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The quantitative data of phases are summarized in table 1.
The quantitative data of phases by Mössbauer spectroscopy.
The quantitative data of phases, 1:12 average hyperfine fields for NdFe11Ti, NdFe10.5Mo1.5 and NdFe10.75Mo1.25 as-prepared.
The processing parameters, quantitative data of phases and magnetic properties of samples are summarized in table 4.
(see TC data in tables 4 and 5).

The thickness reduction was measured by optical 3D scanning method and the influence of the feed, workpiece geometry and planar anisotropy of the blank on the wall thickness reduction was studied.
Based on the results it is determined that the highest reduction of wall thickness is observed in the conical part of the experimental sample.
For the experimental measurement of shape accuracy a non-contact data capture method was used.
Control factors and levels Parameter Sign Level 1 Level 2 Level 3 Feed ratio (mm/rev) Workpiece geometry (-) f pm 1 (1) radius R10 1,5 (2) conical area 2 (3) cylindrical area Rolling direction of the sheet (deg) rd 0 45 90 Analysis of data Using Minitab 16 software, ANOVA (Analysis of Variance) was performed to determine which parameter and two-way interactions significantly affect the performance characteristics.
Analysis of variance (S/N data) Source Sum of squares DoF Mean square p-value F- ratio f 0.066052 2 0.033026 0.000 236.21* pm 0.525874 2 0.262937 0.000 1880.61* rd 0.001030 2 0.000515 0.074 3.68 f*rd 0.001170 4 0.000293 0.174 2.09 f*pm 0.042593 4 0.010648 0.000 76.16* pm*rd 0.001015 4 0.000254 0.219 1.81 Other/Error 0.001119 8 0.000140 Total 0.638852 26 Tabulated F-ratio at 95% confidence level: F (0.05; 2.8) = 4.46; F (0.05; 4.8) = 3.84 * significant parameter or significant two-way interaction Fig. 3 Relation between response (reduction ratio t0/t1) and variables (feed ratio, place of measure) Conclusions Based on the experimental investigation and data analysis using ANOVA has demonstrated that the workpiece shape is very significant factor which intensively influence the wall thickness reduction of spun part.

Through the Ethernet switch conveniently and other systems for the connection and data information sharing, for the large-scale system can also set the data server and gateway server and other systems for the connection.
The method further interpretation Various types of buildings using electronic remote intelligent measuring instrument of energy consumption data ( such as electricity, water, temperature and humidity ), will realize all kinds of energy consumption data real-time acquisition and uploads to the center server.
Data transmission cable ( RJ45 LAN network port ) / wireless ( GPRS/CDMA ) for data remote transmission.
The data packet will be transmitted to the " energy saving supervision platform " in the database, a large amount of historical data is permanently stored, to facilitate future calls, analysis and reference as shown in Fig.2.
Fig.2 System hierarchical structure Internet consumption monitoring Combination of campus GIS geographic information, to the environment as the background, the building energy consumption data as the analysis object, can be intuitive for energy consumption data were analyzed, at the same time application, mobile phone, PDA and many other data terminal to implement energy detection.

Some researches, chiefly focuses on potential and ways of energy conservation and emissions reduction, are mostly based on energy conservation and less on emissions reduction.
Water Pollution Emission Reduction Measures.
This study was to obtain the potimal emision reduction scheme based on a selected typical enterprise parameters to verify the feasibility of the established model, the specific data were shown in Table 1 to Table 3.
Table 1 Data for pollution reduction potential model of refining and chemical enterprises Pollutant Pollutant source Industrial emissions standards(mg/L) Wastewater quantity(t/h) Pollutant concentration (mg/L) COD Catalytic reforming unit(CRU) 200 1060 194 Catalytic cracing unit(CCU) 200 1960 289 Delay catalytic unit 200 3012 289 Hydrofining unit — 0 0 Oxidized asphalt plant — 0 0 Acrylonitrile unit — 0 0 Furfural treatment 200 2960 222 Ethylene unit 200 1160 300 Propylene unit 200 960 661 Pressure-relief devices 200 1160 451 Sulfur recovery unit — 0 0 NH3-N Catalytic cracing unit 25 1010 32.6 Furfural treatment 25 2167 13.01 Sulfur recovery unit (containing sewage stripping) 25 1651 44.7 Table 2 Pollution control equipment parameters for refining and chemical industrial Pollutants Pollution control measures Removal rate (%) Unit processing costs (104 t/104 yuan) Buiding area (m³) COD SBR 90 4000 500 MycelxTM 85 1000 400 NH3-N SBR 75 2000 500 MycelxTM 85 4000 700 Table 3 Existing
In discussion, the following may be drawn based on the optimization model calculation results of this study: (1) For the maximum pollution emission reductions of oil refining chemical enterprise, the maximum emission reductions of COD for refining and chemical enterprises was 6481.50 tons, calculated by means of computer, remarkably improved 29.63% compared with the target reductions (5000 tons); Similarly, the maximum emission reductions of NH3-N was 549.51 tons, improved 9.90% compared with the target reductions (500 tons), which has excellently finished the pollution emission reduction task during "the 12th Five-Year Plan".

