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The characteristics of statistical wastewater data in the present study are listed in Table 1.
Table 1 The characteristics of the wastewater generated by the production of heavy crude oil Parameter Value pH 7.06±0.02 Temperature (°C) 55±2 Oil content (mg/L) 29.8±28.9 Sulfides (mg/L) 2.3±0.6 CODcr (mg/L) 537±166 Volatile phenols (mg/L) 0.45±0.3 Total salinity (mg/L) 4466.3 The wastewater sample described in Table 1 was taken from the settling andfiltration components of the production process for heavy crude oil. 2.2 The pilot wastewater treatment system The pilot wastewater treatment plant (Fig. 1) is composed of six concrete tanks: Figure 1 Chematic of the pilot system for anaerobic hydrolytic acidulation These bacteria were chosen based on a preliminary screening (data not shown) that demonstrated their ability to grow well at a range of higher temperatures and acidity levels, salinity levels.
Table 2 Results of the treatment of the wastewater generated by the production of heavy crude oil parameter Before treatment After treatment Reduction(%) Oil content (mg/L) 25.9 5.0 80.6 TSS (mg/L) 110 10 90.9 Sulfides (mg/L) 2.6 0 100.0 CODcr (mg/L) 362 91 74.8 Volatile phenols (mg/·L) 0.37 0 100.0 After the anaerobic and aerobic biological treatments, the fixed-film bioreactors with a 15-h hydraulic retention time (HRT) reduced COD by 74.8%, TSS by 90.9%,oil by 80.6%, and phenols and sulfides by 100%.

According the code for investigation of geotechnical engineering (GB50021-2001), some corrosive analytical data of groundwater have been selected and listed in the Table 1.
Analysis for the Causticity of Groundwater The data in Table 1 indicate that the groundwater’s causticity to the concrete can be separated into 3 pieces include acid causticity (pH value), calcareous causticity (corrosive CO2) and leaching causticity (HCO3-).
In humid and rainy weather conditions, the soil ventilation property is bad and anaerobic sulfate reducing bacteria will breed rapidly, then sulfate will reduction to sulfur or sulfide.

Reduction of the problem Assume that the initial condition satisfied the following condition: , define a new function to afford the infinite boundaryof , and introduce the function transform lemma 2.1.
The new functionsatisfies (4) with the additional condition (5) Then the considered question is convert to reconstructfrom the information about,where, andare all known.Notice that theshould be taken for.Introduce the density function representation for: (6) withUsing(4), we get thatmeets (7) also considering the geodesic data,with (8) The (7) is an integral equation of the first kind with respect to the density function.Regularizing scheme should be applied to solve this equation to get the approximate solution.
The Model Function Method The (1)-(2)is essentially converted to (7),which is an integral equation of the first kind.The integral operator is compact,is ill-posed,small perturbations in the observation data may lead to large effects on the considered solutions.

The heat exchanger which has a good property of heat transfer and scaling reduction is the basic guarantee for system operation because the washing BFS water consists of much slag wool and its water composition is complex.
The structure parameters of self-cleaning plate shell heat exchanger material heat transfer area (m2) corrugated form corrugated angle (o) clearance between plates (mm) plate thickness (mm) 317L 100 Chevron Shape 60 6 0.75 Results and Discussion The experimental data of the heating system using the waste heat of washing BFS water are shown in the Table 2.
Testing data of the experimental system using the waste heat of washing BFS water time （t） BFS water inlet temperature （℃） BFS water outlet temperature （℃） heating water inlet temperature （℃） heating water outlet temperature （℃） 20：40 78 70 58 71 21：10 77 69 60 70 21：40 77 71 62 72 22：10 80 73 63 73 23：40 80 68 57 70 00：10 82 70 59 72 The effect of heat exchanger parameters to the heat transfer efficiency is shown in the Fig.2.

