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CFD modeling for coal size effect on coal gasi

CFD modeling for coal size effect on coal gasi?cation in a two-stage commercial entrained-bed gasi?er with an improved char gasi?cation

model

Hyo Jae Jeong a ,Dong Kyun Seo b ,Jungho Hwang a ,?

a Mechanical Engineering Department,Yonsei University,Seoul,Republic of Korea b

Coal Gasi?cation Team,KEPCO,Daejeon,Republic of Korea

h i g h l i g h t s

CFD modeling of an E-Gas?coal gasi?er according to coal sizes was conducted. The random pore model with diffusion effects was used via a user de?ned function. The CFD results were reasonable as con?rmed by the operating data. The ef?ciencies of the gasi?er were maximized with a coal size of 100l m.

a r t i c l e i n f o Article history:

Received 5December 2013

Received in revised form 11February 2014Accepted 12February 2014

Available online 12March 2014Keywords:CFD

Two-stage entrained-bed gasi?er E-Gas?gasi?er Coal size

Diffusion effect

a b s t r a c t

Computational ?uid dynamics (CFD)modeling of coal gasi?cation in an E-Gas?gasi?er was conducted with an improved char gasi?cation model using commercial code,ANSYS FLUENT 14.0,in order to study the effect of the coal size on the gasi?cation performance.The CFD modeling was carried out by solving the three-dimensional,steady-state Navier–Stokes equations with the Eulerian–Lagrangian method.Gas-phase chemical reactions were solved using the ?nite-rate/eddy-dissipation model.In order to solve char gasi?cation reactions,the random pore model with bulk and pore diffusion effects was considered via a user-de?ned function (UDF).For each coal size,the species mole fractions,exit gas temperature,carbon conversion ef?ciency,cold gas ef?ciency,and distributions of the gas temperature and species in the gas-i?er were calculated through modeling.The CFD results were reasonable in terms of the species mole fractions and the temperature at the exit as con?rmed through a comparison with the operating data of the Wabash plant.The carbon conversion ef?ciency and cold gas ef?ciency were maximized with a coal size of 100l m.

1.Introduction

As a core technology for clean and high-ef?ciency utilization of coal,gasi?cation has been widely used to produce syngas and fuel gas [1].Generally coal gasi?cation is carried out in one of three types of gasi?ers based on solid feedstock movement.These three types are ?xed-bed,?uidized-bed,and entrained-bed gasi?ers.In the entrained-bed gasi?er,there are two feeding options available,one is slurry feeding and the other is dry feeding [2].Examples of dry-fed gasi?ers are Shell’s or Siemens’coal gasi?ers.General Electric’s (GE)and Phillips 66’coal gasi?ers are examples of water slurry-fed gasi?ers [3].

Although entrained-bed gasi?ers have become available through a number of demonstration projects,information regarding the plant operating results of commercial coal gasi?ers has been restricted.Since an independent attempt to accumulate technical understand-ing is required,universities and national institutes,such as the Department of Energy (DOE)in the United States,have recognized numerical simulation as one of the most important requirements for the advancement of gasi?cation technology [4].Speci?cally,com-putational ?uid dynamics (CFD)analysis provides results for the pre-diction of ?uid ?ow and species concentrations in a gasi?er,considering also the reactor geometry and operating conditions [5].For the production of synthetic natural gas (SNG),the Pohang Iron and Steel Company (POSCO)in Korea has started developing a pair of 2550tons/day Phillips 66’s E-Gas?gasi?ers,which are slurry-fed,pressurized,up?ow,oxygen blown,and entrained two-stage slagging gasi?ers.To keep pace with the POSCO project,

?Corresponding author.Tel.:+8222123282;fax:+8223122821.

E-mail address:hwangjh@yonsei.ac.kr (J.Hwang).

the development of a three-dimensional CFD model for the E-Gas?gasi?er was conducted in this work.

There have been several CFD studies conducted for the E-Gas?gasi?er,including the work of Shi et https://www.wendangku.net/doc/697217986.html,ing ANSYS FLUENT,they carried out a calculation at a set Illinois#6coal feed rate of 2400tons/day,an O2/coal(dried)ratio of0.927,a water/coal slurry ratio of0.3,and a pressure of2.8MPa,and found the temperature distributions and species mole fractions at the exit of the gasi?er [6].Slezak et al.developed a three-dimensional model for the sim-ulation of a2423tons/day E-Gas?gasi?er using ANSYS FLUENT. The model was modi?ed from a previously developed gasi?cation CFD model created by Shi et al.to account for the coal particle den-sity and size distributions with Pittsburgh#8coal.They found the distributions of the temperature and species mole fractions in the gasi?er,carbon conversion ef?ciency,and cold gas ef?ciency[7]. Ma and Zitney developed a three-dimensional model for the simu-lation of an E-Gas?gasi?er using ANSYS FLUENT.Illinois#6coal was used for the simulation.They developed an improved drying model,a devolatilization model,and a char gasi?cation model via user-de?ned functions(UDFs)in the program for an accurate numerical prediction of the gasi?er performance.The calculation was conducted at an Illinois#6coal feed rate of2400tons/day,an O2/coal slurry ratio of0.50,a water/coal slurry ratio of0.34,and a pressure of2.84MPa.They found the distributions of the tempera-ture and species mole fractions in the gasi?er and the carbon con-version ef?ciency[8].Luan et al.developed a three-dimensional model for the parametric study of a2230tons/day E-Gas?gasi?er using ANSYS FLUENT.Illinois#6coal was used in the study,which was conducted by varying the operating conditions,such as the O2/coal ratio,coal slurry concentration,and the coal slurry feed rate, in each stage.They found trends of the temperature and species mole fractions at the exit under various operating conditions[9]. Chyou et al.developed a three-dimensional model for the simula-tion of a2545tons/day E-Gas?gasi?er using ANSYS FLUENT.They investigated the effect of modi?ed second stage injection on the gas-i?cation performance with Illinois#6coal.They also investigated the gasi?cation features by using North Dakota(ND)lignite as the feedstock.They found that the gasi?cation performance of ND lig-nite was always lower than that of Illinois#6coal[10].

In an E-Gas?gasi?er,unreacted char is recycled to the1st stage using the syngas cleaning process[11–13].Therefore,the carbon conversion ef?ciency is an important issue for an E-Gas?gasi?er. The carbon conversion ef?ciency in the gasi?cation process is determined by char gasi?cation,since the rate of char gasi?cation is much slower than that of devolatilization[14].In the study of Kim et al.[15],the reactivity of the char gasi?cation was deter-mined to be largely in?uenced by the particle size at higher tem-peratures.Therefore,this work focused on an improvement of char gasi?cation model in order to investigate the coal size effect on the gasi?cation performance of an E-Gas TM gasi?er.

