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The datas are taken by the actual operation condition.
The observed data is then calculated and plotted on OEM performance curve.
If reduction in flow is continuous, at one point the blower operation will become unstable and a momentary flow reversal will take place.
[4] Performance curve of 1st Reactor Circulation gas Blower K-2201, Basic Data Book Unit Polypropylene vol.9, Pertamina, Palembang, Indonesia
(Bill) Forsthoffer, Forstoffer’s Rotating Equipment Handbooks vol 1., Elsevier, Oxford, 2005 [6] Compressor and Expander in Engeneering Data Book, 12th ed., Gas Processor Suppliers association, Oklahoma, 2004 [7] Ernest E.

Market economy stimulates drop forges to produce forged pieces of highest quality and dimension precision with the accent on reduction in production costs.
a) Prism-shaped semi-product b) Semi-product of ideal forging blank Fig. 2 Semi-product models Fig. 3 Model of forging tool For starting a simulation of forging of forged piece Lever it is necessary to properly define the input data – these data were set as follows: · Process: closed die forging · Forging machine: mechanical press · Material of semi-product: Magnesium T=200-400°C · Material of tool: ASTM A 681 (H13) · Temperature of semi-product: 400 °C · Temperature of tool: 250 °C · Coefficient of friction: 0.2 Fig.4 Numeric simulation of forging process in closed die cavity Demonstrative example of initiation and termination of forging simulation of Mg alloy type AZ31B in closed die at selected technological conditions shows Fig. 4.

The Kalman-filtered data exhibited similar characteristics to the sensor reading data, resulting in a relatively high mean error of 2.2205°C, which exceeded that of other scenarios.
Data recording was achieved by capturing Omron thermometer readings using a camera, with the Kalman-filtered data and timestamps displayed alongside shown in Fig. 11.
In the plotted data, the X-axis represents the data quantity, while the Y-axis represents temperature readings.
Fig. 12 displays the results of the sensor noise reduction with R = 1 and Q = 0.01, revealing a reduction in noise using the Kalman filter, with a mean error of 1.35°C.
Implementing this setting would lead to the loss of original data, as depicted in Fig. 14.

The main reasons for these inconsistencies are: (1) if the measurement of these two indices is rational; (2) if the integration model constructed is suitable to represent these sub-indices and the related factors; (3) if the data required by the model are available.
The related data were cited from Jiang et al. (2011) and obtained from corporate annual reports, environment statistic yearbook, China statistic yearbook, and world steel statistical yearbook during 2007-2010.
Table 1 Four categories of catastrophe models (Woodcock, 1974; Su, 2011) Indices selection and data standardization Environmental performance indices We randomly selected 21 listed companies of thermal power generation and 19 listed companies of steel production from Shanghai Stock Market.
To solve this problem, the original data of these indices should be dimensionless through data standardization.
However, a consistent conclusion has not been made due to index selection, data acquirement, and integration model.

Vertical motive performance test of the rigid hinge expansion joint of Jiashao Bridge Yi Zhao1,a, Tianbo Peng 1,b 1State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, No.1239, Siping Road, Shanghai, P.R.China, 200092.
Figure.3 The arrangement of displacement sensors The test data of the expansion in the vertical rotation test is listed in the Table.2.
Table.2 The test data of the expansion in the vertical rotation test (mm） Displacement sensors` number Maximum Minimum De1 De2 1 10.56 -12.02 2 14.02 -6.52 3 16.84 -16.34 4 22.7 -12.94 5 17.2 -17.1 -0.33 -1.17 6 19.2 -16.4 0.44 -1.04 7 19.3 -16.1 -1.09 -1.23 8 21.3 -14.2 -0.32 -0.72 The rotation angle α and the maximum deviation of displacement sensors De can be calculated as follows: α1=Dmax4+Dmax32-Dmin2+Dmin12d; α2=（Dmax1+Dmax2）2-（Dmin4+Dmin3）2d De1=Dmaxi-Dmax3+Dmax4-Dmin3b×bi; De2=Dmini-(Dmin3+Dmin4-Dmin3b×bi) α1 is the inverse hour rotation angle and α2 is the clockwise one; d is the distance between Sensor-1 and Sensor-3; Dmaxi is the maximum displacement of the Sensor-i; Dmini is the minimum displacement of the Sensor-i; De1 is the displacement deviation of inverse hour rotation and De2 is the one of clockwise rotation; b is the distance between Sensor-3 and Sensor-4; bi is the distance between Sensor-3 and Sensor-i ( i=5, 6, 7, 8).
The test data of the joint in the shearing deformation test is listed in the Table.3 Table.3 The test data of the joint in the shearing deformation test (mm) Displacement sensors` number Maximum Minimum De1 De2 1 5.94 -6.7 2 6.02 -6.16 3 8.16 -7.98 4 8.48 -7.68 5 7.8 -6.9 -0.40 1.04 6 7.8 -7 -0.47 0.89 7 7.3 -6.8 -1.06 1.03 8 7.5 -6.9 -0.93 0.83 The shearing deformation ML can be calculated as follows: ∆1=Dmax1+Dmax2+Dmax3+Dmax44; ∆2=Dmin1+Dmin2+Dmin3+Dmin44 ∆1 is the upward shearing deformation and ∆2 is the downward one; Dmaxi is the maximum displacement of the Sensor-i and Dmini is the minimum displacement; The shearing deformation is from 7.15mm to -7.13mm and the maximum deviation of all sensors is -1.06mm. 5 Conclusions In the paper the vertical rotation test and shear deformation test are designed to check the vertical motive performance of the rigid hinge expansion joint of Jiashao Bridge and provide reliable test data for designing and calculating the expansion and

