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The mainly reasons for the scaling are the incompatibility between Cacl2 killing fluid and formation water as well as the pressure reduction around the wells.
According to the actual production data of Songnan gas field, the paper carries on research on the regular pattern of scaling in gas field to analyze and find the main cause of the scaling in gas well of Songnan.
From the data in table 2 can be seen, the main component of the scale is calcium carbonate.
Table2 The analysis results of the scale in Songnan gas well Sampling point Component analysis The proportion Scale on the drain tank vale from the gas treatment station CaCO3 ＞95% MgO little Scale on the wellbore from YS101 （Mg0.03Ca0.97）CO3 ＞98% SiO2 ＜1% The scaling study on different gas wellbore Table 3 is the produced water quality data from different gas wells.
Table 3 The water quality analysis data in different gas wells Well Number Cation(mg/L) Negative Ion（mg/L） Mineralization of Water(mg/L) Water Type pH K++Na+ Ca2+ Mg2+ Cl- SO42- HCO3- CO32- YP11 9725.91 8.86 0 3949.88 661.99 16347.31 911.6 31605.55 NaHCO3 7.49 YS101 1965.3 180.64 0 1078.69 83.69 3498.47 0 6806.79 NaHCO3 7.85 YP7 7034.16 4.43 0 5098.3 635.61 7752.33 662.98 21187.81 NaHCO3 7.75 YP8 1665.75 35.43 0 104.4 107.08 4213.22 0 6125.88 NaHCO3 6.73 YS1 1237.43 819.29 0 3219.06 187.88 100.65 0 5463.66 CaCl2 4.15 YP3 2528.05 4.43 0 1026.62 120.58 4803.08 0 8482.76 NaHCO3 7.01 Fig.2 The amount of scale in different gas wells and temperature It can be seen from the experimental resul in Fig.2, the amount of the scale has big difference in different gas wells.

The XRD data allows to determine the crystal structure and the values of the lattice parameters of the samples, and to estimate the values of the mean crystallite size, (nm), together with of the lattice microstrain, <ε> (%), through the Williamson–Hall method [13].
The dotted line represents an exponential fit of the data.
The dotted line represents a linear fit of the data.
These values are in good agreement with the data reported for the ball-miled Fe60Al40 [29] and Fe56Al24Si20 [9], respectively.
The SEM images suggest that the addition of Si stimulate the reduction of the powder particle size.

LHS method is informed as an improved method which can overcome the disadvantage of wind power simulation method of the most commonly used - the monte carlo method which needs a lot of statistical data.
so this article will choose the goal that has a minimum amount wind abandoned by wind power, In addition, we selected several other objective functions, such as system power minimal consumption and power generation minimal emissions, as a objective functions of co-generation system performance scheduling, the objective function as follows: (1)The minimum amount of wind abandoned by wind power objective function: （P1） （8） Where,-represents the minimum amount of abandoned wind objective function with the scene S; - represents available power output of wind power with the scene S; (2)The minimum cost of generating energy objective function （P1） （9） Where, represents the minimum cost of electricity with the scene S, represents thermal power generation it’s output at time t with the scene S, represents a set of thermal power units,,and-represents power consumption coefficients obtained from historical data
(3) The minimum generating emissions objective function （10） Where, represents the minimum generating emissions objective function with the scene S; , and represent power emission coefficients, obtained from historical data of units. 3 Conclusions We build a two-stage fire and water turbine scheduling optimization model based on power generation performance, what’s more, we use LHS methods to reduce the number of samples and Kantorovich distance cuts the scene to facilitate the practical application.
Research on bi-objective scheduling optimization model and method based on the energy conservation and emission reduction [J].

Benefit is direct cooperation without requiring import and export data.
Several glass manufacturers provide optical data to the Windows operating system and Daylight Group of the Lawrence Berkeley National Laboratory [1].
Annual weather files contain typical environmental conditions for a particular site based on several years of measured data.
A common file format is the US Department of Energy’s EnergyPlus weather data format (*.EPW).
The Weather Data File configuration dialog allows select the range time period want to use to calculations.