In this work,the numerical calculation results of the coal size effect on the gasi?cation performance of an E-Gas?gasi?er were disclosed for the?rst time to our knowledge.Illinois#6coal(uniform)sizes of 50,100,200,and300l m were considered after validation with the Ro-sin–Rammler diameter distribution method.For each coal size,the species mole fractions,exit gas temperature,carbon conversion ef?-ciency,cold gas ef?ciency,and distributions of the gas temperature and species in the gasi?er were calculated.Our CFD results were com-pared with the operating data of the Wabash plant in the United States.

https://www.wendangku.net/doc/697217986.html,putational model

https://www.wendangku.net/doc/697217986.html,putation setup

Fig.1shows the geometry and grids of an E-Gas?gasi?er.Most of the geometric dimensions of the E-Gas?gasi?er were obtained from the National Energy Technology Laboratory(NETL)’s pub-lished documentation[8,12,13,16].The?ow domain of the?rst stage consists of a cylinder with an inner diameter of2m and a length of8m.The main part of the second stage has a cylindrical ?ow domain with an inner diameter of 1.6m and a height of 12m from the centerline of the?rst stage to the model exit.There is a cylindrical throat section of1m in diameter and1.2m in height near the T-junction of the two stages.The exit is also ta-pered to a smaller cylinder that is1m in diameter.The geometry of the gasi?er and its grids were generated with a hybrid-mesh using GAMBIT(version2.4.6).Calculations were carried out with varying the number of grids from55,000to700,000.The number of grids did not affect calculation results when the number was be-tween88,000and700,000.Therefore,the number of grids of 88,000was applied to the computational model.

https://www.wendangku.net/doc/697217986.html,erning equations

In the numerical procedure,the3-D steady-state Navier–Stokes equations were solved in an Eulerian–Lagrangian frame of refer-ence.All of the coal particles were treated as a discrete,secondary phase dispersed in the continuous phase via the discrete phase model(DPM)with stochastic tracking to consider the turbulent dispersion effect.The discrete ordinate(DO)radiation model,to-gether with the domain-based weighted-sum-of-gray-gases model (WSGGM)for the radiative properties of the gases was applied,and the gravitational force was considered in the modeling.The stan-dard k-e model was applied to capture the turbulent?ow.The species transport equations were solved through the?nite-rate/ eddy-dissipation model.In this model,both the?nite-rate and the eddy-dissipation rate were used and compared,and the slower rate was selected to compute the continuous phase reactions.

2.3.Coal reactions sub-model

2.3.1.Coal devolatilization

The two competing rates model,which was widely used in the previous studies[17,18],describes the devolatilization rate as fol-lows[19]:

Fig.1.Geometry and grids of the E-Gas?gasi?er.

30H.J.Jeong et al./Applied Energy123(2014)29–36

R1?A1eàE1=RT pe1Tand

R2?A2eàE2=RT pe2Twhere R1and R2are competing rates that control the devolatiliza-tion over different temperature ranges.T p is the temperature of the particle(K),A1and A2are Arrhenius-type pre-exponential fac-tors(sà1),E1and E2are activation energies(J/kmol),and R is the universal gas constant(8314J/kmol K).

The two kinetic rates are weighted to yield an expression for the devolatilization:

m vetT

w;o p;0a ?

Z t

a1R1ta2R2

eTexpà

Z t

eR1tR2Tdt

e3T

where m v(t)is the devolatilized mass at time t(kg),m p,0is the initial coal particle mass(kg),and m a is the ash content of coal(kg).f w,0is the mass fraction of evaporating/boiling material.

The coal pyrolysis phenomenon is still not completely under-stood.It is dif?cult to relate the reaction mechanism to fuel prop-erties and such operating conditions as temperature,pressure, particle size,constituents of carrier gas,residence time as well as heating rate[20].In this work,the kinetic rates of devolatilization of each coal size were same;therefore,the devolatilization time of each coal size was calculated by the amount of volatile matter in each coal.In addition,all of the volatile matter produced during devolatilization was assumed to be CHO compounds,the release rate of which is given by Eq.(3)and the composition of which can be calculated from the ultimate and proximate analyses of coal as CH2.761O0.264[9].

2.3.2.Gas phase reaction

The turbulence-chemistry interaction is modeled through the composite Magnussen eddy-dissipation and Arrhenius rates.The reaction rate for each reaction model is de?ned by taking the min-imum of the chemical reaction rate and the turbulent mixing rate [19].

The chemical reaction rate can be expressed as:

R rxn?A r T m eàE r=RT C a

1C b

C c

e4T

where A r is the pre-exponential factor,T is the gas temperature(K), m is the exponent of the temperature,E r is the activation energy (J/kmol),C1and C2are the molar concentrations of the?rst and the second reactants(kmol/m3),respectively,and a and b are the corresponding reaction orders with respect to the two reactants,C3is the concentration of a third species(kmol/m3),which is neither a reactant nor a product,and c is the corresponding reaction order.

Table1shows the gas phase reactions that are included in the model and kinetic parameters of each reaction.

2.3.3.Char gasi?cation

In char gasi?cation models included in ANSYS FLUENT,the char gasi?cation on the internal surfaces of the char was not considered. In order to improve the char gasi?cation model,the random pore model was applied in this work.The random pore model is applied to describe the char gasi?cation model for arbitrary pore size distributions in the char[21].

The char gasi?cation rates are determined according to estab-lished methods with three different reaction regimes:regime I (chemical rate control,low temperatures),regime II(pore diffusion and chemical rate control,intermediate to high temperatures),and regime III(bulk diffusion control,high temperatures).For the present modeling purposes,the rate equation with an effective-ness factor was used to represent the overall reaction rates in these different regimes by using an appropriate effectiveness fac-tor.The rate equation for each gasifying agent is expressed as fol-lows[22]:

dm c;i

?àm c;o g i A c;i P n i i eàE c;i=RT Pe1àxT

?????????????????????????????????

1àw

lne1àxT

for each gasifying agente5T

where the subscript i represents each gasifying agent,m c,i is the mass of char particle(kg),m c,0is the mass of char particle at the initial state(kg),g i is the effectiveness factor,A c,i is the pre-expo-nential factor(1/Pa n i/s),E c,i is the activation energy(J/kmol),P i is the partial pressure(Pa),W i is the pore structure parameter,and n i is the reaction order.x represents the total carbon conversion of char particle by all gasifying agents(=

(m c,0àm c,i)/(m c,0-àm a)).In this work,the kinetic parameters used to calculate Eq.

(5)were obtained from the study by Kajitani et al.[23].The kinetic parameters of char gasi?cation are listed in Table2.

In ANSYS FLUENT,the effectiveness factor is applied only as a constant value.However,the effectiveness factor is not constant during the char gasi?cation.In this work,the effectiveness factor, g i,is determined as follows[22]:

tanh/i

for each gasifying agente6T

where/i is the Thiele modulus.The Thiele modulus is expressed as follows[22]:

Table1

Kinetic parameters of gas phase reactions.