Typically an increase in the size of the stylus tip will result in a reduction in the measured roughness parameter due to the tips inability to contact the bottom of sharp valleys on the surface and this may lead to investigation of accuracy analysis(parameter) and finally we can get which probe give more accurate result in Ra [4].
To analyze the effects of individual factors on the surface roughness, the data obtained from the experiment were statistically analyzed with MINITAB software [8].
Finally, for main effect cut of length, longer distance which is 2.50mm gave more accurate results data of the average roughness (Ra).

The ICATS software was carried out to capture the Frequency Response Functions (FRFs) data at each load step.
On the other hand, using measured FRFs may have certain advantages over using modal data.
Second, the FRFs can provide much more information about damage in a desired frequency range than modal data extracted from a very limited number of FRF data around resonance [4].
The rest problem was most likely affected by bad FRF data capturing.
De Roeck, Structural damage identification using modal data.

e) Physical and chemical property data storage of hazardous materials.
Applicable to store physical and chemical property data of the hazardous materials to provide transfer support for necessary data information during the process of hazardous material discharge estimation and gas three-dimensional dispersion forecasting.
Utilize high space-time resolution atmospheric circulation numerical forecasting model to calculate the meteorological data, then transfer and extract program to extract and convert the meteorological data.
e) Provide the estimation result and simulation parameter document of meteorological data and hazardous material discharge to the core computer module, transfer the physical and chemical property data of the hazardous materials to obtain physical and chemical property data and operate gas three-dimensional forecasting model.
f) Distinguish and screen out effective data of the dispersion simulation result and convert the forecasting data into the standard data of the equitime spatial grid interval coordinates.

Alongside with obvious advantages, these program complexes have a number of disadvantages such as significant time for loading of input data and calculations, complexity in learning, etc.
Input data file 2.
Windows for the data on workpiece configuration, coordinates of initial approximations and the parameters describing the process of stamping (Fig. 7) are settled down in this field.
As a result, the numerical data on values of optimized parameters, namely: the size of each cut's radius and coordinates of their centers are obtained on the screen.
The simulation results obtained have been compared to experimental data and have shown comprehensible inaccuracy.

Artificial neural network In the input layer, the input data are calculated by weights, and the calculated values are calculated with a non-linear function to output the results.
y=f(i=1nwixi+b) (1) (w: neural network weight coefficient, x: neural network Input data, b: bias) In this way, building multiple basic network structures will result in a Artificial Neural network, as shown in Figure 2.
This means that more abstract data representations can be found across each layer, thereby gaining effective machine learning performance.
Based on the experimental data, the Artificial Neural Network was used to training current density values as machining conditions change, then the simulation was conducted, and the predicted values were compared and analyzed.
As shown in Table 3, the 250 trained Artificial Neural Network(ANN) have the lowest root mean square error(RMSE) between experiment and predicted data.

Data Process and Experiment model This paper analysis on EEG analysis is mainly on the components of ERP, the steps are as follows: Step 1, Block larger drift EEG: in EEG acquisition process, such as the subjects movement, wander, outside sound effects, the EEG signal initial there will be large drift, will follow the EEG signal processing impact, so in the EEG before treatment, to remove this a part of the brain electrical signal; Step 2, Ocular artifact reduction: the EEG signal in the original, because to blink or look right and left, the impact on the eye electric signal, so before feature extraction and classification, to remove the impact of this part, this paper is mainly to remove the vertical eye film; Step 3, epoch the data, view the stimulus intervals, and generally 10%-20%, -50, is the common value of -100; the spirit of not more than one event to the principle, in this paper, the interception of data in -100~900ms.
Step 4, baseline correction: the segmented data many not at baseline, so this paper conducted a total of two times the baseline correction and a linear correction.
Step 5, artifact reject: EEG signals collected by the class, there is a part of segmented data caused by various reasons is not good, not only for data analysis useless, it will affect the analysis of the data, so to choose a certain window screening, the window is -80~80.