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To provide a theoretical guidance for the application of selective non-catalytic reduction (SNCR) in a large capacity utility boiler, numerical study of SNCR process in a 600 MW utility boiler was performed based on computational fluid dynamics (CFD) code Fluent.
Good agreement of the calculation results with the industrial test data confirms the reliability of the calculation model.
Introduction Selective non-catalytic reduction (SNCR) is a well known flue gas denitrification technology.
According to literature [14] and the design data of NO removal system, the maximum droplet diameter is supposed to be 300 μm and the minimum to be 50 μm, with average size 200 μm.
In: Effects of Gaseous Additives for Selective Non-catalytic Reduction of NOx.

The main focus of this work was to fabricate Pt/porous n-Si gas sensors with a significant reduction in their breakdown voltages, Vbr.
An increase in electric field can cause a major reduction in the breakdown voltage Vbr.
Our experimental data shows that this current density is not adequate to increase the pores' depth significantly.
The comparison of breakdown data is provided in Tables 1.
As shown in this table the reductions in the breakdown voltages of our sample are significant.

The flight test data and helicopter characteristics are taken from Ref. [[] R.
The power required of current analysis fits well with the flight test data, implying that this model is sufficient to predict the power required.
The flight data of SA349/2 helicopter is used here to correlate with the load analysis.
The details of blade structural properties, static airfoil data and rotor geometry characteristics can be found in Ref. [[] R.
The first ten harmonics of moment in Fig. 3 shows a close agreement between the present analysis and the flight test data.

This paper presents a new methodology for obtaining singleton fuzzy model from experimental data.
This paper presents a simple method for obtaining interpretable fuzzy models from data.
The methodology used in this article to get fuzzy model from experimental data is structured as follows.
This data set contains 699 instances (patterns) with 683 complete data and 16 samples with missing attributes.
Zurada, Data-Driven Linguistic Modeling Using Relational Fuzzy Rules, IEEE Trans.

One of a challenging problem is how to deal with the data which shown the characteristics of high-dimensional, non-linear and multi-faceted nature.
How to effectively determine the appropriate neighborhood parameters are of great significance to obtain the correct low-dimensional structures for data dimensionality reduction and classification.
Application experiment analysis In order to verify the effectiveness of the proposed approach, we use standard data set of UCI Iris data [4] (sample points N = 150, dimension D = 4), making experimental analysis of mapping error.
The experiment shows that the category labels in the data handling to obtain the optimal adaptive neighborhood parameters, to benefit the purpose of data classification.
Laplacian eigenmaps for dimensionality reduction and data representation.

Training data and test data of BP neural network had been reduced by rough set.
Training data and test data of BP neural network had been reduced by rough set.
Discretization data sample are shown in table 2, and Testing Data sample are shown in table 3 [9].
Training model was built with BP neural network, and data sample in table 1 was used as training data, and data sample in table 3 was used as testing data.
Rough set can simply data sample and it can save storage space, and it can avoid of data explosion.

Sonmez[2 ]use a new empirical equations to estimate the strength of rock masses of varying hardness with the data of five groups of slope failures and four sets of uniaxial compressive strength.
Strength reduction method is adopted to reduce the strength parameter for stability safety factor.
Key control point displacement Variation curve with the strength reduction factor k Reducing the strength parameter and with the reduction factor k gradually.
key control point displacement of slope surface Control point elevation Strength reduction factor K [mm] 1.0 1.2 1.4 1.6 1.8 655.5m displacement 5.31 5.29 5.27 5.30 5.68 678.6m displacement 4.22 4.30 4.61 5.76 9.65 693.6m displacement 3.49 3.68 4.29 5.92 10.08 708.6m displacement 2.48 2.72 3.42 5.14 9.14 723.6m displacement 1.84 2.10 2.81 4.43 8.10 738.6m displacement 1.18 1.34 1.80 3.00 6.05 Table 5 is the data of key control point displacement of slope surface with the different location elevation and the figure6 is the relative Variation curve with the strength reduction factor k. according to the fig6, the change of displacement in the different elevation is basically gentle in the range of strength reduction factor k 1 to 1.8.
Strength reduction method is adopted to reduce the strength parameter for stability safety factor.

Theoretical analysis based on the measured data of sandstone samples shows that the real COPL will be obviously underestimated if the COPL computed by the traditional point-count method is far below CEPL and dissolution processes generated the additional porosity.
The correct understanding of the contribution of diagenetic processes to porosity reduction and permeability retention is quite important for the prediction and evaluation of reservior quality [1-6].
Accordingly, they could quantificationally evaluate the relative importance of the cementation and compactional processes to the reduction of the original porosity of hydrocarbon reserviors [5-6].
Based on the measured data of sandstone samples from [5], the deviation in COPL resulting from a DIPL of -5.0% ranges from 0.75% to 2.10%, which is far below that for CEPL (i.e. 0.4~1.6%) (Table 1).
All sample data come from [5].

As the data source of data mining, the data must be huge, contains noise, fuzzy and incomplete.
But in general, data mining includes what the next few process [4]: Data Preparation: the data which data mining will be deal with from different data sources, and it has large volumes of data, complex structure, which mixed with the vacancy data, noise data and redundant data.
Data selection: some of the data in the data source doesn't make any sense to build model and discover patterns.
Data mining: using a variety of data mining methods to analyze the related data.
And studies have shown that when a 5% reduction in the loss of customers, the average value of each customer can increase by more than 25% -100% [9].

In which, building Time period, Response Reduction factor and Importance factor alters design base shear majorly.
Pushover analyses are carried out to find its effects on over strength factor and response reduction factor.
This equations comprosies of Zone Factor (Z), Importance factor (I), Response Reduction factor (R), Spectral acceleration based on building fundamental time period (Sa/g) and total gravity weight of the structure (1) Many studies have reported the appropriate values for Zone factor, Response Reduction factor and Spectral Acceleration based on building fundamental time period.
In other words, it is a force reduction factor used to reduce the linear elastic response spectra to the inelastic response spectra.
Push over curves are idealised to bilinear curves and corresponding data are caluclated as per procedure explained above.