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Fig.1 Flow chart of ferrite rolling test Fig.2 Temperature distribution photo at 800°C Fig.3 shows the temperature distribution data along both rolling and transverse directions of the strip.
(a) Rolling direction (RD) (b) Transverse direction (TD) Fig.3 Temperature distribution data of strip before rolling at 800°C Results and discussions Rolling Parameters.
Fig.4 shows the reduction effect on the rolling force in the ferrite temperature range.
It can be seen that reduction has an obvious effect on the rolling force.
A higher reduction always achieved higher rolling force.

WARDM Algorithm The WARDM model proposed in this paper is divided into three phases: data input stage, data mining stage and data output stage.
At the same, with the reduction of the wminsup, the advantage is more obvious.
The WARDM algorithm has good stability and is more applicable to data mining in large-scale data sets.
Management of Data, 1993: 207-216P
Neurorule: a connectionist approach to data mining.

Data were analyzed by Kruskal-Wallis and Mann-Whitney U test at p<0.05.
Evaluation of biofilm formation was assessed through the XTT reduction assay at 492 nm [12, 14].
By using XTT reduction assay, the amount of biofilm formation are expressed as the optical density (Table 1).
But, within these AgNPs groups, the reduction was not dose dependent.
They also showed the reduction of flexural strength of PMMA with the increase of AgNPs in heat polymerized PMMA resin.

Cavity volume of ring after compression to different reductions.
All rings expands when m < 0.09 irrespective of the reduction.
The white contact area is determined by binary data analysis of the micrographs observed in a microscope in five different radial positions as shown in Fig.7.
Fig. 8a shows the change in m with increasing reduction.
The friction coefficients m are constant at a reduction less than about 20%, while m decreases at a reduction of 30%.

The most useful function of data mining technology is extracting valuable information and knowledge from irregular data.
Data mining technology [2] is a process to extract unknown before while valuable information and knowledge from those seemingly fragmented and irregular data.
As an analytical tool, Data mining technology is usually constituted by multiple steps.
By means of information management technology and database technology to achieve data integration and logistics management, thus data mining technology provides basis for intelligent logistics decision-making system.
At present, the application of genetic algorithms is a relatively mature technology of data mining techniques.

The simulated experiment shows the modeling data is fit to the field data.
And even through Gleeble press and other equipment, it is not easy to attain accurate data.
To get the resistance of deformation model using neural network technology from the field data is an attempt to solve this dilemma.
In the same time, is calculated with Eq.(9) based on the data in table 1.
A model is shown in Figure 2: Fig.2:The deformation resistance of Q235 between 1310～1360K with rolling speed 52.5m/s and reduction 35mm Compared the data in Fig.2 with available data [1], the difference is less than10 %.

For percentage thickness reduction of 59.6%, minimum roughness 2.09μm, and maximum hardness 41.7 BHN, the confirmatory test showed values of 64.78 % thickness reduction, roughness of 2.14μm and hardness of 44.82 BHN that were in agreement with the predicted value.
Experiment parameters % Thickness reduction Roughness, Ra Hardness, BHN A B C D E 1 0.46 6.0 0.50 2000 2300 47.10 3.59 42.77 2 0.46 6.0 0.50 2000 2500 53.62 3.68 44.35 3 0.46 6.0 0.50 2000 2700 54.35 3.79 43.15 4 0.46 8.0 0.75 2500 2300 55.80 3.20 41.97 5 0.46 8.0 0.75 2500 2500 50.72 3.22 41.84 6 0.46 8.0 0.75 2500 2700 56.52 2.93 40.10 7 0.46 10.0 1.00 3000 2300 55.07 2.74 30.36 All values for the experiment varied between following ranges: 8-27 0.56-0.78 6-10 0.5-1 2000-3000 2000-2700 47.1-64.29 2.62- 7.65 32.84- 43.54 *Only first seven sample data shown in above table.
Besides, it provides an efficient solution to the uncertainty, multi-input and discrete data problem.
No Experiment parameters GRC GRG A B C D E % thickness reduction Roughness Hardness 1 0.46 6.0 0.50 2000 2300 0.4488 0.7001 0.8712 0.6734 2 0.46 6.0 0.50 2000 2500 0.5675 0.6881 1.00 0.7519 3 0.46 6.0 0.50 2000 2700 0.5847 0.6727 0.3463 0.5346 4 0.46 8.0 0.75 2500 2300 0.6224 0.7629 0.8179 0.7344 5 0.46 8.0 0.75 2500 2500 0.5078 0.7594 0.8100 0.6924 6 0.46 8.0 0.75 2500 2700 0.6431 0.8109 0.7156 0.7232 7 0.46 10.0 1.00 3000 2300 0.6029 0.3644 0.4333 0.4669 *Only first seven sample data shown in above table.
· Better ' % thickness reduction' is observed with increase in size of forming tool

Also, reduction of molybdenum trioxide by hydrogen or carbon has been reported in the literature [4, 5].
In the present study, carburization-thermal process for reduction of MoO2 is done by using carbon [4, 6].
For identification purposes of crystalline phase composition, diffraction patterns obtained were matched with standard diffraction data (JCPDS) file.
Summary The reduction process of MoO2 to metallic molybdenum can be completed by a two-stage reaction process.
Khodadadi, Synthesis of nanocrystalline molybdenum by hydrogen reduction of mechanically activated MoO3, Int.

The displacement has been studied for three typical kinds of aluminum reduction cells (semi-graphitic, graphitic and graphitized) after 30 days start-up.
Clearly, in our country, the 350 ~ 400KA aluminum reduction cell has gradually become the mainstream, and the cell deformation increases with the higher amperage [4].
Researching the relation between the graphitization degree of the cathode materials and the cell deformation on the basis of the thermo-electric field analysis, this paper could provide the valuable data for the cell technical improvement and the development of the new cell.
Table 1 Properties of cathode materials used in the aluminum reduction cell Material Thermal expansion coefficient (10-6) Elastic modulus (GPa) Density (g/cm3) Thermal conductivity (W•m-1•K-1) Sodium expansion coefficient (10-6) M P M P M P 0.31(1.0%wt) 0.63(2.0%wt） 1.0(3.0%wt) 1.62(5.0%wt) semi-graphitic 2.8 3.5 8 6 1.56 36 30 graphitic 2.8 3.3 8 6 1.59 65 58 graphitized 2.5 3.0 7 5 1.62 110 105 Calculation of thermal field.
The displacement of the 350kA aluminum reduction cells with graphitized cathodes was measured from preheating to 30 days after startup by using mechanical displacement instrument.

Gather data on energy consumption, heat duty, reflux ratios, and operating conditions. 2.
Conduct a final energy analysis using the thermal and operational data from the EIDC system. 2.
The data collection and analysis involve four key steps: 1.
Feed data included temperature, pressure, composition, and flow rate, which are essential for simulation.
The software simulation provided the process flow diagrams and energy consumption data for the direct sequence. 3.