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Then CO2 emission is calculated in the RHF—EAF flow for treating V-Ti magnetite according to data from the pilot plant in Pangang Group.
Based on the data from design and real production the text calculated the quantum of CO2 emissions.
According to these data above we can calculate the ratio of blast furnace gas and coke oven gas and the result is that among the gas mixture blast furnace gas accounts for 60% ,of course the rest is coke oven gas.
The carbon content of metallized pellets is about 5% and the carbon content in the hot metal is around 2%.According to the mass balance calculation we can get the data of CO2 emissions to produce one ton hot metal.
The calculation is based on the energy consumption which is different from the calculation of the RHF-EAF process and all of the data come from the real production.

Some issues in KDD include data mining for complex data structures and complex modelling.
Data Mining in Complex Data Structures This paper tackles data in complex structureas follows.
Data Preprocessing .
Mining Mixed Data .
Clustering High Dimensional Data: This paper suggests to establish strategies for dimensionality reduction; subspace clustering of high dimensional data; and subspace ranking.

RDF inference component, according to the data generated RDF CardOnto and description information semantic closure.
The combination of RDF and XML, not only can the concept system of concepts linked with real world knowledge, to realize the data based on semantic description, also give full play to the respective advantages of XML and RDF, facilitate Web data retrieval and knowledge discovery, and then defined and build the clinical ontology CardOnto according to the medical classification principles of traffic organization, get some clinical diseases, the concept of ontology system, this structure can clearly and accurately represent the knowledge structure of the disease.
Concept lattice redundant relations reduction algorithm achieving Ontology integration In response to the above definition of distributed ontology integration adapter, the project puts forward one of distributed ontology integration based on concept lattice and reduction method, this method doesn’t rely on artificial participation, and it has a high degree of formalization and can be directly on the computer, taking into the quality and efficiency of ontology integration.
Redundant relations reduction algorithm Set up a stack, all the concept of into degree 0 in the concept lattice will be pressed into stack.
To this end, the steps of redundant relations reduction algorithm are: (1) Press the concept of no precursor (count domain is 0) into stack S1

Electrode metal powders are generally synthesized by the wet reduction process [1-2], chemical vapor deposition method [3] and spray dry method [4].
In this study, Ni powders were prepared by the reduction of hydrazine of Ni salts from diethanolamine (DEA) solvent.
Hydrazine hydrate (N2H4⋅H2O) was used as reducing agent and the reduction was allowed to proceed at 180~240 °C for 0~40 minutes.
XRD data revealed the characteristics of nickel crystalline as shown in Fig. 2. 10 20 30 40 50 60 70 80 (c) (b) (a) 2-Theta Intensity (a.u
(a) (b) (c) (d) 3㎛ 3㎛ 3㎛ 3㎛The formation of nickel powder by the reduction of hydrazine of nickel salts from DEA solvent is expected to include the following steps: (1) dissolution of NiCl2, (2) formation of Ni(NH3)n, (3) reduction of dissolution species and (4) nucleation and growth of nickel particles.

When reduced, boundary region of neighborhood-based rough set narrows, on the contrary, open out, indicated that the neighborhood-based rough set has a certain degree of tolerance to the data noise, which can enhance the robustness of the produced rules.
The results show that the model can not only deal directly with the continuous attributes, but also has a certain degree of noise tolerance, it can enhance the robustness of data analysis and processing and is better adapted to practical problems in engineering.
References [1] Berka P, Bruha I., “Discretization and grouping: preprocessing steps for data mining”.
The 6th European Conference on Principles of Data Mining and Knowledge Discovery, Helsinki, Finland, 1998, pp.239-245
[4] Jensen R., Shen Q., “Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches”, IEEE Transactions on Knowledge and Data Engineering 16(12), 2004, pp. 1457-1471

Strength Reduction Elasto-plastic finite element is a method that combined with the strength reduction technique, the limit equilibrium theory and the principle of elastic-plastic finite element, which firstly determine the stress, strain or displacement field of slope for a given strength reduction factor.
And the reduction factor is defined as the slope stability safety factor.
He thought that elastic strength reduction finite element method has strong adaptability.
design and measured data, the calculation models are shown in Fig.2～Fig.7.
Slope stability analysis by strength reduction[J].

Analysis of Unstable Rock-Mass Stability Based on Limit Equilibrium Method and Strength Reduction Method Ying Kong1, a *, Huapeng Shi2, b, Hongming Yu1, c 1Faculty of Engineering, China University of Geosciences, Wuhan, China 2Guangxi Communications Planning Surveying And Designing Institute, Guangxi, China akywx5407@163.com, b276332792@qq.com, cyuhongming55@sohu.com Keywords: Limit equilibrium method, Strength reduction method, Unstable rock mass, Stability analysis Abstract.
Thus, 3D laser scanning was used to acquire geometric feature data, including the length, width, height of unstable rock masses, controlled structural plane, and cutting depth of posterior-margin joints, and to plot the sections of the unstable rock masses (Fig. 2).
Since the saturated cohesion and internal friction angle of unstable rock mass are inaccessible, we adopted similar strata for analogy, and referred to the regional empirical data: 0.21 MPa, 29.8°; the geometric dimensions of the unstable rock masses are showed in Fig. 2.
According to SRM, the slope stability safety factor is defined as the reduction degree of a soil-rock body strength parameter when the slope reaches critical failure, or namely the ratio of actual strength to critical-failure strength of soil-rock body, or namely the reciprocal of the reduction factor [6].
Zhou, Application of Local Strength Reduction Method Based on Particle Flow Code in Slope Stability Analysis, Sci.

The formula was verified by the comparison of the theoretical results with the experimental data.
Then the theoretical results were compared with the experimental data as well and good agreement can be observed.
(14) Results comparison and parametric analysis (1)Comparison with the experimental data When, 4.0and, substitute other parameters including size of the column, concrete strength and CFRP strength stated as reference [6] into Eq. 14, the theoretical results are obtained as list in Table 1, which are compared with the experimental data. is the experimental data[6] and is theoretical results obtained in this paper.
Take theoretical results and experimental data of D1, D2, B2, D3 and D4, the relationship curves of the eccentric bearing capacity and eccentricity ratio is obtained as shown in Fig. 5.
It shows that the eccentric bearing capacity decreases with , which a consistent with the experimental data.

However, in typical image recognition in 1D vectors space, where the number of data samples is smaller than the dimension of data space, suffering from the singularity problem of matrix, NPE algorithm cannot be implemented directly.
However, they are defined only on the training data points and cannot show explicit maps on new testing data points for recognition problem.
In both experiments, we split randomly the data set into two parts.
And relative to 2DLPP and B2DLPP, 2DNPE and B2DNPE have total different ways to preserve local structure of data manifold.
[4] M.Belkin, P.Niyogi, Laplacian Eigenmaps for dimensionality reduction and data representation, Neural Computation, 2003,15:1373-96,

The energy consumption data were derived from China Energy Statistic Yearbook 2002-2011 [6].
Shanghai’s population and annual GDP data were derived from Shanghai Statistics Yearbook 2002-2011[9].
Data of ESn and β were derived from Shanghai energy-saving report [10].
The historic data on carbon intensity reduction showed that the measures taken by Shanghai worked effectively in the past few years.
The data in Fig. 2 shows that industry sector accounts for over 90% of the total carbon emissions over the period 2002-2010.