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The results indicate that the model is in good agreement with the experimental data and the dosage of chlorinating agent and roasting temperature play a key role in improving the volatilization ratio.
Numerous methods such as conventional flotation, acid leaching, magnetizing roasting-magnetic separation and direct reduction roasting, etc., have been theoretically and experimentally investigated to recovery or remove the non-ferrous impurities from pyrite cinder [4, 5].
Table.2 Central Composite Design Arrangement and Results Variables Experimental Y, % λ1, % λ2, °C λ3, min 0.80 1100.00 30.00 29.87 2.50 1000.00 10.00 68.97 2.50 1200.00 10.00 69.70 2.50 1200.00 50.00 69.78 2.50 1000.00 50.00 68.15 5.00 1100.00 30.00 97.05 5.00 1100.00 30.00 97.10 5.00 1100.00 30.00 97.12 5.00 1100.00 30.00 97.18 5.00 1100.00 30.00 96.98 5.00 1100.00 30.00 96.97 5.00 1268.18 30.00 96.55 5.00 1100.00 3.64 90.13 5.00 931.82 30.00 80.33 5.00 1100.00 63.64 98.10 7.50 1000.00 10.00 87.55 7.50 1200.00 10.00 96.31 7.50 1000.00 50.00 90.45 7.50 1200.00 50.00 96.78 9.20 1100.00 30.00 98.67 The experimental data and the response surfaces were analyzed using the Design-Expert software (version 7.1.5) from Stat-Ease Inc., USA.

Additionally, the test results are compared with the recommended data of international general standards, such as BS 7608 and DNV C203, by which the fatigue safety of pipes could be evaluated.
Therefore, this study is not only beneficial to the accumulation of full-scale fatigue data of pipes, but also can provide quantitative basis for evaluating the fatigue life and ensuring the safety operation cycles of pipes.
The control unit is a computerized data acquisition and closed-loop control system and required to storage, read and write the testing data in real time.
The crack-like defects could grow in the subsequent service process, which results in the reduction of loading capacity and the rigidity of the structure as well as the change of the resonance frequency of pipes.
Therefore, this study is not only beneficial to the accumulation of full-scale fatigue data of pipes, but also can provide quantitative basis for evaluating the fatigue life and ensuring the safety operation cycles of pipes.

The hardness of three surfaces data were collected experimentally from various process times, i.e. 140 minutes, 280 minutes and 560 minutes.
Lagrange polynomial interpolation was then used to generate quadratic mathematical formula of the surface hardness based on experimental data.
Therefore, this current research tries to formulate the mathematical equation prediction based on data results obtained from the ion implantation experiment.
Basically Lagrange give a set of k + 1 data points, (x0,y0), … , (xj,yj), … , (xk,yk) where no two variables xj are the same.
For the input data are vector t=140,280,560T and vector H=88.97,125.51,130.2T, execution of this code yields c1 = -0.000581548, c2 = 0.505250000, and c3 = 29.633333333.

The processor can be applied for data processing and be programmed or learned, communicate with data acquisition system.
All measured data immediately are available on dedicated home page.
I/O Data input/ output modules from outside world.
Database server Data storage, also highly depends on application.
Used to transmit data, tune, maintain and upgrade the system.

The data acquisition system consists of a computer center with a data acquisition board, module channels, and connecting cables.
In order to better comprehend the experiment, a statistical analysis of the data obtained experimentally was introduced.
The resultant data are shown in Table 1.
For this reason, the value of R2 is also relevant: it shows that the average from these values is explained almost entirely, covering over 91% of the data.
Therefore, it is necessary that the vibration data be subsequently added to this work, evidencing and justifying the cutting power results.

From the HA and IP data, 4 parameters are deduced: 1.
The data shown is obtained from the top side of sample A.
Data represented stem from the backside of samples A and B.
The data in Figure 6 shows that the residual anisotropy (the unstressed data) changes in the 0...5% strain range, and stays constant at higher strains.
A fit to sin2θ as reflected by Eq. 4 appears to provide a good description of the data, leading to a significant data reduction by use of the fit parameters a0, containing the isotropic bias value, and a2, which represents the anisotropy.

As show in Fig. 2, the task of digitalization and data processing subsystem is to measure and planning, measure and data conversion.
The main content of data pre-processing includes the elimination of noise signal for measurement data ,probe radius compensation, data-border processing, data blocks splice, data vacancy filling & re-sampling, and data partitioning content.
Data conversion interface is used to transform the measurement data points directly into NC code and STL files.
The first aim is to avoid the massive cloud of data points could bring difficulties to the follow-up operation and treatment; secondly, it is in order to balance the contradictions between reduction in the data amount and the decline in descriptive accuracy.
TRACECUT also adds the optional data interface (VDA-FS, IGES, etc.), generating CAD data of surface information.

Data layer mainly offers data service.
Fig.3 Data flow diagram of GIS application system Spatial Data Structure.
The separate management mechanism of spatial data and attribute data is adopted in the system.
Data involved in the GIS system include graphics vector data, spatial attribute data and engineering management data.
Both spatial attribute data and engineering management data are stored by MS SQL Server2005.

And the acquired data is stored as point clouds[7].
The post-processing software is used to process the collection point cloud and image or convert the point cloud to other model data. 2.2 Point Cloud Data Processing In order to meet the requirements of 3D modeling in the underground geological park, the processing of discrete point clouds can be divided into three major steps: point cloud data preprocessing, point cloud data registration and point cloud data block[8, 9]. 1) Point Cloud Data Preprocessing This step contains noise reduction, interferential point removing(redundant points for cars, people, mechanical equipment and so on) and resampling.
However, the data from each station is independent.
Then we can get the complete topography of the underground geological park point cloud data under a unified coordinate system. 3) Point Cloud Data Segmentation The whole point cloud data after registration is very large.
Single Site Point Cloud Data Preprocessing Point Cloud Data Registration Coordinate Conversion of Point Cloud Data Block Triangular Grid Construction and 3D Modeling Model Optimization Incomplete Data Processing Model Simplification Model Merging Point Cloud Data Segmentation Control Point Data Fig.2 Workflow of 3D Modeling based on Terrestrial Laser Scanner 3 Experiment on Underground geological 3D modeling 3.1 Data Description This paper selects an underground geological park in Shenzhen as the experimental area.

Data memory for storing PLC procedures for the implementation of the intermediate state and information, equivalent to the " memory of computer ".
Implementation of PLC procedures required for the input and output image, internal relay timer counter state, middle state, data register, procedures PLC intermediate processing results are stored in the data register.
Implementation of PLC procedures required for the input and output image, internal relay timer counter state, middle state, data register, procedures PLC intermediate processing results are stored in the data register.
System hardware block diagram The communication interface of the main function is to realize PLC data exchange with external equipment.
Through the communication interface, PLC and programmer, man-machine interface, display connection, in order to achieve PLC data input output.