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.

Pellet heat transfer has always been one of the biggest limiting factors in its direct reduction process in the rotary hearth furnace (RHF). Therefore, strengthening its heat transfer process is very important for productivity in RHF. In this study, a coupling model How to cite this article: Li N, Wang F. Numerical analysis of the carbon-containing pellet direct reduction process with central heat transfer enhancement.

In this paper, molecular dynamics (MD) simulation was used to study the wettability of lithium and tungsten. The surface energy barrier and evaporation control the static contact angle with increasing temperature. The effects of 4 different sizes of droplets and 10 different tungsten sections were evaluated. Moreover, it was found that the different arrangements of atoms on the solid surface will affect the wettability, but the size of the droplet has little effect. In addition, the situation of the droplets driven by six different external forces was evaluated. When the force increases, the two states of the droplet and stream will have different properties. Finally, we studied the phase behavior between lithium and tungsten. For example, lithium overflows from the tungsten plate. The tungsten phase is separated in the lithium plate. Lithium is faster than tungsten when it aggregates in the gas phase, and wettability will drive the effects of engulfing and spitting.

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