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Energy Consumer Demand Predict of Hebei Province in the Domain of Low-carbon Economy---- Analysis Based on Gray Model GM (1,1) Juqin Wang1, Baolin Li2 1Department of NCEP University of Baoding, Baoding, Hebei Province, China 2Department of NCEP University of Baoding, Baoding, Hebei Province, China ice_0613@126.com Keywords: Low-carbon economy; energy consumption of Hebei Province; GM (1,1) model Abstract.
In this background, based on energy consumption situation in Hebei Province，we use GM (1,1) gray prediction model to predict Hebei Province’s energy consumption and energy consumption structure in 2003-2010.
x(0)=x01,x02,x0(3),x04,x05,x06,x07,x0(8) =15298,17348,19836,21794,23585,24321,25419,27531 This series accumulated once than generate a new data sequence: x(1)= 15298,32646,52482,74276 , 97861,122182,147601,175132 GM (1,1) model data matrix: B=-12x11+x12 1-12x12+x13 1⋮-12x1n-1+x1n 1=-23972 1-42564 1-63379 1-86069 1-110020 1-134890 1-161370 1 Yn=x02x03⋮x0(n)=1529817348198362179421794235852432125419 Calculated differential equation of GM (1,1) parametersa, u, substituting B and Yn.
The results show in Table 5: Table 5:2011-2015 coal, oil, natural gas consumption predictive value of Hebei Province Year 2011 2012 2013 2014 2015 Predictive value of coal（Ten thousand tons） Proportion（%） 27181 91.50 29075 91.43 31101 91.37 33268 91.30 35585 91.23 Predictive value of oil（Ten thousand tons） Improved predictive value of oil（Ten thousand tons） Proportion（%） 1948 2236 7.53 2041 2452 7.71 2137 2726 8.01 2239 3079 8.45 2345 3546 9.09 Predictive value of gas（One hundred million cubic meters） Improved predictive value of gas（One hundred million cubic meters） Proportion（%） 467 479 1.61 590 602 1.89 747 757 2.22 945 954 2.62 1195 1204 3.09 The analysis of 2011-2015 Hebei Energy Consumption demand prediction In this paper, we use 2003 - 2010 energy consumption data in Hebei Province to establish the gray GM (1,1) model.
References [1] Deng Julong.

Table 1.
Fig. 1.
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Melchers, Cement Concrete Res. 61–62 (2014) 1-10
Richardson, Cement Concrete Comp. 32(7) (2010) 479-485

Fig.1 shows the magnetizing technique for yoke in according to the standard DIN_EN_1290-2002.
For longitudinal cracks For transverse cracks Fig.1 Yoke magnetizing techniques for fillet weld Theoretical background The MFL problem can be treated as a magnetostatic problem by using of magnetic scalar potential method.
In the domain of a magnetostatic field problem, a solution is sought which satisfies the Maxwell Eq (1) and (2).
(a) Horizontal component, Bx (b) Vertical component, By Fig. 6 The influence of distance S on MFL density Conclusions 1) The magnetic flux density at the center of flaw decreases abruptly as the air gap under yoke pole increases, so the suggestion is that the air gap should not be above 1.5mm during the practical detection process. 2) When the distance S of fillet weld away from magnetic poles increases, the MFL density decreases.
References [1] Huang Zuoying, Que Peiwen, Chen Liang, 3D FEM analysis in magnetic flux leakage method, NDT&E International 2006;39;61-66 [2] F.I Al-Naemi, J.P.Hall, A.J.Moses, FEM modeling techniques of magnetic flux leakage-type NDT for ferromagnetic plate inspections, Journal of Magnetic Material, 2006;304;790-793 [3] M.Katoh, K.Nishio, T.Yamaguchi, FEM study on the influence of air gap and specimen thickness on the detestability of flaw in the yoke method, NDT&E International 2000;33;333-339 [4] Mitsuaki Katoh, Modeling of the yoke-magnetization in MFL-testing by finite elements, NDT&E International 2003;36;479-486 [5] M.Katoh, K.Nishio, The influence of modeled B-H curve on the density of the magnetic leakage flux due to a flaw using yoke-magnetization, NDT&E International 2004;32;603-609

Part I, P/M parts were prepared by distaloy HP powder that made by högöanas Sweden company (The chemical composition of this powder is shown in Tab.1).
The density of these parts is 7.65 gr/cm3, The obtained result shows less than 1% content of porosity that shows the effect of forging on reduction of pores.
Fig. 1.
(a) (b) References [1] S.
Sarma: Materials Science and Engineering A Vol. 479 (2008), p. 164