Data analysis.The use of Microsoft Excel to process and analyze the data of simple.
Analysis of data processing and result.Design according to the test method in this experiment, by dilute acid concentration, temperature, reaction time, particle size, solid to liquid ratio of these five factors on the corn straw was pretreated, and measured the OD value of saccharification liquid, into the glucose standard curve of reduced sugar quality, and calculate the content of reducing sugar in sugar solution.
Data processing.As the five graphs in Fig.2 shows, including (1) for dilute sulfuric acid concentration on the corn stalk sugar producing effect, (2) as the ratio of solid to liquid of corn stalk sugar producing effect, (3) as the time of corn stalk sugar producing effect, (4) particle fineness of corn stalk sugar producing effect, (5)as the temperature of corn straw to produce sugar effect
Conclusions In this paper, by dilute sulfuric acid pretreatment the conclusion of corn straw under different factors, taking into account the impact on energy consumption and other factors[6], differences and also on the basis of the data obtained in the experiment shows, given the highest sugar yield test conditions for dilute sulfuric acid concentration 5%, ratio of solid to liquid was 1:10, reaction time is 80min, particle fineness of 120 mesh in the lowest energy consumption, reaction temperature, 100 ℃, this group of conditions of pretreatment of corn stalks is more reasonable, conducive to further for further processing of corn straw.
References [1] Palmowski L，Muller J．Influence of the size reduction of organic waste on their anaerobic digestion．In：11 International．Symposium on anaerobic digestion of solid waste[J]．Barcelona．1999，6：137—144

Results and discussion Crystalline phase of silicon sludge and air classified powder is investigated using a XRD and the data is shown in fig.1.
XRD data shows the presence of SiC, Fe and Si peak.
Fig. 1 X-ray diffraction patterns of (a) silicon sludge powder and (b) air classified powder SEM/EDS data shows the particle size and configuration of air classified powder.
From the data, we can see that fine paticles smaller than 500nm.
This data in accord with SEM analysis result.

Introduction Along with containing more and more PM2.5, the atmosphere is getting more and more harmful to the human’s health.Therefore, the reseach in PM2.5 characteristics and governance method is extremly significant.Based on the long-range AQI statistical data and weather data of Wuhan, the PM2.5 models of relevance, diffusion, emergency treatment and comprehensive governance is set up, as well as the reasonable regulation scheme.
Relevance between PM2.5 and AQI In order to find out the concentration relevance between PM2.5 and AQI of Wuhan, the stepwise regression analysis model is set up, and in regard of the blank data of the AQI data[1] , the five poionts moving average interpolation method is used, and the relevance is obtained by analysising the result.The main idea of the stepwise regression analysis is contant importing the variables, and remove or retain it after checking with F test, until the changing is no more clear[2].
(3) By analysising the gauss diffusion model in References[3][4] and the weather data[5], the diffusion diagram can be simulated by Matlab, which is shown in Fig.2.
(4) Where, and are undetermined coefficients, is effective emission reduction value.

Include:16 underground water level short-term observation well, the artificial monitoring method in 2011 June acquisition data 1 times; 6 long-term observation well of groundwater(K01~K06), monthly monitoring 1, monitoring date 1992~2011 month 15, using WS-1040 dynamic groundwater automatic monitoring data collection, see figure 1.
At the same time, collecting research area related year precipitation, 1989 study area water level contour map, groundwater depth map data, etc.
Based on the long-term observation well (K01 ~ K06) years water level change data, draw representative monitoring stations underground water level within the year and interannual dynamic curve , combined with related statistical characteristic value, induction underground water level within the year and interannual change law or trend.
The 22 observation point 2011 underground water level and buried depth data, draw 2011 regional water level contour map and groundwater depth map(V-Modflow 4.2 software), with the existing 1989 water level contour map and groundwater depth chart are compared.
Using the 22 points groundwater Cl- content data in 2011, draw in 2011 the groundwater Cl- content distribution (V-Modflow 4.2 software).

An assessment of the two models is done by comparing the yield loci and the experimental data points for different levels of plastic work.
The data set used here (CPB06 identification 4 in Tuninetti et al
In the latter, and even if the damage development is not noticeable, coa2 data set gives a good prediction of the onset of coalescence.
This data set is able to predict correctly the onset of coalescence for the notched specimen, but for an underestimated force.
Coa1 data set is more accurate predicting the onset of coalescence for the notched specimen R =1.5 [mm], but also for an underestimated force.