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Carbothermal Reduction Synthesis Of Superfined TiC Powders From TiOSO4 Jie Li1, a, Yi Wu2,b , Pengfei Li3,c 1,2,3Key Laboratory of Nonferrous Materials and New Processing Technology of Ministry of Education, Guilin University of Technology, Guilin 541004, Guangxi, China a43450744@qq.com, b1390209551@qq.com, c376854620@qq.com Keywords: Carbothermal reduction; TiC; Superfine Powders ; TiOSO4 Abstract.
Thermal decomposition of TiSO4, and carbothermal reduction of the mixtures were investigated by using thermogravimetry-differential thermal analyzer and x-ray diffraction analyzer.
Nanostructured TiC was synthesized by methods such as liquid-magnesium reduction of vaporized mixture of TiCl4 and CCl4 solution.
Through solution phase mixing under moderate conditions and subsequent heat treatment at a temperature which was still considerably lower than compared to that used in conventional carbothermal reduction synthesis of TiC.
However, the DSC data indicate that the mixed powder experiences two complete reactions up to 1500˚C.

The state value of energy structure evolution (EUSD) greater than 1, if EUSD value increases over time, the energy consumption structure optimization or energy consumption structure will present normal evolution; otherwise, energy consumption structure will appear inferior or negative evolution. 2.2 Data processing and consequence analysis Choosing time-series data which various types of energy consumption and CO2 emission of energy consumption in nationwide or Hebei province between 1980 and 2012, the calculated CO2 emission intensity correlate value ESCE and the state value EUSD of energy consumption structure in nationwide and Hebei province are shown in table 1.
However, the data dropped period again from 2004 to 2007, finally the statistic fluctuating rose between 2008 and 2012; the state value of Hebei’s energy consumption evolution fluctuated between 1.07 and 1.20, inconspicuous interim of evolution process.
According to energy structure—CO2 emission intensity correlate value (ESCE) , first, ESCE value of nationwide and Hebei province appeared general falling trend, and the main driving force isn’t the evolution of energy consumption structure, but the improvement of energy utilization efficiency led by technology progress; Second, compared to the nationwide data Hebei’s ESCE was much greater and decreasing speed was lower than the nationwide average level.
That is a key link of energy-saving and emission-reduction and low carbon economy development.
Goals of energy structure adjustment should be imbedded through mandatory mechanism in low carbon reduction framework, and then through the incentive mechanism, pressure mechanism and supporting mechanisms to ensure the overall realization of the goals that energy saving and emission reduction and structure upgrade, Low carbon policy in Hebei province are basically center on "to achieve energy-saving and emission-reduction targets", relying on administrative commands and rewards and punishment mechanism to urge the achievements of the goals.

Basic Theory of Strength Reduction Finite Element Method Strength reduction FEM combines strength reduction technique with elastic-plastic finite element method.
(2) Where, andare cohesion and internal friction angle in front of the reduction; andare cohesion and internal friction angle after reduction; is strength reduction factor, also known as strength reserve safety factor or slope stability safety factor.
Analysis data of land slope is numerous.
It’s based on the single- channel seismic data and shallow strata data collected from the slope zone of northern South China Sea to obtain a more accurate submarine slope form and shallow strata characteristics and to establish the simplified theoretical slope model (see 2.2).
Therefore, the parameters are obtained by making full use of existing research results of physical and mechanical characteristics of submarine soil in South China Sea, and trough the statistical regression analysis of static sounding data (see 2.3).

Data and model Data sources and processing.
Historical data include construction value added and energy (fossil fuels listed in Table 1) end-use of the seven regions in East China from 2000 to 2010, sourced from [5][6] and [7] over the years.
Construction value added of each regions in 2020 are predicted with Grey Prediction Model GM(1,1) based on historical data[8].
Especially Shandong would become the focus of emission reduction in East China, since the proportion of its CO2 intensity reduction was close to 60%.
The Preferring potential case stimulates decision makers take differences among regions’ emission reduction space into account, and expect provinces with more reduction space to undertake greater reduction burden.

Methodology and Framework Firstly, we estimate the CO2 emission reduction data of the HFCVs through the GREET model developed by Argonne National Laboratory [5].
U.S. had gotten great progress in its commercialization so the experiment data comes from DOE reports.
All the data of the CO2 emission reduction of HFCVs come from four kinds of hydrogen sources [6].
The data obtained by this method are consistent with the recent international study model results.
According to the GREET model and the data in Table 1, combined with existing data and formulas (1), (2) and (3), we can get the data in Table 2.

With the development of the computer science and technology, especially of computer network, there are a mass of data provided for people each moment.
The requirements of data analysis are more and more high due to the rapidly increase of the data.
However various approaches using rough set theory have been proposed to induce decision rules from data sets taking the form of decision systems.
Output: an attribute relative reduction B.
Rough Sets: Theoretical Aspects of Reasoning about Data.

Before commencing rule discovery, several data mining pre-processing tools are applied to reduce data complexity and controversy as well.
The figure corresponds to sampling rate used in the data acquisition.
To avoid excessive learning time, redundant samples were removed using a cluster-based data reduction method.
We note that the data reduction task was conducted separately for each of the histories.
Of the 393 filtered samples (i.e. outputs of k-means data reduction method), 209 were low damage segments and 184 were high damage segments.

Only I− has a higher promotion of the reduction of bromate at higher concentration than 15. 64 μmol/L, it is due to the strongly reduction potential E0(•I/ I−).
Results and discussion Fig. 1 The effects of NO3− on the Fig. 2 The effects of SO42− on the photocatalytic reduction of bromate photocatalytic reduction of bromate Effects of NO3−, SO42− and HCO3−/CO32−.
Figs. 4 and 5 show the effects of Cl− and I− on the photocatalytic reduction of bromate.
Only I− has a higher promotion of the reduction of bromate at higher concentration than 15. 64 μmol/L, it is due to the strongly reduction potential E0(•I/ I−).
A: Chem. 108(1997), p.37 [22] Wardman P.: J.Phys.Chem.Ref.Data 18(1989), p.1637

IT equipment and systems, housed in data centres, consume a considerable amount of energy.
In this paper, a numerical analysis of flow and temperature distribution of a raised-floor data centre is conducted in order to evaluate the thermal performance of the data centre.
The lower the SHI, the better the performance of the data centres.
As discussed by Fakhim et al. [6], data centre airspace can be divided into two main zones: data centre environment airspace excluding racks (AE), and airflow inside the racks (AR).
It is concluded that CRAC layout in model 11 to achieve an optimum performance in a medium data centre and model 6 leads to a worst performance of the data centre.

DWT-MCDF results in high accuracies with the least sensitivity to training data abundance.
The major drawback of dimension reduction methods is that these work in specific scenarios and no general technique is available that suits all hyperspectral data.
We propose a new classification algorithm based on sparse representation for the classification of hyperspectral data using few training samples in the original high dimensional space and no dimension reduction is applied.
Sparsity is an effective model that deals with constructing of efficient representations of data as linear combination of a few typical patterns which are learned from the data itself.
Support vector machine classifiers as applied to aviris data.