The result shows that the method can solve the multi-objective optimal load distribution problem accurately and quickly, and get the good effect in energy conservation and emissions reduction.
The result shows that the proposed model and algorithm is reliable and efficient and get the good effect in energy conservation and emissions reduction.
A case analysis The field data of power plant.
Eliminating abnormal points through the method of data preprocess, and use the clean data curve fitting based on the least square method.
Tab. 3 The comparation of two algorithm 　 the average coal consumption of 4 units/(g/(kW•h)) 4 units' average emission concentration of SO2 /(mg/m3) 4 units' average emission concentration of NOx/(mg/m3) the average pollutant emission of 4 units/(mg/m3) original data 313.51 471.56 210.74 682.29 results of PSO 307.85 407.78 136.50 544.28 results of GA 308.08 405.03 146.04 551.08 Fig. 2 The comparation of Fig. 3 The comparation of Fig. 4 The comparation of each unit’s power supply each unit’s SO2 emission each unit’s NOx emission coal consumption concentration concentration From the data in table 3 it is clearly observed that, from the point of economics, the integrated power supply coal consumption of 4 units reduced 6.47g/(kW·h)through the optimization use the genetic algorithm(GA), and it reduced 6.7g/(kW·h) through the optimization

The novel strains grew autotrophically with sulfur (data not shown), ferrous iron and pyrite as substrates.
The strains in pyrite cultures were confirmed with a test of the primers expected to be specific for each strain (Fig. 2, data shown for the novel strains).
In contrast to Am. ferrooxidans, neither of the novel strains required yeast extract (Fig. 3B) and neither of them grew significantly with yeast extract as the sole substrate (data not shown).
The RuBisCOs of all three strains used here appear to have been obtained by lateral gene transfer from acidithiobacilli [7, and data not shown].
A similar reduction occurred at 47°C during lithoheterotrophic growth (with yeast extract) of Sulfobacillus thermosulfidooxidans (compared to CO2-supplemented autotrophic growth), although the reduction in the initial rate of solubilization was not seen in this case (unpublished data).

Fig. 1 Orientation of Model Buildings Methodology Research Design The study involve carrying out experimental tests on model buildings and the collection and analysis of spatial and temporal morphological data on the buildings with emphasis on indoor and outdoor environmental conditions and spaces [7].
Types and Sources of Data The study relied on two types of data, namely: i) Primary data physically obtained from the model buildings (comfort factors); ii) secondary data such as thermal comfort indices, insulating properties of materials, etc, obtained from design manuals, charts, standards, etc.
Data Presentation and Analysis In Tables 1 and 2 are shown the average values of data used in the calculation of the factor φcl and φco for the two model buildings.
Most importantly, the study shows that the orientation of buildings in the warm humid zone has significant effect in the reduction of energy consumption in buildings.
[4] Callender, J.N: Time Saver Standards For Architectural Design Data.

The data reduction to calculate the energy release rate followed the derivation of dC/da function of the Irwin-Kies equation, Eq. (2), in which the equation of the compliance change as a function of the crack increment can be calculate according to Eq. (3)
Other forms of scattering that were noticed during data reduction is the effective maximum and minimum loads for crack propagation, due to decreasing loading with increasing number of cycles until a crack increment is detectable which makes the measurements difficult.
Crack growth rate of experimental data on mode I openning.
The coefficients on Table 1 were empirical determined using the experimental data in Fig.2.
For CP2 and CP4 scatter of data overestimated the coefficient m.

The system adopts traditional three-level architecture: data layer, support layer and functional layer (Fig.1).
Fig.1 System architecture Remote dynamic data collection platform.
Microsoft SQL Server is used for data management, supporting the subsequent analysis and processing in other platforms.
Fig.2 On-line data acquisition Fig.3 Real-time and historical monitoring data Risk source information management platform.
Normal data management operation like adding, modification deletion and inquiry are realized.

Incremental Neural Network (IncNet) structure is controlled by the growth and pruning, and the complexity of the match and training data.
To solve this problem, several data sets collected and classified by psychologists.
This paper considers two of these data sets, the first of 27 classes, the second has 28 classes.
The data set includes data 1027 and 1167, for example, 27 and 28 respectively, the class data set.
Ontogenic neural networks and their applications to classification of medical data.