Gas phase reactions A r E r(J/kmol)m a b c Ref.

CH2.761O0.264?0.105C7H8+0.264CO+0.960H2 4.26?106(1/s) 1.08?1080000[36] CH2.761O0.264+1.058O2?CO+1.381H2O9.2?106(1/K/s)8.02?1071000[36] CO+0.5O2?CO2 2.239?1012((m3/kmol)0.75/s) 1.674?108010.250.5[H2O][37] H2+0.5O2?H2O 6.8?1015((m3/kmol)0.75/Kà1/s) 1.67?108à10.25 1.50[37] CO+H2O?CO2+H2 2.34?1010((m3/kmol)0.5/s) 2.883?10800.510[38] H2+CO2?CO+H2O 2.2?107((m3/kmol)0.5/s) 1.9?10800.510[8] CH4+O2?CO+2H2 4.4?1011((m3/kmol)0.75/s) 1.25?10800.5 1.250[39] CH4+H2O?CO+3H28.0?107((m3/kmol)0.5/s) 2.51?10800.510[8] CO+3H2?CH4+H2O 5.12?10-14(m3/kmol/s) 2.73?1040110[29] C7H8+H2?C6H6+CH4 1.04?1012((m3/kmol)0.5/s) 2.47?108010.50[40] C7H8+9O2?7CO2+4H2O 1.6?108((m3/kmol)0.75/s) 1.255?1080à0.1 1.850[41] C6H6+5H2O?5CO+6H2+CH4 4.4?108(m3/kmol/s) 2.2?1080110[42] C6H6+7.5O2?6CO2+3H2O2?108((m3/kmol)0.75/s) 1.255?1080à0.1 1.850[41] C6H6+3O2?6CO+3H2 1.58?1015(m3/kmol/s) 2.026?1080110[43]

H.J.Jeong et al./Applied Energy123(2014)29–3631

/i ?

d p

????????????????????????????????????????????????????????????????????????????????????????????????????????????????????n i t12

A c ;i e àE c ;i =RT P e1àx T???????????????????????????????1àw i ln e1àx Tp q P RT P P n i à1

i t g ;i

M c D eff ;i s for each gasifying agent

e7T

where d p is the size of char particle (m),q p is the density of char particle (kg/m 3),t g,i is the stoichiometric factor of each gasifying agent for each mole of carbon consumed,M c is the molecular weight of carbon (kg/kmol),and D eff,i is the effective diffusivity (m 2/s).Assuming that the pore size distribution is monodisperse and the bulk and Knudsen diffusions proceed in parallel,the effective diffusivity is given as follows [24]:

D eff ;i ?

D KN ;i t1D 0;i

à1

for each gasifying agent e8T

where D KN,i is the Knudsen diffusion coef?cient (m 2/s),D 0,i is the molecular diffusion coef?cient (m 2/s),h is the porosity of the char particle,and s is the tortuosity of the pores.In this work,the poros-ity was assumed as 0.5for Illinois #6coal [25].The tortuosity of 2was applied in this work,since the tortuosity was assumed to be equal to s =1/h [26].

The molecular diffusion coef?cient is important in regime III,since the char gasi?cation is controlled by bulk diffusion.In ANSYS FLUENT,the molecular diffusion coef?cient is calculated by a func-tion of gas temperature and particle diameter without considering each gasifying agent.In this work,in order to obtain the accurate molecular diffusion coef?cient for each gasifying agent,an equa-tion of empirical correlation was used,which is given as follows [27]:

D 0;i ?T 1:75

1M i t

1M CO

1=2

p eV i T1=3teV CO T1=3

h i 2á10à7

for each gasifying agent

e9T

where p is the total pressure (atm),V i is the binary diffusion volume for each gasifying agent molecule (O 2,CO 2,and H 2O for i =1,2,3,respectively)(cm 3),V CO is the diffusion volume for molecule of CO (cm 3),M i is the molecular weight of each gasifying agent species i (g/mol),M CO is the molecular weight of CO (g/mol).

The Knudsen diffusion coef?cient is given as follows [28]:

D KN ;i ?97:0

r P T P

0:5

for each gasifying agent e10T

where

r P is the mean pore radius (m).The mean pore radius is given as follows [28]:

r p ?

2h s 0:5

S t q p

e11T

where S t is the current surface area of char particle (m 2/kg).For a bulk diffusion limitation [29],

dm c ;i dt ?à2p d P D 0;i Y i q g M c

t g ;i M i

for each gasifying agent e12T

where Y i is the mass fraction of species i ,and q g is the gas density (kg/m 3).

All the equations for char gasi?cation model were applied using the user de?ned function (UDF).

3.Boundary and initial conditions

An E-Gas?gasi?er has a capacity of 2450tons/day and is oper-ated at 2.84MPa [13].In this work,the inlet and wall boundary

conditions of the gasi?er were obtained from published papers and technical reports.In order to simplify the computational mod-el,the masses of chlorine,sulfur,and argon were lumped into nitrogen and treated as a gas phase along with the oxidant in the model.The treatment of ash transformation was neglected.The mole fractions of O 2and N 2in the oxidant were 0.95and 0.05,respectively [9].The temperature of the oxidant was assumed to be 380K [12].The O 2/coal (dried)ratio was 0.81[12].

Refractory brick is used as the wall material in a real E-Gas?gasi?er.Since detailed information on the structure and composi-tion of the refractory brick are not available to the public,in this work,the wall boundary condition was speci?ed as ‘‘convective’’in ANSYS FLUENT with an equivalent convective heat transfer coef-?cient of 10W/m 2K,assuming an ambient temperature of 300K.The internal emissivity of the refractory brick was assumed to be 0.6[8].

The 1st stage and 2nd stage of the E-Gas?gasi?er operate in slagging mode and non-slagging mode,respectively [13].In the study of Sun et al.[30],the wall boundary condition for the coal particle was speci?ed as ‘‘trap’’to describe the wall slagging mode.In this work,the wall boundary conditions for the coal particle at the 1st stage and the 2nd stage were speci?ed as ‘‘trap’’and ‘‘re-?ect’’,respectively.

In this work,DPM with stochastic tracking was used to determine the coal particle trajectories.The inlet conditions for coal particles were obtained from published papers and technical reports.The inlet velocity of the coal slurry was 50m/s [6,7].The temperature of the coal slurry was 345K [12].The coal slurry con-centration was 63%[13].Rosin–Rammler diameter distribution method was used as coal size distribution for baseline case.The mean diameter and spread parameter were 100l m and 1.0,respectively [8].After validation of the baseline case with the demonstration plant data,uniform coal sizes of 50,100,200,and 300l m were tested to evaluate the effect of coal size on gasi?ca-tion performance.

