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Basic inputs necessary for SWAT simulation requires weather data, management data, point source data, and three GIS data layers, namely, digital elevation model (DEM), soil type data, and land use type data.
The DEM, soil and land use data are shown in Fig. 2.
Table 1 shows the source and description of basic data.
The major of PSP data include industrial pollution and domestic sewage.
Table 1 Source and description of the input data Type Scale Data format Source DEM 30m GeoTIFF ASTER GEDM Data （http://www.gdem.aster.ersdac.or.jp/） Land use type 1：250 000 Grid Data Sharing Infrastructure of Earth System Science （www.geodata.cn） Soil type 1：4 000 000 Shape Data Sharing Infrastructure of Earth System Science （www.geodata.cn） Model approach.

Modeling Industrial Cost-benefit Matrix Allocation of Carbon Emission Reduction Zuozhi Li Management School, Tianjin Polytechnic University, Tianjin, China, 300387 LZZ200603@hotmail.com Keywords: Cost-Benefit matrix, Industrial layout, Carbon emission, Policy-making Abstract.
max: t As well as, t∈[0,1], Based on really statistical data of base year, the authentic benefit matrix BL and cost matrix CL are acted as the starting point of production; the neighboring year or period production will shift in the fuzzy factor direction from the origin.
To pick up data of base year 2010 from statistical yearbooks of Tianjin Binhai New area, production output layout of 37 industrial sectors is obtained and displayed in table 1.
Added value of carbon emission intensity per profits and taxes varies from minimum to maximum, so the new constrained conditions are shown as follows: xi>=1-si*t，i=1, 2, …, 37 The resulting shows that industrial layout is divided four sectors’ groups to estimate the industrial development depended on data of base year.
Optimal planning of renewable energy-integrated electricity generation schemes with CO2 reduction target.

Engine system model was designed with KULI software according to wind tunnel testing data of heat exchanger provided by supplier.
The locations of the cooling system components are such as table 2, and cooling system model is set up as shown in fig.1 and fig.2: Table 2 The position of the cooling system components Components Width [mm] Height [mm] Depth [mm] Pos x [mm] Pos y [mm] Pos z [mm] 1.cp value - - - -200 300 450 2.cp value - - - 600 300 450 Built-in resistance - - - 500 300 450 1.Area resistance 600 200 20 -50 12 0 2.Area resistance 600 350 10 -50 12 400 Charge air cooler 590 550 76 0 37 0 Mechanically driven fan 640 640 52 300 0 0 radiator 650 900 52 100 0 0 Fig. 1 The internal loop Fig. 2 The external loop Simulated calculation Additionally, calculation parameters of numerical model were determined according to the experimental data of bench simulation run and the parameter setting of model parts.
Table 2 The setting of simulating parameters Conditions Condition 1 Condition 2 Condition 3 RPM Engine [rpm] 1300 1600 2100 BMEP Engine [kPa] 145 164 158 Driving speed [km/h] 15 15 15 Ambient pressure [hPa] 1013 1013 1013 Ambient temperature [℃] 35 35 35 Air humidity [%] 45 45 45 Fan RPM [rpm] 1300 1600 2100 A/C on off off off Water circuit Flow rate[l/min] 240 315 440 Pressure [bar] 2 2 2 Quantity of heat[kW] 151.5 190.69 256 Charge air circuit Flow rate[kg/s] 0.25 0.306 0.4 pressure[bar] 2.5 2.59 2.75 Medium temperature[℃] 145.9 153.06 165 Through the analysis of KULI simulation, the following main data result were attained, including the inlet and outlet temperature and the water temperature differential of the radiator, inlet and outlet pressure and the water pressure differential of the radiator, the inlet and outlet temperature and the temperature differential of the charge air cooler.
The cooling system optimization Although in the allowed temperature range of engine, considering the improvement of engine power and engine compartment space reduction, it is necessary to optimize the overall layout of the cooling system to make a better heat dissipation capability.
(2) For the engine cooling system of heavy vehicle, especially now the power improvement of heavy vehicle engine and space reduction of engine compartment, the components matching is vital to the merits of thermal performance.

Data acquisition.
Data Processing.
So making data merging together effectively is prerequisite of terrain analysis and data processing.
Data processing.
Data Reduction for Reverse Engineering[R].

Fig. 3 shows the PolyMAX stabilization diagram for the reference data set S0.
The available data for each damage scenario are split in two equal parts.
The variances in Eq. 1 are estimated empirically using the available data.
Online (thus, using validation data), at each sample, the current VAFs are computed.
In this case, 2 s of data were used to identify a new model.

There were data processing including gridding, filtering, reduction to the pole, continuation, derivation, and so on.
Geophysical Characteristics and Data Processing Method Geophysical Characteristics.
The data processing has done before interpreting magnetic body including of reducing to the pole and continuation[3].
An enhanced method for source parameter imaging of magnetic data collected for mineral exploration.
Application of geophysical data acquisition and processing.

The as-received samples were tested and used as baseline data.
The system is equipped with a specialized data acquisition, data reduction and data plotting software.
The fracture strain however, experiences an abrupt reduction in value that starts after ½ month of outdoor exposure (Fig. 1).
This reduction reaches 38% after an additional ½ month of exposure.
As fracture strain is generally thought of as measure of material toughness, its reduction is an indication of lower toughness.

SEM data show that the average MnO2 particle size is 482.80 nm.
Figure 2 illustrates the mechanism of metal ion reduction to form particles.
The activation phase is in which the reduction of metal ions and the nucleation of reduced metal atoms occur.
Thus, the XRD data confirmed the formation of both samples.
The performance comparative data of this work with the previously reported studies in AC/MnO2 Material.

Secondly, the bending strength of the panel structures was compared with the plain sheet steels to estimate the possibilities for weight reduction.
For example, in transportation business, the weight-reduction is directly utilized as increased payload or reduced operating costs.
The experimentally measured force versus displacement during three-point bending was recorded and compared with the simulation data.
(a) bending moment - displacement curves and (b) weight reduction estimation.
Results were compared against a conventional steel sheet, and approximations of weight reductions were made.

Data.
The land use data used in this study are from the Chinese Academy of Sciences.
uri=cn.csdb.soil, and the resolution of the data is 2km×2km.
The observed runoff data at hydrological stations are from 2006 to 2007, while the data at rainfall stations and meteorological stations are from 1961 to 2009.
Limited to the data available, the hydrological model is just validated using the observed runoff data of 2 years, although the validation results is good, more observed data including long series of runoff, evapotranspiration and groundwater table should be collected for more reasonable and comprehensive validation.