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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.

First, the pre-system uses principal component model to convert properties of the source data of the basic window, and it plays a role of dimensionality reduction; Second, the post-system uses the density model to execute clustering operation; Finally, it uses the summary of data, generated in before two steps, to execute simply second clustering and update the clustering result, which fit the requirements of streaming data features.
The Description of a Dynamic Data Stream Model Data Stream.
,an), as the data come constantly , the old data are out from one end of the window and the new data enter from the other one end.
Dimension reduction has been one important research topic in the field of pattern recognition, machine learning, and multivariate data analysis.
Especially with the arrival of the information age, people get large amounts of data, high dimension, unstructured data has become easy increasingly, which makes data reduction has become more urgent.

The percentage of storage reduction and data anomalies are investigated for every normal form and database system.
For the advantages, Normalization avoids data modification (INSERT/DELETE/UPDATE) anomalies as each data item resides in one place.
Data are organized and structured more logically.
Also, it enforces Referential Integrity on Data, namely the enforcement of relationships between data in joined tables.
ACM SIGMOD International Conference on Management of Data, May 31-June 1, 1979, Boston, Mass.

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

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

Recently, data mining approaches have been successfully applied to industrial data analysis to derive useful and comprehensive knowledge [4].
One of the new data mining theories is the Rough Set Theory (RST) (Pawlak, 1982), which can be used for reduction of data sets, finding hidden data patterns and generation of decision rules.
In this study, we propose a systematic approach, including data preprocessing, data reduction, and rule generation, for selecting a group of attributes capable of representing mould risk assessment.
In our work, basic event’s historical data is organized into a decision table.
Some of those attribute data are incomplete, vague or ambivalent in Table 2.

Thermodynamic Modeling of Iron Ore Reduction Using Synthesis Gas A.N.
The thermodynamic model constructed corresponds to the literature and calculated data and can be used to optimize the reduction process under various production conditions.
In order to perform the requisite calculation, the following data is required: 1.
Furthermore, the database encompasses data on condensed carbon (graphite).
The graphical data indicate that the most significant components are Fe(s1), FeO(s2) and Fe3O4(s2), with a concentration exceeding 10-1 mol%.

Study on Province-wide Evaluation Index System of Energy-saving and Emission-Reduction in China Shuang Wang Economics and Management College, Dalian University, Dalian, 116600, China wshuang1021@sina.com Keywords: Energy-saving and emission-reduction; Index system; Evaluation Abstract.
In order to arrange energy-saving and emissions- reduction better, achieve the strategic target for energy-saving and emission-reduction, improve production environments, make comprehensive use of resources and develop circular economy, the establishment of a comprehensive and perfect evaluation system of province-wide energy-saving and emission-reduction is of great significance both to the region and the country.
The Construction Principle of Energy-saving and Emission-reduction Index System (1) Comprehensive and Scientific Comprehensive means that we should be all-round coverage of energy-saving and emission-reduction work, widely consider various factors that may affect the energy-saving and emission-reduction, systematically collected selection.
(3) Maneuverability In order to make energy-saving and emission-reduction index system can be effectively applied in practical analysis, solve practical problems, the concept of selecting indicators and the content must be clear and has observables, and have the corresponding data support, rather than take a one side pursuit of perfect theoretical level [2].
Conclusion Only when governments supervise can the work of energy-saving and emission-reduction require to success.

The abstraction of diagnostic feature from field condition monitoring data is a significant research challenge.
CCM Based Dimension Reduction 3.1 Dimension Reduction The degree of correlation between two parameters is represented by correlation coefficient, whereas CCM represents multi-parameter correlations.
(4)Set the correlation factor threshold T=0.80, and run the reduction steps in Matlab, the dimension is reduced to 9
The original feature vectors have 18 elements each before reduction, all of which belongs to three classes.
The test shows that after reduction the recognition rates are equal or even higher than that of before.

One of the modern methods of in-depth data analysis is the process of data mining which forms one stage in a complex process of knowledge discovery in the databases known as the concept of KDD (Knowledge Discovery in Database).
Data mining methods in structural identification In the process of structural identification, we can define the main fields of problems, which are the identification of structural relations, reduction of the system recognition level, and significance analysis of input and output variables.
In primary analyses of extensive databases, the methods for neural networks have proved to be a very powerful tool for data processing.
The main task of this type of neural networks is to reveal spatial representation of complex data structures.
The data mining methods provide large opportunities for data collection and representation, offering suitable techniques to retrieve the knowledge about mutual data dependencies, which can be used in the identification process of complex dynamic systems.