Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes

Han, Yang; Cui, Zeqian; Wang, Lijing; Li, Jie; Yang, Aimin; Zhang, Yuzhu

doi:10.1007/s42243-022-00906-0

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…At this point, the sample is influenced by both the ordering and gasification reactions. During this process, CO 2 molecules collide with the sample, leading to defects in the growth process of the six-membered ring network plane and the stacking process of microcrystallines, resulting in the formation of microcrystalline fragments 31) . Consequently, the V peak shifts upwards during this stage.…”

Section: Cokementioning

confidence: 99%

Identification of Carbonaceous Materials in Blast Furnace Dust by Micro-Raman Spectroscopy

Zou,

Gao,

Liu

et al. 2024

ISIJ Int.

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Carbonaceous materials (CM) in blast furnace dust (BFD) contain abundant information about evolution behavior and utilization efficiency of metallurgical coke and injected pulverized coal.A novel method for determination of origin and content of BFD by micro-Raman spectroscopy was proposed in this paper. Ten randomly areas was selected and each one covers an area of 120×120μm in the surface of briquetting demineralization-BFD sample. Average area was multi-point scanning measured by micro-Raman spectrometer for 140 test points with the same step length. Then, contrasted CM including evolutive coke, coal char and precipitated carbon from deposition of carbon process were prepared in laboratory simulated the process of fuels under gradually heating up condition of blast furnaces (BFs) and tested by micro-Raman spectroscopy. Meanwhile, the raceway coke was also sampled and tested for compared with coke at high temperature. Typical feature of spectrogram of these CMs can be well correspond to the BFD results. Therefore, CMs in BFD could be attributable to different sources, and its content can be counted by statistics of spectrogram. By comparing the identification results of petrography method, this method can measure the small particle size such as soot derived from very fine pulverized coal and volatiles which is hard to measure by the latter. For the investigated BF, unconsumed char escape from raceway and powdery coke in stack contributed the main source of CMs in BFD, while content of overflowing coke powder from high-temperature region of BF and precipitated carbon by deposition carbon reaction is very low.

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Section: Cokementioning

confidence: 99%

Identification of Carbonaceous Materials in Blast Furnace Dust by Micro-Raman Spectroscopy

Zou,

Gao,

Liu

et al. 2024

ISIJ Int.

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“…In recent years, there have been relatively few studies on the prediction of vanadium content of molten iron [10][11][12]. With the development of big data technology in the metallurgical industry, prediction models of silicon content, which is also an important index of molten iron quality, have emerged and guided the actual production operation of blast furnaces [13][14][15]. Combined with the challenges of blast furnaces in the field of traditional vanadium extraction, the application of big data mining technology provides a new direction for the research of vanadium extraction in vanadium-titanium blast furnaces: to establish a prediction model to accurately predict the vanadium content of molten iron and grasp the trend in vanadium content, thus providing a technical basis for the subsequent optimization of blast furnaces for highly efficient and stable vanadium extraction operation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Vanadium Content of Molten Iron in a Blast Furnace and the Optimization of Vanadium Extraction

Li,

Liu

et al. 2023

Separations

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The vanadium content of molten iron is an important economic indicator for a vanadium–titanium magnetite smelting blast furnace, and it is of great importance in blast furnace production to be able to accurately predict it and optimize the operation of vanadium extraction. Based on the historical data of a commercial blast furnace, the clean data were obtained by processing the missing data and outlier data for data mining analysis and model development. A combined wavelet-TCN model was used to predict the vanadium content of molten iron. The average Hurst index after wavelet transform was calculated to reduce the complexity of the wavelet transform layer selection and the model computation time. The results show that compared to single models, such as LSTM, LSTM with attention, and TCN, the combined model based on wavelet-TCN (a = 5) had an improvement of about 11~17% in R2, and the prediction accuracy was high and stable, which met the practical requirements of blast furnace production. The factors affecting the vanadium content of molten iron were analyzed, and the measures to increase the vanadium content were summarized. A blast furnace should avoid increasing the titanium dioxide load, increase the vanadium load appropriately, and keep the relevant operating parameters within the appropriate range in order to achieve the optimization of vanadium extraction from molten iron.

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Viscosity and Structure Studies of Iron-Based Quaternary Melts: The Effect of Silicon

Fan,

Gao,

Zhang

2024

Metall Mater Trans B

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Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes

Cited by 4 publications

References 14 publications

Identification of Carbonaceous Materials in Blast Furnace Dust by Micro-Raman Spectroscopy

Identification of Carbonaceous Materials in Blast Furnace Dust by Micro-Raman Spectroscopy

Prediction of the Vanadium Content of Molten Iron in a Blast Furnace and the Optimization of Vanadium Extraction

Viscosity and Structure Studies of Iron-Based Quaternary Melts: The Effect of Silicon

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