Search results

Study on the Lining Erosion of Deep Reduction Electric Arc Furnace in Smelting Metallized Pellets Process Produced by Vanadium Titanium Magnetite Qi Jianling1, a 1 PanGang Group Research Institute Co., Ltd. , State Key Laboratory of Vanadium and Titanium Resources Comprehensive Utilization, Panzhihua 617000, Sichuan, china ) aqijianchou123@126.com Key words：deep reduction electric arc furnace；refractory; erosion Abstract.The erosion of the lining refractory of the deep reduction electric arc furnace is introduced, and the erosion appears in the process of smelting metallized pelltes produced by vanadium titanium magnetite.
Introduction A pilot plant was constructed in PanGang in 2009 which applied Rotary Hearth Furnace (RHF) and deep reduction Electric Arc Furnace (EAF) to deal with vanadium titanium magnetite.
Fig.1 Analysis of lining erosion mechanism Arc erosion of the deep reduction electric arc furnace (EAF) The electrode diameter of the EAF is 2200mm and the distance between outer edge of electrode and the lining is only 550mm.
However, the charges of the deep reduction EAF are the metalized pellets, and in order to improve the TiO2 content in the slag, it does not adjust the basic of the slag and the value of CaO/SiO2 is just around 0.2～0.3.
Handbook of chart data about steelmaking[M].

The soft-reduction segments with remote roll gap adjustment functions (the key devices for implementation of soft-reduction action).
The servo hydraulic control system with high control accuracy for soft-reduction (the hydraulic drive system for implementing action of soft-reduction).
The soft-reduction basic automatic control system with high efficiency in the implementation (The electrical control system for implementing action of soft-reduction).
The L2 model database has been debugged, we have confirmed the database structure and the variable set, we have also defined the relationship of data communications between L1 basic automation program and L2 process control model and improved the database functions to test and verify the normalcy and stability of L2 model database.
As Fig.1 shows, it is the macroscopic comparison of the JB45 and JB50 steel grade produced without soft-reduction and with soft-reduction, and the details lie in Table1.

To optimize the reduction technology of SSI and its removal ability for nitrate from wastewater, the influence of reduction time of SSI on nitrate removal percentage was investigated.
Because the specific surface area was impact smartly by the reduction time of SSI, it was suggested that nitrate removal ability of SSI should declined with the increasing of reduction time once the reduction time longer than the optimized reduction time.
But the higher specific area was also impacted by deoxidizer, reduction temperature and reduction time, etc.
lnr=ln(-dC/dt)=lnkobs+nlnC (5) Fig.2 The relationship between lnr and lnC According to the kinetic experiment datum shown in Fig. 1, reaction rate equations of nitrate removal by SSI reduced in different reduction time could be calculated and illustrated in Table 1.
SSI reduction could not be optimized merely on the basement of its removal ability for nitrate, other factors should be taken into account, such as reduction atmosphere, temperature, reduction time, etc.

The thickness reduction was measured by optical 3D scanning method and the influence of the feed, workpiece geometry and planar anisotropy of the blank on the wall thickness reduction was studied.
Based on the results it is determined that the highest reduction of wall thickness is observed in the conical part of the experimental sample.
For the experimental measurement of shape accuracy a non-contact data capture method was used.
Control factors and levels Parameter Sign Level 1 Level 2 Level 3 Feed ratio (mm/rev) Workpiece geometry (-) f pm 1 (1) radius R10 1,5 (2) conical area 2 (3) cylindrical area Rolling direction of the sheet (deg) rd 0 45 90 Analysis of data Using Minitab 16 software, ANOVA (Analysis of Variance) was performed to determine which parameter and two-way interactions significantly affect the performance characteristics.
Analysis of variance (S/N data) Source Sum of squares DoF Mean square p-value F- ratio f 0.066052 2 0.033026 0.000 236.21* pm 0.525874 2 0.262937 0.000 1880.61* rd 0.001030 2 0.000515 0.074 3.68 f*rd 0.001170 4 0.000293 0.174 2.09 f*pm 0.042593 4 0.010648 0.000 76.16* pm*rd 0.001015 4 0.000254 0.219 1.81 Other/Error 0.001119 8 0.000140 Total 0.638852 26 Tabulated F-ratio at 95% confidence level: F (0.05; 2.8) = 4.46; F (0.05; 4.8) = 3.84 * significant parameter or significant two-way interaction Fig. 3 Relation between response (reduction ratio t0/t1) and variables (feed ratio, place of measure) Conclusions Based on the experimental investigation and data analysis using ANOVA has demonstrated that the workpiece shape is very significant factor which intensively influence the wall thickness reduction of spun part.

The quantitative data of phases are summarized in table 1.
The quantitative data of phases by Mössbauer spectroscopy.
The quantitative data of phases, 1:12 average hyperfine fields for NdFe11Ti, NdFe10.5Mo1.5 and NdFe10.75Mo1.25 as-prepared.
The processing parameters, quantitative data of phases and magnetic properties of samples are summarized in table 4.
(see TC data in tables 4 and 5).

The calculated roughness is close to the measured data.
The results from the FEM simulation will be compared with the experimental data that used the same rolling and profile parameters.
Fig. 8 shows a comparison of the simulated surface roughness with the experimental data.
It can be seen that the calculated results are Oxide scale Steel close to the experimental data.
The results of the simulation are compared with the experimental data to verify the effectiveness of the developed model for calculating the surface roughness transformation.

The rate of reduction scales with the reaction time for both RT and 50 ℃ as evidenced from electrical conductivity data.
Therefore, the metals such as Fe, Al and Zn with dilute hydrochloric acid have been used for the reduction of GO because of their high reduction potential values.
Tin powders (100 mesh, 99.5%, Alfa Aesar, USA) were used as reducing agent for GO reduction.
However, this peak becomes broader with increasing the duration GO reduction at 50 °C.
FT-IR and XPS analysis confirms the successful reduction of GO using Tin powder.

They can not yield a simple yet effective mapping function for the new coming test data sets.
Classification performance on Iris data set.
The following two real-world data sets are all from the UCI machine learning repository [11].
Influence of data normalization mode.
The proposed MML has nothing to do with specific data.

Reduction of forming stage is the most effective approach for reduction of manufacturing cost and time of such multi-stage forming parts.
However, if there is not enough data in advance, the system can not provide the optimum process design.
Stage reduction by using numerical optimization Fig.1 shows the idea of stage reduction to obtain the optimum multi-stage process design.
   (7) Result and discussion Optimization and stage reduction.
Here the further stage reduction from 2-stage to 1-stage is discussed.

J.W.Park et al [16] who used coal-based direct reduction process to research the direct reduction of iron in the hot mill sludge.
The carbon (50.47%) was the main active reagent in the direct reduction process.
Effect of reduction time on the iron recovery of lead slag was shown in Fig. 5.
Fig. 6 Effect of reducing agent ratio on iron recovery The recovery of iron in lead slag is mainly achieved by reduction reaction, which includes direct reduction and indirect reduction with coal as reductant.
Fig. 7 Effect of CaO ratio on iron recovery From data shown in Fig. 3, the values of Eq. (4) and Eq. (5) are always less than that of Eq. (1)and Eq. (2), indicating the increasing possibility of reduction Fe2SiO4 and Fe2AlO4 to metallic iron after adding CaO into the mixture.