includes the quality of the green pellets, the composition of coal ash, the combustion efficiency of pulverized coal, roasting temperature, FeO content and alkali metal input. Finally, it is recommended that some measures to mitigate deposit formation can be adopted, such as increasing the compression strength of preheated pellets, choosing high-quality raw materials with low alkali metals, improving the combustion of pulverized coal.
“…In the roasting process, a stable rotary kiln temperature is usually required to ensure smooth production [44]. However, for pelletizing plants with unstable ore sources, the composition of iron concentrate changes frequently, forcing operators to adjust the temperature of the rotary kiln to improve the performance of roasting pellets [57]. Importantly, the melting point of the deposit-forming silicate is close to the roasting temperature range of iron ore pellets.…”
The deposit-forming problem is one of the main bottlenecks restricting the yield and production benefit of iron ore pellets produced by coal-fired rotary kilns. In order to implement measures to ensure the efficient production of pellets by coal-fired rotary kilns, the mechanism and influencing factors on the deposit formation were reviewed. The pellet powder and coal ash come together to form the material base of the deposit. Meanwhile, the local reducing atmosphere caused by the continued combustion of residual carbon increases the FeO content, resulting in the formation of low-melting-point silicates. Moreover, alkali metal elements in coal ash can also promote liquid phase formation to cause serious deposit aggregation problems. During high-temperature roasting, the liquid phase corrodes the surface of the refractory brick to form the initial deposit, whereafter, it binds the pellet powder and coal ash from the material layer, which causes the deposit to accumulate continuously. The deposit formation of coal-fired rotary kilns is the result of interaction between many factors, which includes the quality of the green pellets, the composition of coal ash, the combustion efficiency of pulverized coal, roasting temperature, FeO content and alkali metal input. Finally, it is recommended that some measures to mitigate deposit formation can be adopted, such as increasing the compression strength of preheated pellets, choosing high-quality raw materials with low alkali metals, improving the combustion of pulverized coal.
“…The three common processes used in iron ore pellet production are the shaft furnace process, the straight grate process, and the grate-kiln process . As the dominant process for iron ore pellet production in China, the proportion of iron ore pellets produced by the grate-kiln process has exceeded 70% . Deposit formation in a rotary kiln is frequently observed in iron ore pellet production by the grate-kiln process, disturbing normal production and decreasing productivity .…”
Deposit formation
in the coal-fired rotary kiln is frequently found
in the production of fluxed iron ore pellets by the grate-kiln process
and affects normal production. In this paper, the effects of pellet
basicity (CaO/SiO2 mass ratio) on the simulated deposit
formation were investigated. The results show that the porosity of
deposits samples increases from 30.8 to 41.5% as the pellet basicity
increases from 0.6 to 1.2, and most of the holes are irregular in
shape. The contents of CaO and Fe2O3 in the
silicates of the deposit samples increased with increasing basicity.
The primary phase of the deposit samples changed from the M2O3 phase region to the clinopyroxene phase region with
a lower melting point. As the basicity increased, the calculated proportions
of the liquid phases in the deposit samples had an increasing trend.
Moreover, the deposit sample adhesion to the refractory brick increases
with the increase in pellet basicity.
“…Similarly, the BF heat mode and hot air volume mode were carried out based on BP neural network at Kawasaki Steel, Japan [13]. However, with progress of ANN technology, two obvious drawbacks of the BP model were found [14]. The first one is that the BP network appears to converge slowly and with difficultly, as the input variables are too many or some of them are actually correlative.…”
The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.
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