Study on the Lining Erosion of Deep Reduction Electric Arc Furnace in Smelting Metallized Pellets Process Produced by Vanadium Titanium Magnetite Qi Jianling1, a 1 PanGang Group Research Institute Co., Ltd. , State Key Laboratory of Vanadium and Titanium Resources Comprehensive Utilization, Panzhihua 617000, Sichuan, china ) aqijianchou123@126.com Key words：deep reduction electric arc furnace；refractory; erosion Abstract.The erosion of the lining refractory of the deep reduction electric arc furnace is introduced, and the erosion appears in the process of smelting metallized pelltes produced by vanadium titanium magnetite.
Introduction A pilot plant was constructed in PanGang in 2009 which applied Rotary Hearth Furnace (RHF) and deep reduction Electric Arc Furnace (EAF) to deal with vanadium titanium magnetite.
Fig.1 Analysis of lining erosion mechanism Arc erosion of the deep reduction electric arc furnace (EAF) The electrode diameter of the EAF is 2200mm and the distance between outer edge of electrode and the lining is only 550mm.
However, the charges of the deep reduction EAF are the metalized pellets, and in order to improve the TiO2 content in the slag, it does not adjust the basic of the slag and the value of CaO/SiO2 is just around 0.2～0.3.
Handbook of chart data about steelmaking[M].

Key words: BrO3-; Titanium Dioxide; Photocatalytic Reduction Kinetics.
Their experimental data showed that the kinetic curve of the bromate photocatalytic removal at pH 5.0 was very different from that at pH 7.0 and the bromate removal was greatly promoted at pH 5.0.
To verify the effect on bromate reduction at low pH, the bromate reduction was investigated under dark condition at pH 3.0.
However, in fact, they still play a certain roles in promoting bromate reduction.
In the existence of Fe3+, the bromate reduction is easier than other cations.

But the reaction requires 6-10 equivalents of NaBH4 for the reduction.
Results of the reduction of methyl benzoate in different solvents.a Entry NaBH4 (equiv.)
Possible mechanism and tentative intermediates in the reduction of the esters.
Products were all known compounds and were identified by comparing of their physical and spectra data with those reported in the literature.
General experimental procedure for the reduction of esters.

Gene expression data usually have only a dozen or a few dozens of samples, but hundreds or even more than a million feature variables, if we classify the data directly, often fail to get good results, so for such a large data, dimensionality reduction becomes a key to the success of gene data classification.
High-dimensional data reduction method Data dimensionality reduction has played a more and more important role in research recent years.
Map high-dimensional data to low-dimensional space, and low-dimensional data can reflect the information in the original high-dimensional data, this is called data dimensionality reduction[2].
PCA is short for principal component analysis, it is a linear method which compresses data through the covariance matrix of the data, integrate the original data to extract the comprehensive variables which reflect the information of the original data best, comprehensive variables extracted are called principle component, the principle component is usually a linear combination of the original data[3].
In this study, the first 38 group of samples as the training data, the latter group of 34 samples as the test data, using two methods for testing, one is the non-linear Laplacian Eigenmaps dimensionality reduction combined with SVM (linear kernel function) classification, the other method is the linear dimensionality reduction PCA dimensionality reduction combined with SVM (linear kernel function) classification.

Increasing O2 concentration can also improve the catalytic reduction efficiency.
It is clearly seen from both figures that once the engine is switched to DF-PCCI mode, CO emissions are substantially higher and OEM DOC is ineffective while exhaust temperatures from each combustion modes are very similar (data are not shown here but the reader can find more details in [8]).
Ranges of each parameters employing in DOE are chosen based on data from NEDC.
However, if exhaust temperature is up to 250oC, CO reduction efficiency can reach up to 100 % (case12-15) as well.
Reduction efficiency significantly improves after exhaust temperature is up to 250oC.

The paper analyzes the fuel-switching project for district heating and main pollutant reductions, emission reductions of atmospheric particulate matter are calculated by materials accounting method.
The economic, environmental and social benefits are also calculated according to pollutant reductions.
In this paper, reductions of the pollutant emissions have been calculated and the co-benefits have been analyzed.
Reference to the literature [4,5] get the data of the residential layer burning wet de-dusting boilers as Table2 : Table 2 Data of the layer burning boiler Combustion mode The dust of the coal The bottom ash in the total ash Proportion of particulate matter in the flue gas Removal efficiency Layer burning boiler 0.084 0.85 0.80 0.13 0.07 0.99 0.90 0.50 This project only consider the residential heating boilers, so A is the coal consumption, is 44.72t.
Suppose this project only use one control technology, so , and , from calculations, all the particulate matter emissions can be obtained from Table 3: Table 3 Result of three kinds of particulate matter emissions Particulate Matter 0.0126 [t/t] 1.008 1.638 4.41 [t] 450.78 732.51 1972.15 The emission reductions of particulate matter: Natural gas as clean energy, regarded as no particulate emissions after burning, so the emission reductions of TSP is 3155.44t, the emission reductions of PM10 is 2704.66t, and the emission reductions of PM2.5 is 1972.15t.