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PNN input and output signals can be time-varying function or procedural data compared with traditional neural networks.
By detecting the above parameters, the value of the effluent BOD can conduct online real-time estimates. 1) Soft-sensing Model of Swage Disposal Process Based on BFB BFB process is a complex, dynamic and biochemical reactions, since water quality and water level, as well as microbial growth conditions are always changing, the effluent BOD is also a corresponding change in water quality indicators, and difficult-line real-time measurement[8].Using the process of neural networks to predict, according to reactions characteristics of effluent biochemical to select DO, NH3-N, TOC three auxiliary variables ,which can basically reflect the variation of BOD, and are not related and in line with soft-measuring principle.According to a specific BFB process, a reaction period is about 3 hours，the sampling period should be based on the data trend of an increase to reduce.
Sampled data obtained in the form of: (6) The input data is synthesized in the 5-order polynomial form in the sampling period , thus train sample set, so that can not only meet the accuracy requirements, but also simplify the calculations. 2) Pretreatment of Date and Selection of Orthogonal Function In order to simplify the complexity of the training process, select the triangle basis function, the number of basis functions is determined by the accuracy of function approximating and the generalization ability of the network.
Table 1 Part of the process neural network training data Group 1 2 3 …… 50 t(min) TOC DO NH3-N TOC DO NH3-N TOC DO NH3-N …… TOC DO NH3-N 0 524.6 12.3 8 456 10.3 7 521 5.6 6 531 6.3 4 30 456.6 10.3 8 386 9.5 6 462 4.5 5 469 4.9 3.5 60 400 8.5 6.5 324 6.4 5 400 4.2 3.9 394 3.5 2.7 90 365 6.8 6 300 5.1 4 320 3.5 3 300 3 2.2 120 300 5.1 4 265 3.8 3.2 200 2.5 1.6 242 2.6 1.6 150 210 3.1 2.5 168 2.5 2.1 153 2.5 1 158 2.1 1.2 180 125 2.1 1.5 75 1.9 1.5 75 2 1 75 1.5 1 BOD 32 33 34 …… 35 Table 2 Predicted results and the relative error Number Detection value of BOD Calculate value of BOD Relative error 1 46 47.5 3.2% 2 44 42 4.5% 3 36 36.8 2.2% 2 Fuzzy control design and simulation of BFB Fuzzy control is a kind of knowledge model based on fuzzy reasoning and regard control experience as control, which is widely used in intelligent control.
In view of this situation, put forward fuzzy control strategy, which take Dissolved Oxygen (DO) as control variable, and take error(e), between DO measurement values and given value, error rate(ec), as two input variables of a fuzzy controller, design the rules of the fuzzy control, realized the BFB optimization control, BFB fuzzy control realization diagram as shown in Fig.2: Fig.2 BFB fuzzy control realization diagram BFB sewage treatment fuzzy control rendering which take DO as control variables in Fig.3: Fig.3 Fuzzy control effect of BFB The control effect show that taking DO for control variable of fuzzy control method, with the optimization target for energy conservation and emission reduction, the effluent water quality continuously become stable and the system operating normally. 3 Conclusion In order to solve the nonlinear and time-varying problem in BFB sewage treatment process, designed the outlet water quality BOD soft measurement model based on PNN; At the

Table 4 The experimental data of recovery rate of rock fragments Medium R1 (%) R2 (%） R1/R2×100% Distilled water 35.4 34.6 Completion fluid 77.42 75.19 97.1 As is shown in the table,the rolling recovery completion fluid rate is greater than 75%,2 times of diatilled water,showed that the completion fluid has good effect of inhibition of clay hydration dispersion. 3.2 The test of statis expansion The experimental result of statis expansion is shown in table 5 Table 5 The experimental data of the test of statis expansion Immersion medium Different expansion time（h）of the 000 expansion rate（%） 0 1 3 5 10 20 Distilled water 0 5.2 13.7 15.6 19.6 21.2 Completion fluid 0 0.53 0.99 1.79 2.84 2.93 As is shown in the table, core inflation rate is less than 3%, and it is greatly reduced compared with distilled water expansion rate when using completion fluid immersion.
The result shows that the completion fluid has good capacity of inhibition of clay hydration expansion. 3.3 The test of core recovery Select Sanan oilfield three pieces of different permeability natural core ,then measure the changes of core permeability afer injecting completion fluid, data is shown in table 6.
Table 6 The experimental data of core flow Core number K1 （10-3μm2） K2（10-3μm2） Permeability reduction value （%） 1 0.49 0.37 24.5 2 53.2 42.3 20.5 3 182.5 151.2 17.2 Indicated through the core flow experiment, for the cores with different permeability, permeability values were less than 30%, core recovery rate is more than 70%,completion fluid has good efect of reservoir protection.
Single well daily oil production increased by 3.05t, rate of water content decreased by 10.3%, fluid production strenth and oil production strength is higher than adjacent wells by 0.17 m3/d.m and 0.20 m3/d.m, increased by 14.5% and 51.3% respectively, compared with adjacent wells. 4.2 Reducing skin factor Skin factor S calculated by well test data, includes any injury of the formation casuded from the drilling, completion ,perforating,injection,production,subsequent job and stimulation,it can reflect the extent of injury in experimental zone roundly.