4.Results 4.1.Baseline case

Although the E-Gas?gasi?er has been made available through the Wabash river coal gasi?cation repowering project,detailed spatial distributions of the temperature and species mole fractions in the gasi?er have not been disclosed in previous reports.Instead,the species mole fractions and gas temperature at the gasi?er exit are open to the public [13].Therefore,for model validation in this work,the temperature and species mole fractions at the exit of the gasi?er were regarded as index parameters to compare our CFD results with the operating data of the Wabash plant in the United States.Table 3shows that our CFD results for the E-Gas?gasi?er were in good agreement with the operating data.Also,our CFD re-sults were in good agreement with the results of Ma and Zitney [8].The spatial distributions of the gas temperature,wall tempera-ture,and species concentrations obtained by the CFD calculations

Table 2

Kinetic parameters of char gasi?cation reactions.Char gasi?cation reactions A c,i

E c,i (J/kmol)n i W i

Ref.C(s)+0.5O 2?CO 1.13?102(1/Pa 0.68/s) 1.3?1080.6814[23]C(s)+CO 2?2CO 6.27?105(1/Pa 0.54/s) 2.83?1080.543[23]C(s)+H 2O ?CO +H 2

4.18?104(1/Pa 0.64/s)

2.52?108

0.64

[23]

H.J.Jeong et al./Applied Energy 123(2014)29–36

are shown in Figs.2–6.The gas temperature i?er obtained by the CFD calculation is shown in ticles were fed into the 1st stage,they absorbed gas and underwent the evaporation and The produced volatile matter,CO and H 2,were with the oxidant fed into the 1st stage,resulting reaction heat.Therefore,in the 1st stage,the the ?ame zone were higher than those of other peratures of the 2nd stage were lower than those due to endothermic reactions occurring in the devolatilization,cracking of volatile species,and The average gas temperatures of the 1st stage were calculated to be 1608K and 1245K,gas temperature in the range of 1600–1700K was ously for the 1st stage [11–13].

Fig.3shows our CFD results of the inner wall tribution of the gasi?er.The wall temperatures imposing a uniform convective heat transfer a constant ambient temperature and a constant The average wall temperature of the 1st stage was higher than the ash fusion temperature of 1332K [31].The average wall temperature of around 1100K.Therefore,our CFD results and 2nd stages should be operated in ash ging modes,respectively [13].

The distributions of the species molar in the gasi?er are shown in Figs.4–6.The CO 21st stage was higher than that of the 2nd stage,was supplied only in the 1st stage.However,the dant was not suf?cient enough to cause the 1st stage,resulting in a CO concentration that the CO 2concentration in the 1st stage.In the concentration near the ?ame zone was higher parts,due to the oxidation of volatile matter olatilization process and the oxidation of CO mal cracking or partial oxidization of the volatile stage,the CO 2concentration at around the coal higher than that of other parts,since CO 2was ward water gas shift reaction.In the 1st stage,the centrations near the ?ame zone were much other parts,since CO and H 2produced via the or partial oxidation of the volatile matter were with the oxidant.On the other hand,in the and H 2concentration at around the coal slurry than those of other parts,since there were no CH 4was produced by the cracking of volatile sequent reactions of the hydrocarbon species with After the water was injected into the 2nd stage,with steam accelerated the formation of H 2and acted with CO 2to produce CO via the backward reaction.Therefore,CO and H 2concentrations were higher than those of the 1st stage.4.2.Effect of coal size

In the previous section,our CFD modeling of the E-Gas?gasi?er was validated through a comparison with the

operating data of the Wabash plant in the United States [13]and with the temperature characteristics stated in technical reports [11–13].In this section,the coal size effect on the gasi?cation per-formance of the E-Gas?gasi?er was discussed with the model cal-culation results.The total mass loading of the coal was constant with various coal sizes.

Fig.7shows the average gas temperature of each stage,the average inner wall temperature of the 1st stage,and the exit tem-perature,according to variations in the coal size.The average gas

Table 3

Comparison between the CFD results and operating data.

CFD results

Operating data [13]CO (mol%,dry)46.642.2–46.8H 2(mol%,dry)39.732.3–34.4CO 2(mol%,dry)11.814.9–17.1CH 4(mol%,dry)

1.1 1.0–

2.3Exit temperature (K)

1211

<1311

Fig.2.Gas temperature distribution.

Fig.3.Inner wall temperature distribution.

temperature of the1st stage increased with the increase in coal size,since the total surface area of the coal particles decreased.

With the decrease of the total surface area,the absorption coef?-cient of coal for the radiation heat transfer from bulk gas to coal decreases[32].The increase of the average gas temperature of the1st stage with coal size could also be caused by the fact that the total heat of reaction in the1st stage increases with coal size (see Table4).The increase of the average gas temperature of the 1st stage also caused an increase in the average gas temperature of the2nd stage as well as the exit temperature,which was consis-tent with the result in the study by Silaen and Wang[33].Fig.7 also shows that the average inner wall temperature of the1st stage for any coal size was lower than the average gas temperature of the 1st stage due to heat loss to the wall.Table4shows that the ratios of heat loss to coal higher heating value were lower than3%for all coal sizes.The average inner wall temperatures of the1st stage for

Fig.4.Distribution of CO2molar concentration. Fig.5.Distribution of CO molar concentration.

Fig.6.Distribution of H2molar concentration.

Fig.7.Effect of coal size on temperatures.

reaction and heat wall loss in the1st stage with coal sizes.

Total heat of reaction of

the1st stage(MW)

Heat wall

loss(MW)

Heat wall loss/coal

heating value

209à0.7 1.9

237à0.8 2.1

263à0.9 2.4

300272à1.0 2.7

all coal sizes were maintained above the ash fusion temperature (1332K).

Fig.8shows the effect of the coal size on the carbon conversion ef?ciency of each stage,the overall carbon conversion ef?ciency,and cold gas ef?ciency.In this work,the carbon conversion ef?-ciency and cold gas ef?ciency de?ned in previous papers were used [2,17,18,20,34,35].The carbon conversion ef?ciency of the 1st stage decreased as the coal size increased from 100to 300l m.This trend can be explained with temperature distribution (see Fig.7).When the temperature is relatively high,the diffusion becomes more important than the chemical reaction.Then the effectiveness factor g is inversely proportional to the coal size,thereby decreas-ing the reaction rate.In addition,on the total external particle sur-face area basis,large particles have lower total external area,which leads the reaction rate to decrease.

