Optimization of an acidic chlorine scrubber with a rate-based simulation engine

European Symposium on Computer Aided Process Engineering - 11 R. Gani and S.B. Jorgensen (Editors) 9 2001 Elsevier Science B.V. All rights reserved.

535

Optimization of an Acidic Chlorine Scrubber with a Rate-Based Simulation Engine W. Steinbach, A. Friedl., H. Hofbauer Institute of Chemical Engineering, Vienna University of Technology Getreidemarkt 9/159, 1060 Wien, Austria email: wstein(afriedl,hhofba)@mail.zserv.tuwien, ac.at The absorption of chlorine from an exhaust gas stream into an aqueous acidic solution of ferrous chloride is modeled. Chemical reaction kinetics as well as mass and heat transfer is taken into account to determine the rate of absorption. The calculation is performed by the AspenPlus - RateFrac TM simulation engine using the ELECNRTL property method. Chemical property data is checked and several parameters based on the electrolyte NRTL activity coefficient model are regressed. A sensitivity analysis is carried out to optimize the operating conditions and the design of a random packed column with respect to the off-gas concentration of chlorine. 1 INTRODUCTION Chlorine is a poison gas, that must be removed from exhaust gases of many processes down to a legal limit of concentration. In steel industry, where HC1 is used to remove rust and scale from the metal surface, C12 can be formed while regenerating the used acid. Therefore a scrubber fed with an alkaline solution of sodium hydroxide and thiosulfate is provided. This chemicals are not needed if the iron-II containing pickling solution itself is used as washing liquid. Because reactions in electrolytic systems are usually fast and the absorption can not be calculated with an equilibrium model, the design of the column in dimension and pumparound depends on the kinetics of the process. Thus, a rate-based simulation taking into account the three terms of kinetics, mass transfer, heat transfer and chemical reaction kinetics will lead to a successful prediction of the absorption process. The scrubber is a random packed column with counter current flow as shown in Figure 1. The gaseous inlet stream can consist of typical combustion gas containing H20, N2, 02, CO2 and small amounts of HC1 and C12. The liquid inlet stream is the spent pickle liquor containing H20, HC1, FeC12 and small amounts of FeC13. Temperature is considered to be 85~ for the gas and for the liquid, the scrubber works at atmospheric pressure.

536 ILIQIN I

9

GAsouT

9

I~,ooo, J

Fig. 1: Principal flowsheet of a chlorine-scrubber 2 THERMODYNAMIC AND TRANSPORT PROPERTIES

AspenPlus TM recommends a predefined property method called ELECNRTL which was used in the simulation. The physical models contained in this property method are listed in table 1. Table 1 Summary of physical models used Common

Vapor pressure Heat of vaporization Surface tension

Vapor mixture Fugacity coefficient, Density Enthalpy, Entropy, Gibbs energy Vapor viscosity Vapor thermal conductivity Vapor diffusivity

Extended Antoine Watson/DIPPR Hakim-Steinberg-Stiel/DIPPR- Onsager-Samara Redlich-Kwong Ideal gas heat capacity / DIPPR, Barin correlation, Redlich-Kwong Chapman-Enskog-Brokaw Stiel-Thodos / DIPPR Dawson-Khoury-Kobayashi

537

Liquid mixture Activity coefficient, Gibbs energy Liquid molar volume Infinite dilution heat capacity Enthalpy, Entropy

Electrolyte NRTL, Extended Antoine, Henry's constant, Brelvi-O'Connell Rackett, Clarke Criss-Cobble Ideal gas heat capacity / DIPPR, Watson / DIPPR heat of vaporization, Criss-Cobble infinite dilution heat capacity, Electrolyte NRTL Rackett / Clarke Andrade/DIPPR- Jones-Dole Sato-Riedel/DIPPR- Riedel Wilke-Chang - Nemst-Hartley

Density Liquid viscosity Liquid thermal conductivity Liquid diffusivity

Reprinted from AspenPlus T M Handbook, 1999 As AspenPlus extends the calculation of physical properties from binary mixture to multi component mixtures the property data for the simulated system must be checked by literature [2,3]. Parameters of the electrolyte NRTL model were correlated to literature data from [3] for the systems H20-C12 and HzO-HC1-CI2. TM

100

80 ~ B

80-

SystemH20-CI2

9

=i4o

0

E E

+,

SystemH20-HCI-CI2

60-

,ooc

..

