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In the case, that the proposed mechanism is correct and corresponds to the experimental data set, it is possible to use the relation established in Eq. 2 for further calculations e.g. loss of calcium ions.
Because to the heterogeneity of concrete and uncertainties in the input data, it is appropriate to determine the kinetics of the reaction using additive properties, for example the change of pH over time, or with use of chemical analysis methods as flame atomic absorption spectroscopy (AAS) and determine the concentration of components (Ca2+, Fe3+, Al3+) in the aggressive solution.
The result of the chemical deterioration process is from this viewpoint primarily the reduction of compressive strength as outlined below.
At the basis of the kinetics of chemical reaction (Eq. 1) (experimental data are required) the amount of dissolved Ca2+ ions can be determined.
As a comparative data, some cubes are stored separately in aggressive solutions of different pH.

The improvement emphasizes on the reduction of slurry viscosity, increase of anti-channeling ability and simplification of field construction.
According to the experiment data, 1.5% BOND expanding agent is added in the cement slurry formulation.
There are two basic principles of determining field centralizer position: on one hand, reasonable position and quantity of centralizer should be designed according to field data; on the other hand, emphasis should be stressed on the supervision of positioning casing centralizer in the field.
In this case, the position and quantity of casing centralizer should be recalculated according to the field data of drafting process.
Field Application Based on the analysis of experiment data and field practice, the synthesize technology for improving the quality of shale gas well cementing is developed.

Table 2 list testing result data of mechanical property of developed steels Y20Sn and the requirement of National Standard automatic steel Y20.[5] Comparing the testing result data of Y20Sn with requirement of Y20, it can be seen that tensile strength of Y20Sn steels can reach the requirement of Y20 Standard, and their extensibility and percentage reduction of area are higher than the requirement of Y20 Standard even.
Table 2 Compare of the testing result data of mechanical property of Y20Sn and the standard requirement of Y20 Heat number Steel Type Diameter (mm) Tensile strength (MPa) Extensibility (%) Reduction of area, (%) Hardness (HB) 1 Y20Sn 5.990 483 29.33 55.41 132 2 6.010 479 28.80 58.75 138 3 6.008 432 28.42 58.64 136 GB8731－88 Y20 － 450～600 20 30 ≦175 Testing Results of The Machinability.
Under the data of wear curve of cutting tool, wear T( tool life time) - V( the velocity of cutting) curve can be deduced, which is shown by Fig. 6.

Both groups of methods suffer from the selection of parameters which are most sensitive to damage and the requirement of sufficient measured data for the identification.
A given set of time domain data is decomposed into a finite number of interpretable components according to their respective singular values.
Local damages of 10% reduction in the elemental stiffness of Elements 8 and 20 are assumed.
The time duration of measured data lasts for 1.0 second and the sampling frequency is 500 Hz.
Isezaki, Principal component analysis and singular spectrum analysis of ULF geomagnetic data associated with earthquakes.

Figure 2: Integration of Energy planning in the factory life cycle Strategic planning processes pursue the goal of an overall reduction of energy use.
In this case the missing energy data of plants are the greatest challenge.
While analyzing the limitations of production systems, specific data is gathered and related to their energy states, representing the energy demand.
For volume, personal and machine flexibility, plant specific data, such as the energy state, the production in- and output, is collected.
As a consequence of the flexibility character, dimensions and their components like the activation time, deactivation time, the duration etc. have to be measured and connected to the material flow plant’s specific data.

General methods have been used for the treatment of wastewaters containing lead metal such as chemical precipitation, membrane separation, flotation, bio-sorption, electrochemical reduction, ion exchange and adsorption [3].
The obtained data were then evaluated by modified pseudo-second order of Ho’s (Eq. 1).
The obtained data were evaluated by Langmuir isotherm adsorption model (Eq. 2) to determine adsorption capacity (b) and equilibrium constant (K).
The data from this experiment has been used to evaluate the adsorption isotherm model of Pb(II) on AB and DIB using different models.
Application of the data using various adsorption isotherm models has revealed that the adsorption of Pb(II) ions by AB and DIB is best described by the Langmuir isotherm model as shown in Table 2.

This situation results directly from a dearth of reliable experimental data due to the difficulties inherent in simulating hot-rolling conditions in the laboratory and in accurately quantifying precipitation states.
The aim of the present work is firstly to extend the data on strain induced precipitation using a model microalloyed steel and secondly to compare different mechanical tests operating in different deformation modes.
With respect to the amount of niobium precipitated, while the general trends are correct, i.e. the %Nb precipitated increases with the holding time, there is some dispersion in the data obtained with the different simulators.
This means that for precipitation to stop recrystallization a reduction of the dislocation density through recovery processes is required.
In this case a precipitation start time shorter than 1s was reported, while our data indicates a Ps value around 30s for the present steel.

The results showed that: in the 6 different plant of SF and SSF constructed wetlands for TN removal efficiency decreased as the concentration increased, different plant, different levels of treatment rate reduction, and removal value of SSF was lower than SF.
Using A potassium sulfate eliminate alkaline-ultraviolet spectrophotometry to test TN. 3）pilot program: an experiment with 9 days when the experiment 3 days, the concentration of 3 days, the data for 3 days.
Also, from Fig.2 and Fig.3 shows the six plants SF and SSF constructed wetland forTN removal efficiency decreased as the concentration increased, different plant different data removal rate.
Whether the SF or SSF constructed wetlands, Reed wetland sewage removal reduces the maximum, and minimum was Canna. reed SF constructed wetland removal down from 75.56% to 54.7%, dropped 20.86%, SSF constructed wetland removal efficiency dropped from 76.67%to 58.47%, dropped 18.2%, the average down 19.53%; Canna sf constructed wetland to removal rate dropped from 67.91%to 50.86%,decreased 17.07%; the ssf removal rate dropped from 69.93%to 53.99%, decreased 15.94% on average 16.51% reduction.

Data processing path The objective of the data processing is to convert the image acquired by the optical detector into a set of points containing x,y,z co-ordinates.
Therefore the matrix is essential to convert image data to shape data.
The shape data is stored on the internal hard drive for future analysis.
The shape data is stored on a common hard drive.
In this mode only 2D data from the images would be acquired.

SET 1 SET 2 SET 3 Units Initial Optimal Initial Optimal Initial Optimal Fcost 0.1519 0.0167 0.1840 0.0192 0.5231 0.0163 μm2 Fcost reduction - -89.0% - -89.6% - -96.9% - Avg.
First ANN trials to predict wear were not very conclusive, mostly due to the lack of learning data.
The ANN uses (i) 2 hidden layers of neurons defined by a total of 20 neurons, using selu and sigmoid activation functions, and (ii) recurrent architecture to account for the cumulative wear calculated from previous strips as input data.
Nevertheless, this work is a first shot at ANN wear models and additional data could greatly improve their performances, by reducing the overfitting for instance.
On the other side, ANN have important potential for bigger training database and additional data which, assorted with bigger ANN structure, could lead to much better roll wear predictions.