It was interesting to note that the carbon conversion ef?ciency of the 1st stage increased as the coal size increased from 50to 100l m.This phenomenon can be based on the competition be-tween char and volatiles for the available O 2,which is related to the volatile/O 2reaction kinetics implemented in the model.Vola-tiles leaving a coal particle can create a Stefan ?ow that prevents O 2from diffusing to the coal surface.Therefore,slower devolatil-ization may delay char oxidation and gasi?cation,resulting in low-er carbon conversion ef?ciency.However,usually volatiles react with O 2more quickly than coal char,which leads to following phe-nomena;(1)The partial pressure of volatile matter near the ?ame zone decreases with increase in coal size,since the reaction time

required to ?nish coal devolatilization process increases with in-crease in coal size.(2)The decrease in partial pressure of volatile matter near the ?ame zone affects the reaction rate for partial oxi-dation of volatile matter.In our CFD results,the reaction rate for the partial oxidation of volatile matter decreased with increase in coal size.(3)The decrease in the reaction rate increases the partial pressure of oxidant near the ?ame zone,which affects the increase of the reaction rate of the partial oxidation of coal char.As shown in Fig.9,the molar concentration of O 2near the ?ame zone at the coal size of 50l m was lower than that at the coal size of 100l m.For these three reasons,slower devolatilization for larger coal size could increase char oxidation and gasi?cation,resulting in higher carbon conversion ef?ciency as coal size increased from 50to 100l m,as shown in Fig.8.

The carbon conversion ef?ciency of the 2nd stage increased with increase in coal size.This trend can be explained also with the temperature distribution and particle residence time.Fig.7shows that the temperature of the 2nd stage increased with increase in coal size.The increase in temperature resulted in the increase of carbon conversion ef?ciency (see Eq.(5)).Furthermore,the particle residence time increased from 2.079to 2.505s with in-crease in coal size from 50to 300l m,respectively.The increase of particle residence time also increased the carbon conversion ef?-ciency,which can be easily noticed with the integration of Eq.(5).Fig.8shows that the cold gas ef?ciencies for all coal sizes were 72.8–76.7%,while the overall carbon conversion ef?ciencies for all coal sizes were

82.7–87.4%.

Fig.9.Distribution of O 2molar concentration near the ?ame zone at coal sizes of 50and 100l

H.J.Jeong et al./Applied Energy 123(2014)29–3635

Fig.10shows the effect of the coal size on the species mole frac-tions at the exit.The mole fraction of CO increased from50to 100l m,but decreased for sizes larger than100l m.This trend was consistent with that of the carbon conversion ef?ciency,since CO is mainly produced from carbon in the coal particle during the char gasi?cation.The mole fractions of H2and CO2increased with the coal size,which was caused by the increase in the exit temper-ature with the coal size(see Fig.7).As the temperature in the gas-i?er increased,the forward water gas shift reaction became higher than the backward water gas shift reaction.Another explanation for higher CO2at lower carbon conversion is that more CO2and less CO have to be formed to use up the available O atoms,which also leads to higher exit syngas temperature.The results shown in Fig.10for coal sizes of100–300l m were consistent with those presented in the study by Silaen and Wang[33].They conducted CFD simulations of coal gasi?cation with coal sizes of100–300l m.

5.Conclusions

Computational?uid dynamics(CFD)modeling of coal gasi?ca-tion in an E-Gas?gasi?er was conducted using commercial code, ANSYS FLUENT14.0,in order to study the effect of the coal size on the gasi?cation performance.Our CFD results for the E-Gas?gasi?er were in good agreement with the operating data.With an improved char gasi?cation model,the coal size effect was signif-icantly observed in our CFD results.The mole fraction of CO in-creased from50to100l m,but decreased for sizes larger than 100l m.The mole fractions of H2and CO2increased with the coal size.The cold gas ef?ciencies for all coal sizes were72.8–76.7%, while the carbon conversion ef?ciencies for all coal sizes were 82.7–87.4%.The carbon conversion ef?ciency and cold gas ef?-ciency were maximized at a coal size of100l m. Acknowledgements

This study was supported by the Korea Institute of Energy Tech-nology Evaluation and Planning(2010)under the Ministry of Knowledge Economy of the government of the Republic of Korea. The authors gratefully acknowledge this support(No. 2010T100101092).

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医药工业洁净厂房设计要求规范

医药工业洁净厂房设计规范第一章总则第1.0.1条为了贯彻执行国家《药品生产质量管理规范》（以下简称ＧＭＰ），提出符合ＧＭＰ要求的生产厂房、设施及设备的设计要求，特制订本规范。第1.0.2条本规范适用于新建、改建和扩建的医药制剂、原料药和药用辅料的精制、干燥、包装工序，直接接触药品的药用包装材料、无菌医疗器械等医药工业洁净厂房的设计。第1.0.3条医药工业洁净厂房诉设计必须贯彻国家有关方针、政策。做到技术先进、确保质量、安全实用、经济合理，符合节约能源和保护环境的要求。第1.0.4条医药工业洁净厂房的设计，既要满足当前产品生产的工艺要求，也应适当考虑今后生产发展和工艺改进的需要。第1.0.5条在利用原有建筑和设施进行洁净技术改造时，可根据生产工艺要求，从实际出发，充分利用现有的技术设施，符合因地制宜的原则。第1.0.6条医药工业洁净厂房的设计应为施工安装、维护、管理、检修、测试和安全运行创造必要的条件。第1.0.7条医药工业洁净厂房的设计除应执行本规范外，还应符合现行的国家标准、规范和规定的有关要求。第二章生产区域的环境参数第一节一般规定第2.1.1条为了保证医药产品生产质量，防止生产环境对产品的污染，生产区域必须满足规定的环境参数标准。

注2：空气洁净度的测试以静态条件为依据，测试方法应符合国家医药管理工业洁净室和洁净区悬浮粒子的测试方法》中有关规定；注3：对于空气洁净度为100级的洁净室，室内大于等于5μm尘粒的计数，应进行多次采样，当其多次出现时，方可认为该测试数值是可靠的。第2．2．2条药品生产有关工序和环境区域的空气洁净度等级按国家CMP等有关规定确定。第2．2．3条洁净室内的温度和湿度应符合下列规定：一、生产工艺对温度和湿度无特殊要求时，以穿着洁净工作服不产生不舒服感为宜。空气洁净度100级、10000级区域一般控制温度为20～24℃，相对湿度为45～60%。100000级区域一般控制温度为18～28℃，相对湿度为50～65%。二、生产工艺对温度和湿度有特殊要求时，应根据工艺要求确定。第2．2．4条洁净室内应保持一定的新鲜空气量，其数值应取下列风量中的最大值：一、非单向流洁净室总送风量的10～30%，单向流洁净室总送风量的2～4%；二、补偿室内排风和保持室内正压值所需的新鲜空气量；三、保证室内每人每小时的新鲜空气量不小于40m3。第2．2．5条洁净室必须维持一定的正压。不同空气洁净度的洁净区之间以及洁净区与非洁净区之间的静压差不应小于5Pa,洁净区与室外的静压差不应小于10Pa。青霉素等特殊药物生产洁净区，固体口服制剂配料、制粒、压片等工序洁净区的气压控制，应符合第8.5.1条要求。第2．2．6条洁净室和洁净区应根据生产要求提供足够的照度。主要工作室一般照明的照度值不宜低于300LX；辅助工作室、走廊、气闸室、人员净化和物料净化用室可低于300LX，但不宜低于150LX。对照度要求高的部位可增加局部照明。