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Wen, Solid State Ionics, vol. 160, no. 1–2, pp. 141–148, May 2003
Gupta, Ionics, vol. 17, no. 6, pp. 479–483, May 2011
Lee, Solid State Ionics, vol. 186, no. 1, pp. 1–6, Mar. 2011
Phys., vol. 126, no. 1–2, pp. 404–408, Mar. 2011

Ni 1 structural steel has its specified CCT curves.
It can be analyzed and compared by contrast with the common dynamic phase diagram [1,4] each other.
Fig.3 Air-cooling new-type dynamic phase diagram of φ10 mm Ni 1 structural steel Fig. 4 Air-cooling new-type dynamic phase diagram of φ2 mm Ni 1 structural steel bar (alloy Ⅲ) Ⅲ.
Because space limited ,the test inspection is ommitted[1-4] Ⅴ.
References [1] Z Qing-Hua, C Hong, C Xiao-Dao, New-Type Dynamical Phase Diagrams and a Nonequilibrium-Lever Rule for Carbon Steels[J].The Physics of Metals and Metallography, 100 (5) (2005) pp.472-479

The schematic diagram of microwave-assisted sintering setup used in this study is shown in Fig. 1(a).
For the reinforcement material, Si3N4 powder with an average particle size < 1 µm obtained from Sigma Aldrich was used.
The blended powders were weighed approximately 1 g using a weighing balance for each green compact pellet form.
Table 1: Temperature measurement results on different microwave power output with and without SiC crucible.
Scripta Materialia 2005;52:479-483

Fig.1: Main types of aging and damage.
Table 6: Temperatures used and cases studied Studied case Case 1-550 Case 12-560 Case 13-479 Case 14-480 Case 15-450 Temperature (°F) 550 560 479 480 450 Fig. 19 regroups the initiation probability curves for the 5 cases studied.
Results are printed at each evaluation time for case1-550, case12-560, case13-479, case14-480 and case15-450.
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Journal of Piping and Pressure vessel, 111–112 (2013) 1–11

Invoking the orthonormal property of V, post-multiplying Eq. (2) by V on both sides gives [ ] [ ] 1 0 1 1 , ∆ ∆ = F V F V U S 0 (4) from which F∆V0 = 0.
Total number of points 1600 1600 1600 1600 1600 1600 Lost percentage (%) 22.00 23.75 24.50 23.50 19.00 23.25 Number of iterations 12 14 14 11 14 10 Number of frequencies used in the last iteration 479 467 495 367 479 343 Relative error at the last iteration (%) 0.62 0.33 0.55 0.72 0.87 0.83 Table 4 Computational details for signal reconstruction at 6 sensor nodes Note: see Fig. 4 for sensor node locations.
Set number Sensors at nodes 1 [1, 2, 3, 5, 8, 9] 1 8 2 [1, 2, 3, 5, 8] 4 5 8 12 14 3 [2, 3, 5, 8, 9] 1 4 8 12 Potential damaged elements Table 5 Damaged detection of experimental truss structure Fig. 1.
Block diagram for data loss reconstruction algorithm Column Tube Beam Beam Element 7 end 1 end 1 (2) Element 18 (7) end 2 Element 18 10m 5m 5m 5m 5m 10m 20m 4m 4m 1m 9m 12 13 199 8 4 1 2 1 95 20 14 15 16 13 21 22 10 12 17 18 10 11 7 8 6 3 4 5 6 7 2 3 1 - element number 1 - node number Column 11 Fig. 3.
Quek: Proceeding of The Twenty-first KKCNN symposium on Civil Engineering, 27-28, Oct, Singapore (2008), p. 105. 3m 1m 1m 1m 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1m m2 1 2 3 4 5 6 7 8 9 10 1 - element number 1 - node number /2

(1) Assume the change trend of operation reliability (curve 1) is in accordance with exponential distribution, and can be expressed as , (1) where i means the i times maintenance, when i=0, it means the initial state of system, that is to say t0=0，R0=1; Set the system failure rate λ is fixed value, that is to say λ=c, (λ>0)
Therefore, in the perspective of economy, it may have not need to maintain again. 4 Application of time-variant reliability maintenance model with Weibull distribution operation reliability 4.1 Basic assumptions of the model (1)Assume the initial reliability is 1 and no minimum guarantee life, so the change trend of operation reliability (curve 1) is accordance with two parameter Weibull distribution [9], and can be expressed as , (11) where i means the i times maintenance, when i=0, it means the initial state of system, that is to say t0=0，R0=1; Set η as characteristic life and m as shape parameter, they are fixed value
Proceedings of the 7th International Conference on Properties and Applications of Dielectric Materials, June 2003, 1-5, Nagoya: 479-482
AIIE Transactions, 1994, 1(3): 221-228
Proceedings of the 7th International Conference on Properties and Applications of Dielectric Materials, June 2003, 1-5, Nagoya: 479-482