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2015
DOI: 10.1016/j.compchemeng.2015.04.035
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Predictive models and operation guidance system for iron ore pellet induration in traveling grate–rotary kiln process

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Cited by 24 publications
(9 citation statements)
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“…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.…”
Section: Effect Of Roasting Temperaturementioning
confidence: 99%
“…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.…”
Section: Effect Of Roasting Temperaturementioning
confidence: 99%
“…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 .…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%