基于openGL的三维地形场景的生成

1、背景介绍 (3) 2、openGL中地形动态显示 (3) 3、程序的主要功能 (4) 3.1 三维地形的生成 (4) 3.2 天空盒的生成 (8) 3.3 树的生成 (9) 3.4 ３ＤＳ模型的读入 (11) 3.5 键盘交互实现漫游 (11) 3.6汉字的显示 (12) 4、总结 (13) 4.1 项目总结 (13) 4.2 小组成员分工 (14) 参考文献 (15)

1、背景介绍地形是自然界最复杂的景物之一，对其三维显示和漫游一直是计算机图形学、地理信息系统、数字摄影测量和遥感研究的热点之一。但由于受地形结构复杂，数据量大等条件的制约，要实时模拟具有真实感的大范围三维地形，最大的难点是，如何精简并有效地组织地形数据，以达到高速度、高精确度的可视化目的。 openGL是开放式图形工业标准，是绘制高度真实感三维图形，实现交互式视景仿真和虚拟现实的高性能开发软件包。利用openGL进行地形动态显示的基本框架如图1所示：图1 openGL地形现实基本框架 2、openGL中地形动态显示利用openGL进行地形的三维可视化，包含以下几个步骤：（1）openG L模型映射：利用openGL 制作三维立体地形图，就要将数字地面模型格网用openGL提供的点，线，多边形等建模原语描述为openGL图形函数所识别。（2）遥感图像与地形融合：openGL提供两类纹理：一类纹理图像的大小必须是几何级数；另一类Mipmaps 纹理可为任意大小。在Mipmaps纹理映射的基础上，可将遥感图像与地形融合。在遥感影像与数字地形相套合时，地形与遥感影像的配准是关键。为了获取更好的视觉效果，配准方案可采取数字地形向遥感图像配准，通过控制点，建立匹配方程，将数字地形由大地坐标系转到影像坐标系中。（3）观察路线设置与视点计算：为了达到三维交互控制的目的，可在正射的遥感数字影像上任意选择观察路线，对路线上的采样点记录其平面坐标，根据采样点的平面位置从DEM 中采用一定的插值方法，确定观察路线上采样点的高程和平面坐标，当采用Fly-through方式观察时，观察路线上每个视点的高度可由观察点地面高程加上飞行高度确定当采用walk-through方式观察时观察路线上每个视点的高度可由观察点地面高程加上

浅析淘宝网的发展现状和未来发展模式

浅析淘宝网的发展现状和未来发展模式摘要：2008年以来，受到全球金融危机蔓延深化的影响，我国多数行业都受到了不同程度的冲击。但包括网络零售的电子商务行业发展却一路繁荣，成为危机背景下经济增长的一个亮点。本文以淘宝网为例，运用SWOT分析法，浅析目前淘宝网的发展面临的优势，劣势，机会和威胁，而后提出我们对于淘宝未来发展的应对之策。关键词：淘宝网，网络购物，C2C模式，一．引言在国内C2C网络购物网站中，以淘宝网发展最为迅猛，《2009年上半年中国网络购物市场发展报告》显示，2009年上半年网络购物交易规模达到1034.6亿元，其中，淘宝网交易额达到827.6亿元，占据整体市场份额的80％，位居第二的是拍拍网，市场份额占整体市场份额的8．1％。可以看出，淘宝网的运营模式可以代表国内C2C网购市场的一个重要发展方向，因此研究淘宝网的运营模式对准确把C2C电子商务现状和走势，解决C2C电子商务今后发展急需解决的突出问题，具有现实意义。二．电子商务的内涵电子商务：从广义上说，是指以电子设备为媒介进行的商务活动；从狭义上说，是指以计算机网络为基础所进行的各种商务活动，包括商品和服务的提供者、广告、消费者、中介商等有关各方行为的总和。报告中的电子商务是指狭义上的。三．网络购物概念网络购物：借助网络实现商品或服务从商家/卖家转移到个人用户（消费者）的过程在整个过程中的资金流，物流和信息流，其中任何一个环节有网络的参与，都称之为网络购物。网络购物分类见下图：四．淘宝的市场份额目前，我国网民的大部分网购商品网络购物交易集中于平台式购物网站，，而其首选购物网站则是淘宝网。淘宝网用户市场份额达84.6，处于绝对领先地位。

4 项目建设技术路线与三维建模方案

朝阳区数字化三维仿真模拟城市管理系统建设方案

版本控制修改记录说明

1.概述 1.1.项目建设背景 “数字城市”是城市信息化发展的方向，是数字地球的一部分，三维地理信息是“数字城市”的重要基础空间信息。三维城市的建立能够全方位地、直观地给人们提供有关城市的各种具有真实感的场景信息，并可以以第一人称的身份进入城市，感受到与实地观察相似的体验感。随着二十一世纪的互联网技术、计算机技术、3S（GIS/RS/GPS）技术、虚拟现实、航空与航天技术等的飞速发展，给地理信息技术手段带来前所未有的变革，利用高分辨率卫星影像以及航空像片，通过对影像的平面、高程、结构、色彩等的数字化处理，按照统一坐标无缝拼接而成可以迅速建立基于真实影象的“三维数字城市”，人们可以直观的从三维城市上判读处山川、河流、楼宇、道路。借助传统平面地图的概念，叠加空间矢量数据，地物兴趣点数据、以及三维模型数据形成可视化“三维数字”城市展示系统。与传统二维地图相比，“三维数字城市”展示系统突破平面地图对空间描述二维化、三维空间尺度感差、没有要素结构与纹理信息等诸多限制，通过对真实地形、地物、建筑的数字化三维模拟和三维表达，提供给使用者一个与真实生活环境一样的三维城市环境。通过数字化三维仿真模拟城市的实现对城市的管理，把传统的限于二维的城市管理范围扩展到了三维甚至多维的管理范畴，为城市建设、政务管理、企业信息发布与公众查询提供多维的、可持续发展的信息化服务，将大大提高城市整体信息化管理和经营管理水平，并有利于提高公众参与城市管理的积极性和参与性。 1.2.项目建设目标以先进的技术手段，在三维仿真模拟城市场景中实现朝阳辖区单位、人口、部件、事件、社区绿化等相关信息的管理，进一步提高朝阳区政府城市管理水平，提高居民参与城市管理的积极性。另一方面，能够很好的展现数字朝阳的建设成果。最终为建设和谐朝阳提供技术保障，为数字奥运做出贡献。