The parameter N is determined by the statistical data of a specific channel locks.
The first example use the merging of three groups of data in [3], to applied the arranged algorithm to 22 waiting vessels, the utilization rate of effective area of the lock chamber(280 meters long and 32.8 meters wide) is 89.91%, the results are shown in Fig. 1 (the digital on figure represent the waiting vessel number).
Fig. 1 Lockage arrangement of three groups of data merging in [3] The second instance is the instance 2 given in [4].
Fig. 2 Lockage arrangement of data in [4] Conclusions Based on the analysis of the factors influencing of the ship lock navigation capacity, this paper from the angle of improving the once lockage tonnage to improve the navigation capacity of existing ship lock of the Three Gorges, establish ant colony optimization model for the ship lock arrangement, design and implement the corresponding algorithm.
[3] Sun Bo, Qi Huan, Zhang Xiaopan, etal: Dimensionality Reduction Quickly Arranging Algorithm of Lock Chambers in CoScheduling of Three Gorges Dam and Gezhouba Dam System(in Chinese), Computer Technology and Development, Vol. 16(2006), No. 12, pp. 207-212

Grey Relational Analysis The various steps involved in grey relational analysis for the multi-performance optimization [7] are as follows: · Data Preprocessing: Data preprocessing is performed on the experimental data of the performance characteristics to normalize their values between zero and one.
Based on the quality of the performance characteristic, normalization can be performed as follows: ‘Smaller is better’ performance characteristic equation (Tool wear and circularity ) (1) where xi(k) is the normalized data for the ith experiment of the kth performance characteristic (i = 1,2,…18 and k = 1,2,3). αi(k) is the experimental value for the ith experiment of the kth performance characteristic, and min αi(k) and max αi(k) are the smallest and largest of the experimental values of the kth performance characteristic respectively
Experimental Data Table 1 Results of the experiment conducted Feed Rate (mm/min) Spindle Speed (rpm) Lubrication type Circularity (µm) Tool wear (µm) 5 2000 Dry 51.4 15.67 10 2000 Air 43.7 10.98 15 2000 Wet 49.8 8.97 10 3500 Dry 44.4 13.47 15 3500 Air 40.87 12.45 5 3500 Wet 27.65 11.34 15 5000 Dry 43.4 14.96 5 5000 Air 22.55 16.08 10 5000 Wet 24.5 9.98 10 2000 Dry 54.85 12.45 15 2000 Air 54.9 9.88 5 2000 Wet 39.23 9.89 5 3500 Dry 34.45 15.89 10 3500 Air 35.67 10.98 15 3500 Wet 39.1 11.45 15 5000 Dry 43.4 13.98 5 5000 Air 22.45 15.98 10 5000 Wet 24.5 10.79 Table 2 Analysis of variance for circularity and tool wear Circularity Tool Wear Source DF F % Contrib.
Also, as palm oil is cheap and easily available, this means a reduction of costs for the manufacturing industries.

Data were processed in MATLAB, considering as independent parameters: the argon flow, the bubbling duration and the pressure of argon bubbling and as a dependent parameter: nitrogen removal efficiency.
The following are areas of regression equations obtained from data processing in MATLAB for double correlations.
Analyzing these results, several observations can be made. 1) Processing the same data to determine the triple correlation regarding the dependence between the nitrogen removal efficiency and the vacuuming parameters (duration of treatment, argon pressure and flow) as in the simple correlations allow a comparative analysis of the results; 2) Taking into account the triple correlation coefficient R=0.8952 and S=3.7127 deviation, I consider that this correlation expresses in a very efficient manner the correlation between the three parameters of steel treatment in the LF facility and nitrogen removal efficiency; 3) The regression hipersurface has the saddle point coordinates: Pb=4.4317; Db=554.6995; Tb=83.459 and ηN=37.6433, positioned in the technological fields. 4) The regression hipersurface expressed analytically through a polynomial function of the 2nd degree presented by equation (4), can not be represented graphically (in the space of four dimensions) by successive replacement
) The synthetic slag used for steel desulfurization and deoxidation has a positive influence on nitrogen removal, but given the values ​​of the correlation coefficients (data processed in EXCEL program), the influence is much less significant, to note that the process of nitrogen elimination was studied during treatment in LF, where it was intended that the slag to have characteristics that lead to an advanced deoxidation and desulphurisation. 3) The research conducted revealed that nitrogen removal efficiency can be controlled through the process parameters of bubbling the steel in the L.F. plant (duration, steel temperature, pressure and argon flow).
Drăgoi, Researches regarding the reduction of the gas content from the steel produced end treated on the EBT-FL technological flow, Ph.D thesis, Politehnica Publishing House, Timisoara, 2012; [2] S.

