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The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947.
In the present investigation, the experimental data were tested with respect to the Langmuir isotherm.
The data obtained from the adsorption experiment conducted during the present investigation was fitted using different COD concentration into the isotherm equation.
The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947 in Fig. 6.
The studied adsorption data fitted well to Langmuir adsorption model with the correlation coefficient 0.9947.

The selective reduction was promoted by selecting the appropriate amount of modifier.
Reduction order was elucidated in this paper, Fe was reduced from the slag followed by P, Mn and Si and the reduction rate of Si could reach about 51%.
The metal phase was rich in Fe, Si, Mn and P as a result of the selective reduction.
Effect of Temperature on Selective Reduction.
The variations in the recovery rate of Si are shown in Fig.4; here the SiO2 30 mass% in the slag was indicated by diamonds, circles represent the data points resulting from the reduction of 40 mass% SiO2 and the triangles denotes the 50 mass% SiO2 in the slag.

The comparison results found good agreement between the numerical model and wind tunnel experimental data with an error of 7.8% in the wind reduction ratio, indicating the present numerical model can be used to undertake study on butterfly and non-planar porous fences.
The rationality and the validity of the numerical method developed in the present work have been evaluated by comparing the numerical results with the experimental data.
The validity of the present numerical method has been evaluated by comparing the numerical results with the experimental data which contain the mean velocity profiles behind the porous fence and the wind reduction ratio.
Fig.5 Comparison of non-dimensional mean velocity profiles at Z/H = 0.8 and 1.2 Fig.5 shows that numerical values are higher than experimental data in the downstream region.
As a result, the numerical predictions show good agreements with the experimental data.

In order to optimize vibration digging shovel’s drag reduction performance parameter, building the regression model of traction resistance between vibration frequency and amplitude, penetrating angle and the traction rate based on the vibration reduction simulation experiment results, moreover, it is optimized.
Domestic and foreign related research on this technology is mostly based on theoretical mechanics modeling and experimental research, simulation study on vibration drag reduction technology is less.
The data were processed by Design-Expert analysis.
Building regression model Application Design-Expert software on traction resistance data (Table 2) are analyzed, getting the code representation of the traction resistance of distribution coefficient Y of 2 regression model: Y=1464.00+19.17X1+30.00X2+6.67X3+10.83X4+10.00X1X2-2.50X1X3-5.00X1X4- 7.50X2X3-7.50X2X4+5.00X3X4+15.50X12+19.25X22+114.25X32+15.50X42 (1) According to the size of regression coefficient of each model factor, the available sequence the influencing factors: the vibration frequency X3, vibration amplitude X2, traction rate X1, penetrations angle X4.
[5] Qiu Lichun: Bulk Subsoiler System Dynamic Model and Study of Drag Reduction and Energy Saving (Ph.D., Shenyang Agricultural Uinversity, China, 1998)

The Model of the Al reduction cell Mathematical Model.
The finite element method (FEM) model of the reduction cell is shown in Fig.1, but the air around the Al reduction cell is not shown.
The element Source 36 is used to provide current data, and need to predefined geometry; the element Solid 117 includes 20 nodes and is used for the static magnetic analysis.
Fig. 1 Schematic diagram of pre-bake anode Al reduction cell Fig. 2 FEM model of Al reduction cell Calculated Results and Analysis.
Fig. 3 X magnetic intensity of the Al of the Al reduction cell (Tesla) Fig. 4 Y magnetic intensity of the Al of the Al reduction cell (Tesla) Fig. 5 Z magnetic intensity of the Al of the Al reduction cell (Tesla) Fig. 6 Sum magnetic intensity vector of the Al of the Al reduction cell (Tesla) Fig. 7 X magnetic intensity of the electrolyte of the Al reduction cell (Tesla) Fig. 8 Y magnetic intensity of the electrolyte of the Al reduction cell (Tesla) Fig. 9 Z magnetic intensity of the electrolyte of the Al reduction cell (Tesla) Fig. 10 Sum magnetic intensity vector of electrolyte of the Al reduction cell (Tesla) Fig. 11 X magnetic intensity of the cell wall of the Al reduction cell (Tesla) Fig. 12 Y magnetic intensity of the cell wall of the Al reduction cell (Tesla) Fig. 13 Z magnetic intensity of the cell wall of the Al reduction cell (Tesla) Fig. 14 Sum magnetic intensity vector of the cell wall of the Al reduction cell (Tesla) Fig.12~Fig.15 are the X, Y, Z and the magnetic

Data Envelopment Analysis Method The Theory of Data Envelopment Analysis Method.
The basic idea of this model is: Form the analysis of the sample input and output data, the model is to determine the effective decision-making unit , and to determine the production frontier.
is the copper loss of transformer Data model of line loss potential analysis of Gansu Power Grid Suppose that Line loss is x1, transformer loss is x2 and others loss is x3.
An output indicators is the whole network line loss rate, the input and output data of each decision-making unit is given by the following matrix.
(5) (6) In this model, X1i, X2i, X3i and yi are the known data.

Many researchers attempted to evaluate reduction factors for lateral stiffness in order to reflect stiffness reduction effect on slab analysis.
Stiffness Reduction Factor for Flat Plate Slab Variable for Stiffness Reduction.
To propose the stiffness reduction factor, this study analyzed the variables having an effect on stiffness reduction in the prior test results of slab-column connection.
Proposal of Stiffness Reduction Factor.
Fig. 2(a) and (b) show similar tendency for stiffness reduction, but Fig. 2(b) is displays minimum data dispersion.

The analysis results are also compared with the experiment data from published references and they show a good agreement with the experiment data.
As shown in Figs. 4a the calculated values from two material models agree well with the experiment data.
And the results from the Model 2 are lower than the experiment data until the wear depth ratio becomes 0.8.
It means that Model 2 gives conservative pressure values comparing with the Model 1and the experiment data.
And the calculated results are compared with the experiment data in the references [4, 5] except the case when the wear length is 50.8 mm because of absence of appropriate experiment data.

The standard Gibbs free energy change can be calculated by using basic thermodynamics data [8] when the vanadium oxides were reduced by reducing agent ferrosilicon
Then it is more beneficial to the ferrosilicon reduction reaction.
After 3~5 minutes, prepare tapping after the self-reduction agglomerate are fully melting.
In the self-reduction agglomerate, the amount of reluctant ferrosilicon is surplus.
(In Chinese) [8] Yingjiao Liang, Yinchang Che: Thermodynamics Data Book of Inorganic Compound.

Rule acquisition is a hot topic in the field of data mining.
Introduction Rough set theory proposed by Pawlak [1] is an effective mathematical tool, which can deal with imprecise, uncertain, inconsistent data.
Essentially, there are only distribution reduction and assignment reduction.
Conclusions In the paper, under the model framework of the granularity of the rough set theory, we use rule extraction algorithm to mining the credibility of the implicit rules from the inconsistent decision-making system, identify data with maximum distribution reduction,which decision-making most likely to occur.
Acknowledgment This work has been supported by the National Natural Science Foundation of China (Grant No. 11205029） References [1] Pawlak Z,“Rough Sets theoretical aspects of reasoning about Data,”Dordrecht Kluwer Academic Publishers, New York 1991,pp.9-30