洁净厂房设计规范2019

1 总则 1.0.1为在医药工业洁浄厂房设计中贯彻执行国家有关方针政策,做到技术先进、安全可靠、确保质量、节能环保,制定本标准 1.0.2本标准适用于新建、扩建和改建的医药工业洁净厂房设计。生物制品、毒性药品、精神药品、麻醉药品以及放射性药品的生产和质量检验设施除应执行本标准外,尚应符合国家有关的监管规定。 1.0.3医药工业洁净厂房的设计应为施工安装、系统设施验证、维护管理、检修测试和安全运行创造必要的条件 1.0.4医药工业洁净厂房的设计除应符合本标准外,尚应符合国家现行有关标准规范的规定。 2术语 2.0.1医药洁净室pharmaceutical clean room 空气悬浮粒子和微生物浓度,以及温度、湿度、压力等参数受控的医药生产房间或限定的空间。

2.0.2医药工业洁净厂房pharmaceutical industry clean room 包含医药洁净室的用于药品生产及质量控制的建筑物 2.0.3人员净化用室room forcleaning personne 人员在进人医药洁净室之前按一定程序进行净化的房间 2.0.4物料净化用室room forcleaning materia 物料在进入医药洁净室之前按一定程序进行净化的房间 2.0.5受控环境controlled environment 以规定方法对污染源进行控制的特定区域 2.0.6悬浮粒子airborne particles 用于空气洁净度分级的空气悬浮粒子尺寸范围在0.1um~1000um 的固体和液体粒子。 2.0.7微生物microorganIsms 能够复制或传递基因物质的细菌或非细菌的微小生物实体 2.0.8含尘浓度particle concentration 单位体积空气中悬浮粒子的数量

中国路灯现状与未来发展分析

中国路灯现状与未来发展分析一、目前全国公路的分布与建设据“中华人民共和国交通运输部《09年公路水路交通运输行业发展统计公报》”数据统计，2009年底，全国公路总里程达386.08万公里，按公路技术等级分，各等级公路里程分别为：高速公路6.51万公里，一级公路5.95万公里，二级公路30.07万公里，三级公路37.90万公里，四级公路225.20万公里，等外公路80.46万公里。公路桥梁、隧道总量继续增加。2009年底，全国公路桥梁达62.19万座、2726.06万米，全国公路隧道为6139处、394.20万米，是世界上公路隧道最多的国家。二、我国公路未来5年的发展 1、公路公路建设方面我国现在在二级以上的公路建设（不包括高速公路）投入发展规划，每年约以12万公里的速度递增，未来5年将增加60万公里的公路交通枢纽。 2、高速公路建设方面关于高速公路的网点建设方面，《国家高速公路网规划》已经国务院审议通过，规划的出台标志着中国高速公路发展进入了新的历史阶段。据中国交通部部长张春贤表示，中国国家高速公路规划网络是一项庞大的工程，未来30年静态

投资两万亿元人民币，这个投资的力度随计划建设的进度而变化。2010年前，每年的年均投资大约在1400到1500亿元人民币，每年增加3000公里左右。2010年以后到2020年之间，年均投资大约在1000亿元人民币，每年增加2000公里左右。至2015年，我国高速公路将增加1万公里，总长度预计达到7.51万公里。 3、隧道建设方面随着公路网点的建设，我国未来五年在隧道建设方面预计将会增加300公里。三、我国路灯的分布与发展中国在2006年具有1500万盏路灯，并以每年20％的速度增长，也就是每年新增的路灯数也有300万盏。至2010年，中国路灯的现存数量已经达到2700万盏。在桥梁、隧道灯方面，按照《公路隧道设计规范》的设计标准，隧道照明每10米按装一盏照明灯具（两边共2盏），目前我国现存隧道灯数量达到624万盏。在未来5年的道路建设发展规划和发展速度计算，我国将新增道路照明灯具共1500万盏（按照年增加300万盏计算），隧道灯将增加6万盏（按照总长增加300公里计算）。四、关于路灯方面节能减排的发展思路近年来，随着我国城市建设规模的不断扩大和建设水平的不断提高，作为城市建设的一项重要内容，城市道路照明、

三维地形建模技术标准

上海勘测设计研究院企业标准 Q/SIDRI1XX.XX－2014 三维地形建模技术标准 XXXX-XX-XX发布XXXX-XX-XX实施发布

目录前言 ......................................................................................................... I 1 总则 (1) 2 术语 (1) 3 工作环境 (1) 4 数学基础 (2) 5 原始地形图规定 (2) 6 建模规定 (3) 7 成果要求 (4) 8 交付与使用 (5)

前言本标准是参照SL1—2002《水利技术标准编写规定》进行制订，是我院企业技术标准编写的依据。本标准由上海勘测设计研究信息与数字工程中心提出。本标准主编部门：信息与数字工程中心本标准参与部门：勘测院本标准主要起草人：方毅本标准于2014年7月首次制定。

1 总则 1.0.1 目的为了落实公司的发展规划，推动三维协同设计的应用，提升公司信息化水平，为了保障三维地形建模工作的顺利进行，规范其建模流程，方便后续专业进行三维设计工作，以提高整个团队的工作效率，特制定本标准。 1.0.2 适用范围本标准适用于所有项目中三维地形模型的建立、应用和管理。 2 术语 2.0.1 DTM Digital Terrain Model，数字地面模型，本公司的三维地形建模就是指建立数字地面模型。 2.0.2 高程从某一基准面起算的地面点的高度，我国采用的是水准高程，即基准面为似大地水准面。 2.0.3 等高线指的是地形图上高程相等的各点所连成的闭合曲线。 3 工作环境 3.0.1 使用软件三维地形建模使用的软件主要是Mircrostation、GeoPak以及AutoCAD。 3.0.2 专业环境使用GeoPak建立DTM模型时，工作环境执行如下规定：

中国中小企业发展现状与未来前景分析

中国中小企业发展现状与未来前景分析中国的民营中小企业差不多都是由个体户、夫妻店和家庭作坊演变而来。由于失业和再就业的压力，总会有大量下岗和失业人员寻求创业的途径和机会，因此个人和家庭创业然后形成小企业将是中国长期而普遍的现象，研究小企业生存和发展的模式，以及政府需要为之提供的政策环境，对中国经济发展和社会稳定具有十分重要的现实意义。下岗和失业人员本身处于弱势地位，我们不可能对其专业素质期望太高，也不能指望在比较短的时间内能通过培训使其成为具有竞争力的企业家。因此，小企业成长需要政策和体制上的帮助。在小企业的发展中有必要克服当前流行的一个错误观点，即小企业做大了就是成功。报告认为，小企业是一种企业形态，有其自身的特性和生存规律，从国内外历史上看，家庭作坊也有百年老店，证明小企业有自己的成功之路。小企业变成大企业只是一种变化，不能作为成功的标志，大企业也有倒闭的，企业的规模与其成功与否没有直接关系。另外，小企业的管理模式并不复杂，往往是由经营者直接面对员工、面对客户，所以经营者的素质就等于是企业的素质。小企业主未必都有作大的志向(尽管这种志向并不重要)，但一定都有多盈利的愿望，政府的一切政策法规和支持措施应以帮助小企业盈利为出发点，抓住这个要点，并以此为中心展开促进小企业发展的各项工作，就会形成小企业繁荣和成长的良好局面。政府不需要设定某种企业模式，也不需要设定企业成长的某种指标，政府的政策法规就是企业自我设计的重要参考因素。有时可以听到抱怨说小企业不注重品牌，不讲求信誉，报告认为不在乎自己形象的企业只能是少数，从一般经济理论分析可以看出，企业