The volume of a transferred electrode metal droplet which has the shape of a spherical segment with the base equal to the electrode cross section can be determined by the formula [23]: (5) The active surface area of the molten electrode metal droplet of can be found by the formula [22]: (6) Using the experimental data (Table 1) of the droplet transfer parameters [4], we verify the obtained formulas 4-6.
Table 1 - Surfacing parameters Power source - rectifier Electrode type Average parameters values (oscillograph) AKIP-4122/1V Number of short circuits during surfacing Short circuit duration τk.z., ms diode LB-52U Current 89+2.7 А Voltage 20.8+0.6 В Estimated rate of welding 0.25 m/min 17 6.7 ± 1.85 inverter 22 5.36 ± 1.34 diode LEP UONI 13/55 Current 88+2.7 А Voltage 21.5+0.6 В Estimated rate of welding 0.29 m/min 17 6.5±2.1 inverter 22 6 ± 1.9 diode CL-11 Current 86 А Voltageе 24.5+0.6 В Estimated rate of welding 0.27 m/min 12 12 ± 3.8 inverter 24 8.1 ± 2.3 Data in Table 1 show that the droplet transfer time decreases and the number of short-circuits increases when using an inverter rectifier which means that the transferred electrode metal droplets are smaller.
Table 2 - Average estimated data on the mass and radius of transferred electrode metal droplets Power source - rectifier Electrode type τk.z., 10–3 s Droplet mass m, g Droplet radius, R, mm Droplet volume V, mm3 diode LB-52U 6.7 ± 1.85 0.099 ± 0.002 1.39 ± 0.026 6.89 ± 1.9 inverter 5.36 ± 1.34 0.052 ± 0/015 1.05 ± 0.01 4.36 ± 1.38 diode LEP UONI 13/55 6.5±2.1 0.091 ± 0.004 1.3 ± 0.03 6.5 ± 1.99 inverter 6 ± 1.9 0.071 ± 0.002 1.23 ± 0.02 5.66 ± 1.8 diode CL-11 12 ± 3.8 0.57 ± 0.04 2.5 ± 0.05 15.48 ± 4.9 inverter 8.1 ± 2.3 0.175 ± 0.05 1.8 ± 0.04 10.28 ± 2.9 Analysis of the data in Table 2 shows that the use of an inverter rectifier makes it possible to reduce the volume of a transferred electrode metal droplet by 9-37% which provides a more stable fine-droplet transfer, especially when using high-alloy electrodes.
To prove the calculation results presented in Table 2 we have analyzed images of high-speed filming (KOMPAS and VEGAS programs) (Figure 1) which certify the calculated data presented in Table 2.
Figure 1 - Kinogram frame of electrode metal transfer in MMA (inverter rectifier, CL-11 electrodes) Decrease in time of the droplet at the end of the electrode (Table 1) and reduction of the geometric dimensions of the transferred droplets (Table 2) reduces heat content of the electrode metal droplets.

A cross-section curve is generated by discrete data, and then a surface is generated by the cross-section curve according to certain rules.
Control points can be calculated by curve or surface data points, and then surface modeling can be completed.
The data format needs to be changed before discrete data are imported into SolidWorks software.
Firstly, control points can be calculated after data points of a blade section are imported into computer.
On the one hand, if the rotation angle is small, the electrode is easily deformed due to insufficient rigidity, and the electrode loss is increased because of the reduction of effective discharge reaction area.

Oxidation necessitates only 0.5 V, whereas reduction requires 7-20 V.
Comino et al. produced AgNWs by modifying a polyol approach that involved the reduction of a silver precursor (AgNO3) with EG as the reductant.
Despite its importance for multiple applications, previous research provides experimental data and explanation of how the parameters of nanowire network influence this NIR transmittance for understanding and suggestions for improved device design.
According to theory, varying microwave power have an effect on the reduction rate and seed growth, which results in products with variable morphologies.
Mass loss data from Thermogravimetric Analysis (TGA) showed in dry ice quenching medium have good thermal stability only 2.88%, compared to other medium of 8.73% (ambient temeprature) and 4.17% (ice), respectively[27].

These two kinds of method lop too much side on subjective experiences or on objective data, and fail to consider the comprehensive effects of both objective and subjective information.
Factor analysis is a multiple statistic analysis method which could be used for data simplification and dimension reduction.
Only subjective evaluation or only objective evaluation mainly emphasizes subjective data or subjective experiences, and fails to comprehensively consider both subjective and objective information.
Step 2, normalize the data obtained in step 1.
After collecting and tidying up these corporations’ financial data in year 2007 and by applying factor analysis, the evaluation indictors screened are all in TABLE Ⅰ.

Zhang et al used HHT to analyze recordings of hypothetical and real wave motion, the results of which had been compared with the results obtained by the Fourier data processing technique [3].
The analysis of the two recordings indicated that the HHT method is able to extract some motion characteristics useful in studies of seismology and engineering, which might not be exposed effectively and efficiently by Fourier data processing technique.
It is controversial that the structural damage was simulated as a sudden stiffness reduction.
A new spectral representation of earthquake data: Hilbert spectral analysis of station TCU129, Chi-Chi, Taiwan, 21 September 1999.
Application of Hilbert-Huang Transform to Structural Health Monitoring: Experimental Data.