40-

I--.--.I

-r 20

-1-

20-

50~

i

20

I

I

I

40

60

80

100

0 I 0

T [~

, 4

, 8

HCI [ m o l l l ]

Fig.2,3" Henry's Constant; Comparison of literature data [3] and AspenPlus results after regression.

3 ABSORPTION MODEL The overall reaction of chlorine is described as follows.

12

538

C12 + 2 Fe 2+ :=> 2 C I + 2 Fe 3+

The kinetic of this irreversible reaction is known to be in the second order (first order with respect to both C12 and Fe2+-ions) [1 ]. By analyzing Hatta's number for irreversible secondorder reaction with reasonable values, it can be found, that the absorption kinetic of chlorine is determined by mass transfer and not by reaction kinetics. Mass transfer coefficients and the interfacial area available for mass transfer in packed columns is calculated using the correlation developed by [4].

{lexpl 14 .e~ k t = 0,051. (Retw) 2/3 9

1/2

.(ap .dp) ~

(1)

"/g'/~t 1 / 3 p/ t

kg = 5,23 . (Reg )~ . (Scg,C12 )l~3 . (ap . dp ~ 2 .

(2)

a p . D g,cl2

R.rg

(3)

The heat transfer coefficient is calculated, using the Chilton-Colbum analogy [5]. kavSc2/3=

(4)

htc Cp,mix

The dissociation of HC1, FeC12 and FeC13 are defined by the following reaction equations. HC1 + H20 ~ FeC12 ~

CI + H 3 0 +

Fe 2+ + 2 CI

FeC13 ==:, Fe 3+ + 3 CI

4 OPTIMIZATION With the method of sensitivity analysis, where you vary one or more variables over a wide range of values you can get a very clear picture of the optimal design parameters and operation conditions. Because the height of a column is often limited due to construction reasons, the variables remaining for this optimization are the column diameter, the liquid pumparound and the size

539 of the packing. The packing is only available with certain sizes, thus two variables, column diameter and pumparound, are varied. The efficiency of absorption is defined by the relation of incoming to absorbed amount of chlorine. The results are shown in the figures 4 to 7. Table 2 Legend for figure 4 to 7

_Symbol

E ~

A 1 9 9

E

97 % 94 % 91% 88%

5

%" 100

4

~

8o

,ag I1) o')

m r

3

60

2

40

r~

"5 -J

"-i

0 0

0

,

0

Gas

3 4 Charge [kg/rn=s]

dv =

1 inch

Fig 5: dv = 2 inch

1

Fig. 4:

20

2

'~n 20

~ ' 6000

E

E

~15

~

~-~

,--, 4000

~. 10

--

.I=

o 5

3

4

5

2000

-~

9 ~ ,

, m

, m

--

2

~"

o 9~

1

Gas Charge [kglm=s]

, ~ ,

0

-0

1

2

3

4

Gas Charge [kglmZs] Fig. 6:

dp =

1,5 inch

5

0 0

1

2

3

4

Gas Charge [kglm=s] Fig 7:

dp =

3,5 inch

5 CONCLUSIONS The results of the simulation show that the influence of the packing diameter, which is correlated to the specific interfacial area by equation (1), has an extremely strong influence on the performance of the absorber with a given gas charge. This significant results would not have been obtained with a equilibrium based simulation. An absorber could only be designed with a lot of experience and oversizing. The rate-based

540 simulation as shown in this practical example gives us the opportunity to design a scrubber near to its optimum. REFERENCES 1. 2. 3. 4. 5.

H. Hikita et al., Chem. Eng. Sci., 30 (1975) 607. F. Hine, et al., Bull. Chem. Soc. Jpn., 41 (1968) 71. C.C. Chen and L.B. Evans, AIChE J., 32 (1986) 444. Onda et al., J. Chem. Eng. Japan, 1 (1968) 56. F. King, M. Spiro, J. Solution Chem., 12 (1983) 65.