Numerical analysis of the carbon‐containing pellet direct reduction process with central heat transfer enhancement

Li, Nan; Wang, Feng

doi:10.1002/htj.21913

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…The degree of metallization is of primary importance and has been studied extensively by experiments. The experimental results obtained by Liu 23) was used to validate the physical pellet model and a reasonable correlation between calculated and experimental data has been obtained, 24) as shown in Fig. 2.…”

Section: Results and Validationmentioning

confidence: 72%

“…It is believed that the solid-solid direct reduction reaction of metal oxides and carbon particles only plays a role in initiating the reduction, and the reduction of pellet mainly depends on the intermediate products of CO and CO 2 . The direct reduction reactions of carbon-containing pellet are as follows: 23)…”

Section: Reaction Of the Direct Reduction Process Of Carbon-containing Pelletmentioning

confidence: 99%

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Prediction Model of Carbon-containing Pellet Reduction Metallization Ratio Using Neural Network and Genetic Algorithm

Zhang

Wang

2021

ISIJ Int.

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In this study, we established mathematical model of the carbon-containing pellet reduction process and used the neural network model to speed up the prediction process for actual production in the rotary hearth furnace (RHF). In order to obtain enough data to make a neural network, we calculated some results under different conditions by the pellet reduction mathematical model. Then, we developed and trained a feed-forward back-propagation neural network model using MATLAB software. The input parameters of the model included the temperature in the furnace, the reduction time, size and C/O ratio of the carboncontaining pellet and the output parameter was the final degree of metallization of the carbon-containing pellet. Beside, we optimized initial weights and thresholds of the model utilizing genetic algorithm, and also compared and analyzed the number of hidden layer neurons, training algorithm, learning rate, and population size of it. Finally, we chose 4-10-1 as the modeling structure of the neural network, the Levenberg-Marquardt training algorithm, the learning rate of 0.1 and population size of 150 as the optimal configuration. The coefficient correlation of training set and test set data calculated by the model indicates that the established neural network model has a high degree of suitability. Therefore, the neural network model combined with genetic algorithm has superiority as a reliable and efficient tool for predicting the reduction metallization rate of carbon-containing pellet in the RHF.

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Section: Results and Validationmentioning

confidence: 72%

Section: Reaction Of the Direct Reduction Process Of Carbon-containing Pelletmentioning

confidence: 99%

Prediction Model of Carbon-containing Pellet Reduction Metallization Ratio Using Neural Network and Genetic Algorithm

Zhang

Wang

2021

ISIJ Int.

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Influence of furnace temperature and non-uniform heat flux density on direct reduction process of newly designed carbon containing pellet

Wang

Pan

2022

J. Cent. South Univ.

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Numerical analysis of the carbon containing pellets direct reduction process

Wang

Zhang

2021

Metall. Res. Technol.

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In view of the carbon-containing composite pellets direct reduction process in rotary hearth furnace, a mathematical model coupling heterogeneous chemical reaction kinetics, heat and mass transfer of this process was established. The effects of furnace temperature (from 1273.15 K to 1673.15 K) and pellet radius (from 6 mm to 16 mm) on the direct reduction of carbon-containing composite pellets were studied by adopting computational fluid dynamics software. The pellet temperature and composition changes under different operating conditions were analyzed. CO and CO2 fluxes, heat fluxes on the pellet surface were especially studied. Total heat absorption by the pellet, CO and CO2 overflow from the pellet surface together with pellet degree of metallization (DOM) and zinc removal rate (ZRR) were calculated. Results show that with the increasing of furnace temperature or the decreasing of the pellet radius, the temperature difference between pellet surface and its center and the final DOM, ZRR increased. The larger the pellet radius, the smaller the heat absorption, also the smaller CO and CO2 overflow. But heat absorption and CO overflow per unit volume are higher. There is an optimal pellet radius with high CO utilization efficiency. Pellet porosity decreases at first and then increases with reducing time. It is also found that effective thermal conductivity is a major factor limiting the pellets temperature increasing. The reduction sequence of the pellets is Fe2O3→Fe3O4→FeO→Fe.

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Numerical analysis of the carbon‐containing pellet direct reduction process with central heat transfer enhancement

Cited by 3 publications

References 20 publications

Prediction Model of Carbon-containing Pellet Reduction Metallization Ratio Using Neural Network and Genetic Algorithm

Prediction Model of Carbon-containing Pellet Reduction Metallization Ratio Using Neural Network and Genetic Algorithm

Influence of furnace temperature and non-uniform heat flux density on direct reduction process of newly designed carbon containing pellet

Numerical analysis of the carbon containing pellets direct reduction process

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