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Coal-fired power generation in thermal power is the most major method, data shows that the national power generation capacity was 966.413 million kilowatts by the end of 2010, of which thermal power was 704.1444 million kilowatts, accounting for 72.86% of the total installed capacity.
Therefore, the situation of energy conservation and emission reduction is very grim.
Because the coal consumption data is very poor, it will be carried out equal dimension new information to the model, when the forecast get a value ,added to the known data at the same time remove the oldest data, keep the dater series equal dimension, then established a new GM (1.1) model ,predict the next value, and then added to the known data at the same time remove the oldest data, such as metabolism, individually forecasts, followed by replacement, until complete of the prediction target and reach the prediction accuracy.
In addition, carbon capture and storage technology (CCS) has significant emission reduction.
In the calculation assumes that the proportion of thermal power units remain unchanged, taking the 2010 national data, that is thermal power units accounted for 73.68%; and assumes power grid net loss (3.9%) also remain unchanged.

It is connected to a custom PC, that has the capability for both spindle motion control and carriage vertical applied load control, and data acquisition and display unit (monitor).
Experimental design and data collection.
Data is displayed in real time on the monitor.
The analysis is done to extract all the information present in the data, taking account of variability and measurement error.
The analysis of variance (ANOVA) is done on the experimental data to evaluate the statistical significance of the model.

We will use this information for a dimension reduction of the parameter space in order to reduce the curse of dimensionality.
With the so constructed data base, we set up an interpolatory metamodel which can be evaluated much faster compared to simulation runs.
An RBF metamodel RNpar→RM is a linear combination of radial basis functions fx= j=1Nexpφx-xjcj (2) with Nexp the number of simulations and coefficients cj so that the interpolation condition holds, which means that all simulated functionals (original data) are predicted exactly.
Donoho, High-dimensional data analysis: The curses and blessings of dimensionality, AMS Conference “Math Challenges of the 21st Century”, 2000, available from http://www-stat.stanford.edu/~donoho/
Skillicorn, Understanding Complex Datasets: Data mining with matrix decompositions, Chapman & Hall / CRC, 2007

As deduced from laboratory studies, these CWL almost exclusively were adjusted to promote sulfate reduction, concurrently aiming at U reduction and precipitation of U minerals.
In order to contribute to the limited data set, about 20 small-sized natural wetlands in the former German uranium milling area nearby Ronneburg, Thuringia, were investigated hydrogeochemically by means of U accumulation processes.
The statistical evaluation of the data set may reveal primary elemental relations when normalized to refractory Si in the pore water depth profiles, as deduced in one of the wetlands from similar concentration distribution e.g.
On the other hand, in evaluating substrate elemental composition with respect to causal relations, both with measured data and data normalized to refractory Zr, clearly prevailing processes for U retention couldn't be elucidated.
In this position, U reduction and biomineralization of U(IV) species may provide the best conservation, unless forming minerals are nanometer-sized, compromising colloidal transportability.

The lattice parameters, oxygen content, volume of unit cell, orthorhombic distortion, hole concentration in CuO2 plane and charge on Cu-O plane were evaluated using XRD data.
The structural data obtained from XRD patterns are tabulated in Table 1.
The oxygen content in the samples is calculated by formula [5] from X-ray diffraction data using Eq. 1 and tabulated in the Table 1
The orthorhombic distortion was evaluated using XRD data by the formula [c/(a+b)] [6].
From XRD data, it is confirmed that all samples are in orthorhombic pervoskite structure.

These data were finally used to obtain parametric process maps.
Parameter Value(s) Heating rate [°C/s] 10 ±0.5 Soaking time [s] 60 ±0.1 Testing temperature [°C] 300, 350, 400 ±2 Testing strain rate [1/s] 0.5, 5, 100 Maximum strain [-] 1 Repeatability 3 Material flow curves.The force-displacement data recorded by the acquisition system were elaborated to obtain the material flow curves, following Eq. 1 and 2: (1) (2) where s is theflow stress, e the equivalent strain, h0the initial specimen height and s the testing machine stroke.
The data about the material flow curves for the highest and the lowest testing temperatures are shown in Fig.2.
Fig.7 – Shifting of the load peak as a function of thickness reduction.
Fig.8 - Cartesian force components as a function of feed ratio and thickness reduction.

Internal defects of the concrete specimen were simulated and compressive strength reduction mechanism was studied.
From this study, it is shown that numerical analysis can effectively simulate internal defects and provide an efficient way to study compressive strength reduction.
Among the defect conditions considered in this paper, for the influence of weak layer direction, the maximum reduction in standard compressive strength is 61.8%; for the influence of weak layer thickness, the maximum reduction in standard compressive strength is 73.1%; for the influence of weak area, the maximum reduction in standard compressive strength is 89.9%.
Use result for every defect condition included in this research as a data point for the random event, the average value of compressive strength is -3.831MPa, standard deviation s=2.184MPa.

The engine data was implemented in the engine model developed in GSP (Fig. 1).
Assessment of deterioration effect on the turbofan was done according to information and data from the references.
The simulation result approximately matches the data given in Frith [4] and also Ioannis [7].
Comparison between GSP data with reference data (Frith’s and Ioannis’s data) ranges from 0.59% to 8.71% as shown in Table 4.
The result shows similar data trend and thus the obtained GSP result are well validated.

Finally, by measuring the process parameters in rolling production site and applying the optimized rolling schedule to the rolling production, many test data are obtained.
Before the experiment, post strain gauge on the foothold of the mill, then connect the data wire with test instrument, and record test data by testing software in the rolling process.
Use probe and instrument on hand to measure the temperature of aluminum plate, then record the data on handbook.
In width direction, plate thickness data shown in Tab.3 were get by measuring the thickness every 15mm, then draw the crown curve, comparison of plate crown between curve calculated with crown model and curve drawn from thickness data above is shown in Fig.7, the difference is very little, and the most difference is 8μm.
The optimum reduction of economization on energy.

Meanwhile, the statistical data of regional pavement diseases collected by the department of highway conservation and management are enormous.
The assumed value of affecting factor after pretreatment for disease data corresponds to the attribute value of related condition attribute.
Limited by the quantity and types of statistical data, the paper only conducts a simplified numerical example without checking on believe degree.
Therefore, the reasonability and applicability of the model should be checked on larger scale practical data and believe degree level.
Considering the incompleteness of statistical data, the rough set theory of incomplete information sysytem can be used to deal with the gathered data to objectively determine the disease genesis.