A novel process for preparing Fe-Cr-Ni-C alloy: synergetic reduction of stainless steel dust and laterite nickel ore.

In order to improve the recovery ratio of valuable metals in stainless steel dust, reduce environmental pollution, and promote solid waste resource recovery and sustainable development of industry, the synergistic reduction process for preparing Fe-Cr-Ni-C alloy was studied in detail by changing the addition of laterite nickel ore and reduction process conditions. The results show that with the addition of laterite nickel ore, the basicity of raw materials is reduced, the precipitation and aggregation of metal particles are promoted, the separation effect of metals and slags from reduction products is improved, and the metal recovery ratio also improved in the synergistic reduction process. When the ratio of stainless steel dust to laterite nickel ore is 94%:6%, reduction temperature is 1400 °C, reduction time is 20 min, and FC/O is 0.8, the metals and slags of the reduction product can be separated naturally after cooling; the recoveries of Fe, Cr, and Ni are 90.6%, 90.1%, and 91.2%, respectively. The grades of Fe, Cr, and Ni in the Fe-Cr-Ni-C alloy are 62.7%, 18.9%, and 4.1%, respectively. The content of harmful elements S and P in the alloy is low, so it can be directly used as raw material for stainless steel smelting.

Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN.

The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production.

Influence of Temperature and CO2 on High-Temperature Behavior and Microstructure of Metallurgical Coke.

Metallurgical coke is an important raw material for blast furnaces. Specifically, temperature and CO2 significantly affect its metallurgical behavior. In this study, the influence of temperature and CO2 on the high-temperature behavior of three metallurgical coke samples, used in blast furnaces of different volumes, was investigated. The carbon structure and pore structure of the coke samples were analyzed. The results indicated that as the temperature increased from 1100 to 1500 °C, the weight loss ratio increased 10-fold and the drum strength decreased to approximately 80% in Ar. Under a CO2 atmosphere, as the temperature increased from 1100 to 1300 °C, the reactivity index increased from 20 to 70%, and the strength after reaction exhibited the lowest value of 40% at 1250 °C. When the temperature increased from 1100 to 1500 °C, the stacking height of the layer structure Lc of the coke samples increased to ∼5.5 nm. Under the influence of CO2 and temperature, the Lc of the coke samples increased to approximately 4 nm between 1100 and 1300 °C. Furthermore, CO2 slightly affected the carbon structure. The changes in pores under the influence of CO2 and temperature were greater than those under the influence of temperature between 1100 and 1300 °C. Typically, the strength of coke is high when the pore number, roundness, and porosity are low. The strength and microstructure parameters of the coke samples were correlated via multiple regression. The results of the multiple regression showed that the carbon structure and pore number had the highest impact on coke strength, followed by roundness and porosity.

Green and efficient utilization of stainless steel dust by direct reduction and self-pulverization.

Stainless steel dust is a solid waste and contains a large number of valuable Fe, Cr, and Ni metal oxides, which should be recovered efficiently. Through direct reduction and self-pulverization separation, the goals of high metal recovery ratios and grades in the new process of comprehensive stainless steel dust utilization were achieved. Combined with theoretical analysis and experimental research, the effects of different conditions (FC/O ratio, reduction temperature, reduction time) on the reduction process and self-pulverization of reduction products were studied. The results showed that the optimal FC/O ratio was 0.8, reduction temperature was 1450 °C and reduction time was 20 min for the metal oxides in stainless steel dust to be completely reduced by carbon-thermal reduction; the self-pulverization holding temperature was 1100 °C, the holding time was 15 min, the conversion ratio of Ca3SiO5 to Ca2SiO4 reached the maximum, the content of γ-Ca2SiO4 in the reduced slag after cooling was increased, and a higher degree of self-pulverization was achieved. Fe-Cr-Ni-C alloy with an iron grade of 66.82%, chromium grade of 20.02% and nickel grade of 4.12% was manufactured successfully from stainless steel dust. The recoveries of iron, chromium and nickel in the stainless steel dust were 92.50%, 92.02% and 93.74%, respectively.