的短期行为通常是由政府政策的短期行为引致，所以克服企业短期行为的最好办法是政府政策的长期稳定和前后一致。应该说，从中央政府到地方政府的方向性政策中，不管是提供市场准入和提供资金扶持方面，都有很好的法律和法规环境。现在的问题是在个体实施这些法律法规的过程中，尚有一些体制上的不配套、程序设置上的不到位以及更重要的一点即政府工作人员观念转变未完成。以体制为例，中国的金融体系原来完全服务于国有特别是大型国有企业，在银行自身的商业化改造中，也是注重于银行自身风险的防范和提高盈利能力，还没有来的及改革银行乃至整个金融体系使之能够服务于各类企业特别是中小企业。尽管在中央政府的指示下，各大银行均表示要为中小企业融资提供帮助，但完成整个面对小企业服务体系的设计和安排肯定要花费很长的时间。前任中国人民银行行长戴相龙先生在十六大之前的一次讲话中明确了中国金融系统目前的重要工作之一是完成针对中小企业的金融服务体系改革，预示着中小企业的融资状况在不远的将来会有所改善，但在现行体制下中小企业的资金紧张状况还会再持续一段时间。另外一个重要问题是中小企业如何面对政府政策的变化和政府部门的管理。中小企业是中国新生的经济门类，政府的政策、法规和体制必然是随着小企业的成长壮大而不断地制定、修改、完善和调整，换句话说就是存在边制定边修改的情况，这就会给小企业带来很大的压力。如上述，小企业的特点就是人数比较少，不能象大企业那样可以设立专门的部门或人员负责政府相应部门的联系和协调工作。因此，小企业在忙于自己生意的同时，就难于拿出许多时间奔波于政府的各个职能部门之中，而且即使这样，也未必跟得上一些政策法规的变化。这种情况一方面增加了小企

地形三维建模

实验三地形三维建模实验内容： 1、以实测高程点为基础数据，在Cass中制作地形三维模型。 2、以实测等高线为基础数据，在ArcGIS中制作地形三维模型。主要操作步骤： 1、获取实测高程点的坐标文件数据。（*.dat） 1）使用全站仪、棱镜等测量设备，在指定区域内实测若干高程点，并记录每个高程点的平面坐标及高程。注意：测量高程点时，每个点的间距在5米左右，均匀覆盖所测区域，测站时量测仪器高、棱镜高，输入测站点高程值。高程点数不少于60个。在测高程点的同时，兼顾地物的测量。线性地物数（道路、陡坎、沟渠）不少于5个。 2）实测结束后，将数据转换成Cass坐标文件（*.dat）在这里以CQSJ.dat数据文件为例 2、在Cass软件将高程点进行展绘，绘制成等高线。将绘制完成的数据保存为DGX.dwg。（本讲义以CQSJ.dat数据为例） 1）打开Cass，导入CQSJ.dat中的高程点选择“绘图处理—》展高程点”菜单，依次输入绘图比例尺“1:500”，高程点的间距“1”米，即可展绘文件中的高程点。

选择“等高线—》建立DTM”菜单，构建三角网。

再选择“等高线—》绘制等高线”菜单，生成等高线

再选择“等高线—》删三角网”，删去三角网。

3）修饰等高线在图上标注相应等高线的高程值 4）绘制其他地物（道路、陡坎、沟渠等）注意：线性地物穿过等高线时，等高线要断开。 5）完成后，保存为DGX.dwg文件。 3、在Cass中进行地形三维建模使用“等高线—》三维模型—》绘制三维模型”菜单，选择高程点数据文件CQSJ.DAT。依次输入高程乘系数（默认是1.0，此值是高程值的缩放比例，如果高程值的变化不大，可适当输入较大的系数，三维地形的起伏将比较明显，本例中输入5），输入网格间距（默认是8.0，绘制网格的大小，可根据需要进行调整），选择进行拟合。即可看到地形的三维模型，由于此处的高程乘系数为5，地形起伏得到放大，显得比较明显。

利用Smart3D建模软件生成三维地形过程精编版

利用Smart3D建模软件生成三维地形过程本篇经验将和大家介绍以一组无人机倾斜摄影照片为原始数据，通过Smart3D 建模软件，重建生成三维地形的过程，希望对大家的工作和学习有所帮助！工具/原料 ?包括Smart3D建模软件 ?一组垂直拍摄而且多角度、重叠度满足重建要求的航片 ?航片对应的pos数据文件概况关于通过无人机航拍的照片，照片进行三维重建生产模型，一些情况下照片中是自带有GPS数据信息的，而另一些情况则是会导出一组无定位信息的照片和对应的pos数据文本。前者我们直接新建区块，把照片直接导入给软件跑出结果就ok了。那么，这次我们主要来谈论研究第二种情况，即照片和pos分开的情况。 END 区块导入表格的编辑区别于第一种情况我们需要编辑下导入区块的表格，我们将照片的文件路径、参考坐标系、传感器的基本信息等信息嵌入到这个表格里，通过它来实现对照片和pos信息数据的导入。后面的操作处理是跟直接导入照片的方法是没有差别的。首先，我们看到原始数据的文件夹如下图所示，包括一组照片和相应的pos 文件，如下图所示：

1. 2 可以看到，这个pos数据是以文本文档的形式存在，如下图所示： 3 而在导入区块的过程当中，我们需要导入Excel表格，那么，这时需要运用一定的办公软件的技巧将其转换为Excel表格，这个表格需要包含如下图的4个工作表，如下图所示： 4 结果如下图所示： 5 Photogroups工作表中，名称列需要与照片工作表的PhotogroupName一致，如下图所示：

6 Photos工作表的编辑结果，如下图所示： 2.7 控制点工作表中，由于无人机航拍的区域不是很大，且对于建模成果的精度没有设定范围，追求建成模型的速度，我们本次先不设控制点，很多朋友都是误把照片放到了这个工作表中，致使处理出现问题，需要注意一下。编辑结果，如下图所示： 8 Options工作表中，是坐标系和照片路径的信息，设置如下，如下图所示：

医药工业洁净厂房设计规范

医药工业洁净厂房设计规范 Jenny was compiled in January 2021

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————————————————————————————————作者：————————————————————————————————日期： 2

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