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The reduction of impurities and modification of layer structure to optimize the adsorbing ability has become an urgent discovery.
The integrated information from the surface area data with pore diameter and volume data means the pore formed in concave inward formation and widening inside surfaces.
The kinetic adsorption conformed to the PSO model supported by the data in Table 2 (according to the R2 value closer to 1).
Table 4 displayed and compared the recent work data in the maximum capacity adsorption of different adsorbents in adsorb the RB and MG dyes contaminant.
Table 5 displayed the data of thermodynamic adsorption, and it can be concluded that the interaction between RB and MG in the bentonite-based adsorbent occurred differently (dominated by chemisorption).

Figure 3 shows on the right-hand side the procedure for inserting the data generated from the 2D process model into the 3D shear test model.
To validate the methodological approach, the experimental data obtained in this study are compared with two different metamodels.
This deviation results from the inaccurate fitting of the respective metamodel in relation to the calculated data points of the FE models.
The calculated meta-models have no data points for the strains underestimated in this study and can only predict them.
The numerical prediction of the geometrical characteristics match the experimental data.

The solution is to generate NC data directly from a CAD model and transform the data uploading to control system of flexible manufacturing system.
There are two approaches to data exchange.
Another view of the integration is based on the exchange of data in terms of sharing data generated from CAD systems.
Geometric data contained lines, curves and vertex.
For integration are important direct production data, also other data, not necessarily directly made to the flexible production system.

Furthermore, the Multivariate- Partial Linear Squares Regression (M-PLSR) method is applied to regress the data and validate the LOD values.
From the data above, it is necessary to concern about the Cd contamination and it is crucial to accurately measure the Cd amount in soil and determine its Limit of Detection (LOD).
Meanwhile, 45 samples are randomly selected from all the samples as training data and 12 samples are used as test data.
The BP neural network is used to analyze the energy spectrum data under different LODs.
Samkar, Examination of Dimension Reduction Performances of PLSR and PCR Techniques in Data with Multicollinearity. 

This can be ascribed to the enhancement of the surface energy, which was caused by the reduction of the crystal grain size of TiO2.
The contact angle was tested by German Data Physics OCA 20 contact angle test instrumentation.

Through processing experimental data, to get the role water reducer played in conditions of different water cement ratio.
concrete strength grade is lower than 60.0MPa, the relationship between the strength of concrete and cement water ratio can be expressed as: fcu = αa fce (C/W– αb); fcu=fh=Afce（C/W-B） (15) αa,αb-regression coefficients; fce-28d cement compressive strength measured values（MPa）； fcu-28d concrete mixing strength（MPa） Test results Table 6 With water reducer Without water reducer group fce C/W fcu fcu/ fce group fce C/W fcu fcu/ fce A1 36.725 1.3474 26.04667 0.801436 A1 36.725 1.3474 29.1765 0.897728 B1 36.725 1.6434 31.13541 0.958013 B1 36.725 1.6434 35.372 1.088369 C1 36.725 2.0367 41.44267 1.275159 C1 36.725 2.0367 45.0924 1.387469 D1 36.725 2.3327 48.1763 1.482348 D1 36.725 2.3327 51.10637 1.572504 By (15) transforming: =αa; Let a=,b=; We can gain the following chart data
This experiment builds the relational expression of concrete strength and cement water ratio based on the data and chart, which are helpful to determine the water cement ratio by the strength of concrete accurately.
This is very convenient for practical engineering to effectively avoid the reduction of concrete strength in the construction process for too large or small water cement ratio, or difficulties in construction.

Construction of the regression equation should be carried out with the preliminary classification of data on the quantities of the active component of the film impedance of mercury and an alkaline electrolyte.
Introduction Modern energetic suffers significant loss caused by reduction of reliability and service life of power plants heat- mechanic equipment due to construction materials surfaces corrosion destruction [1].Passivating film on the surface of metals and alloys are one of the most important factors that determine the kinetics of corrosion processes [2-7].
The results of calculation by the above equations, with the experimental data with an accuracy of no more than 6% for the first equation and and not more than 0.5% for the second equation.
Construction of the regression equation should be carried out withthe preliminary classification of data on the quantities of the active component of the film impedance of mercury and an alkaline electrolyte.

The process logic control system manage the process data and the business logic control system manage the business data, together they provide data interface for the workflow engine and then the workflow engine packages the process data and business data in order to pre-control and tracking the working process.
The business data processing system take the responsibility of the business data statistic and analysis and provide the processing results to the data service component in order to show the graphical results to the user.
We use SQL SERVER to store data.
Data & Knowledge Engineering, 2004, 50(1):87-115
Analyzing process models using graph reduction techniques [J].

In the present study, size of nano particles, temperature and concentration are used as input data for the network and thermal conductivity ratio of the nanofluids are the output of the model. 75% of the data are used for training and remained data were employed for validation and test.
Coefficient of correlation for overall data is 0.99293 which shows high accuracy of the proposed model.
Obatined results for correlation of coefficent In figures 2 and 3, the differences between predicted data by the model and experimental data for various data are represented.
Differences between predicted data and experimental data Figure 3.
Conclusion Perceptron neural network with 10 neurons is applied in the present study to model thermal conductivity ratio of CuO/Ethylene glycol by using temperature, size of nanoparticles, and concentration as input data. 75% of data are used for training and the remained data are utilized for validation and test.

Temperature distribution has been measured by use of the installed laboratory equipment and respective data recorded has been presented in the form of graphic charts.
Data transmission has been realized by use of Advantech PCL 818 HG multiplex board and PCI inboard.
Datas which it has been taken from thermocouples are gathered and listed in Microsoft Excel programme.
Fig. 3 and Fig. 4 respectively indicate temperature variations occurring in cold rolled samples of AA5454-O material where tensions have taken place on account of temperature difference arising during deformation, based on data obtained from thermocouples that were placed into the workpiece (along L0 = 150 mm).
Temperature variations in the case of hot rolling (500 °C) process, as dependent on number of roll passes and time According to these graphics that were plotted on the basis of data collected by means of the measuring boards, a certain correlative proportion is existent between the number of roll passes and the decrease in the temperature of the sample.