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A study on the refining of nickel sulfate solution from sulfuric acid leached solution of spent lithium ion battery and fabrication of nickel nanopowders from the nickel sulfate solution was investigated. the nickel sulfate solution with high purity(>99.9%) was refined by precipitation method and solvent extraction method. the nickel nanopowders were synthesized by liquid phase reduction method with hydrazine and sodium hydroxide. the purity of nickel sulfate solution and nickel nanopowders were measured by EDTA(ethylenediaminetetraacetic) titration method with ICP-OES (inductively coupled plasma optical emission spectrometer). morphology, particle size and crystal structure of the nickel nanopowder was observed using transmission electron microscopy and x-ray diffraction spectroscopy.
To carry out the liquid phase reduction, 400g of 30 wt% NaOH solution was added and maintained 70℃ of temperature and 400 rpm of stirring rate using hotplate and overhead stirrer.
Figure 4 shows The XRD pattern of the fabricated nickel nanopowder. from the data, the crystal structure of the nickel nanopowder was FCC[3].
Nickel nanopowder was fabricated by liquid phase reduction from the fabricated nickel sulfate solution.

However, sometimes DPIV images are overexposure, uneven distribution of the gray values, and obscurity on the edges of the particles, which will affect the accuracy of flow data by inversion.
In the different stages: boundary treatment, noise reduction, identification and elimination of the error vector, which are applicable to the processing of the DPIV information for getting the velocity vectors with higher resolution and precision through the continuous wavelet transform of velocity data.
These researches can be roughly divided into two groups: (1) identifying the turbulent or eddy structure by the wavelet analysis of experimental data and simulation data; (2) developing turbulence modeling and numerical methods using wavelet bases [1].
(1) Compute the wavelet coefficients of vector data based on the discrete wavelet transform
Analysis Results DPIV images may be overexposure, uneven distribution of the gray values, and obscurity on the edges of the particles, which will affect the accuracy of flow data by inversion.

In the research [7], a scientifically-substantiated sequence of procedures was created, with a detailed selection of equipment and test samples, in order to provide reliable experimental data when studying the temperature regime of a fire.
Nomogram for determining the coefficients of tensile strength (a) reduction, compressive strength (b) reduction and tensile modulus (c) reduction to design fire-resistant steel building structures at Am/V = 150 [m-1].
I-beam profile according to DSTU 8768:2018 Geometric characteristics of cross-sections Cross-section coefficient Am/V, [m-1] Resistance moment, Wx, [m3] Profile № 55 157.842 2000×10-6 Profile № 40 123.246 947×10-6 Profile № 27а 93.684 407×10-6 Using the data in tab. 2, we can determine the boundary moment using the formula [1]: Mc,Rd=Mpl,Rd=WplfyγM0
Fig. 9 shows that the obtained data can be used to build nomograms to determine the corresponding beam boundary moment.
Analyzing the data shown in fig. 11, it can be observed that in almost all the area of the possible values of the opening coefficients and fire load density, the time of reaching the boundary state of the loss of load-bearing capacity is much greater for the temperature regimes determined by the proposed mathematical models than the values obtained using the standard temperature regime of fire.

The value of the numerical prefactor was determined by fitting Eq. 1 to the strength data of isotropic foams, for which R=1 and f1=1, resulting in C=0.3 for plastic foams [12].
Using the data presented in [4], the reinforcement efficiency factor Γ was evaluated by Eq. 5 and plotted in Fig. 1 as a function of CNC volume fraction νc in the monolithic PU.
The presence of the filler led to a reduction in the cell size without apparent changes in their geometrical anisotropy, as in [4].
It is seen in Fig. 3 that most of strength data are close to the prediction employing the values of ηo based on Eqs. 9 and 10.
Reinforcement efficiency factor of strength of nanocomposite PU foams vs CNF volume fraction as derived from experimental data [8] (markers) and predicted by Eq. 5 (lines) Summary With the aim to separate the nanofiller reinforcement effect from the foam strength changes caused solely by variation in foam density, a reinforcement efficiency factor of foam strength is introduced.

Also to determine the depth of penetration of the DG has been developed 2 types of scenarios Scenario 1: Level of implementation of the DG is growing due to the reduction of active power at the terminals of the TT from 3.33% to 33.33% and introduction WG in each load node covers reduction capacity. 
The data obtained during the execution of scripts in the table.
Reduction power produced covered DG (with constant load) Fig. 3 Maximum rotor speed deviation and oscillation duration when the penetration level of DG is simulated according to Scenario II, and a fault is simulated in all possible branches Fig. 4 Maximum rotor speed deviation and oscillation duration of DG implementation in the test system when the penetration level of DG is simulated according to Scenario III and a fault is simulated in all possible branches.
Load growth covered the introduction of the DG (constant centralized generation) or an increase in capacity DG). 2. 2 reduction power produced CG covered by DG (with constant load) It is shown that large power flows adversely affect the damping of the oscillations: the stronger the loaded line, the weaker the connection between the generator and load and the vacillations of the CG. 
Estimation of reduction depreciation charges in power network when DG source is connecting Advanced Materials Research Vols. 1008-1009 (2014) pp 818-822 [4] Reza, M., Slootweg, J.

In this study, data for processes and chemicals involved in petrochemical industry is used.
The data for every parameters was then analyzed for their mean values which is used as the k-value in Eq. (3).
Aside from mean values, the data was also analyzed in order to obtain the lowest as well as the highest parameter values.
As mentioned previously, all three equations that is Eq. (1), (2) and (3) is based on the data gathered.
In this study, data from the petrochemical industry is used.

Design of the Data Local-storage Circuit.
The stored real-time data can reach 1600 groups of data.
MAX232 controls the data input and output through R1I and T1OUT, and also the serial port is utilized for the data transmission between the system and the computer.
The remote GSM data transmission is implemented through the AT command.
The comparison on the actual measurement data and theoretical data under the 29.5℃ environment is shown in the table 1.

All monitored parameter data is scored in the datacenter of steam turbine cloud platform.
User can get all relative data based on the unique RFID tag of steam turbine.
Data collection module can get useful information by condition monitor.
Many sophisticated sensors and computerized components of condition monitor system are capable of delivering data about the machine’s status and performance.
Fig.4 Turbine fault service WSDL document The monitoring data were got from the steam turbine sensor and monitoring program.

The drag coefficient and the Strouhal number were calculated and compared with the existing experimental data.
Table 1 displays the discrepancy between the simulation and experimental data.
The experimental data fluctuates unstably with a descending trend which is in the contrary with simulated data.
Although having declining tendency, the simulated data is smooth without fluctuation throughout the subcritical regime.
This may be due to the insufficiency of the turbulence model itself or the less consistency of the experimental data.

Because the microgripper arm has a reduction of section in the compliance area, for mathematical modeling the beam will be considered with a constant section, by applying a correction factor that will take into account the effect of the compliance.
The bending deformation force was measured using a quartz crystal balance, and the deformation was measured with a Laser triangulation system Keyence LK-G3001PV (with a precision of 0.05 μm), which allows recording data by a specific software.
Experimental setup for determining the compliance effect The experimental results for the two section reduction (compliant) arms of the microgripper are shown in Fig. 5.
Force-deformation dependence on bending tests performed on section reduction arms The slope of the curve represents the value of the elastic constant.
In order to achieve the model of the compliance effect, tests were carried out on the arms with and without section reduction, from which a global elastic correction coefficient Kg, was determined.