Design of Industrial asorption reactive.pdf

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DESIGN OF INDUSTRIAL REACTIVE

ABSORPTION PROCESSES IN SOUR GAS

TREATMENT USING RIGOROUS MODELLINGAND ACCURATE EXPERIMENTATION

R. Thiele1,, R. Faber2, J.-U. Repke2, H. Thielert3 and G. Wozny2

1BASF AG, GCT/D L540, Ludwigshafen, Germany.2Technical University of Berlin, Germany.3Uhde GmbH, Dortmund, Germany.

Abstract: In this paper a rigorous rate-based model for the selective absorption and desorptionof sour gases in packed towers within a coke oven gas purification process (VACASULFw-process) is presented. It considers multi-component mass transfer of CO2, H2S, NH3 andHCN in aqueous potassium hydroxide (KOH) or potash (K2CO3) solutions and the influence ofchemical reactions on mass transfer using enhancement factors. A modified Pitzer model isused to estimate activity coefficients in the electrolyte solution for the calculation of the chemicaland the phase equilibria as well as the reaction kinetics (Bronsted-Bjerrum equation). Using datafrom the literature this thermodynamically consistent approach was successfully validated for thephase equilibria and the reaction kinetic calculation. Since no experimental data for the above-mentioned chemical system in packed towers is available in the literature, own experiments werecarried out in a pilot plant packed tower. A newly developed experimental method using redun-dant measurements and a Data Reconciliation procedure improved the accuracy of concen-tration profiles for the gas and the liquid phase. Using eight absorption and eight desorptionexperiments, as well as industrial on-site measurements of the entire plant, the model was suc-cessfully validated. In addition, the model was used for global optimization using evolutionaryalgorithms, revealing an optimization potential of 30% in operating costs.

Keywords:packed towers; reactive absorption; gas purification; sour gas treatment; masstransfer.

INTRODUCTION

In the industry design of gas purificationplants is still often done using empiricalmethods and simple spreadsheet calcu-lations. Thus, finding an efficient design forthe units and optimal operating parametersfor the entire process is not straightforward.In this joint research project between an

academic and an industrial partner, reactiveabsorption and desorption processes are rig-orously modelled using thermodynamic andchemical engineering fundamentals takinginto account the complex reaction systemand its effects on mass and heat transfer(Thiele et al., 2003a; Thielert, 1997).

Frequently installed processes like theammonia-hydrogensulphide scrubbing pro-cess and amine-based processes havealready been modelled with great success(Brettschneider et al., 2004). However,models for processes with potash like theVACASULFw process are still not available.

During the start-up of a recently built industrial

VACASULFw plant, the experimental resultsshowed a surprisingly large increase in effi-ciency for a specific alkaline concentrationrange, which was not predicted by the empiri-cal methods in the design stage.

A literature review revealed that in thepast investigations focussed on absorptionof CO2 in highly concentrated solutions.Benson characterized the CO2 absorption

with high potassium carbonate concentrations(Bensonet al., 1954). This so-called BenfieldProcess was for the first time rigorouslymodelled in 1981 (Joshi et al., 1981) andlater extended with the insertion of dissolvedarsenic as promoter for an enhanced CO2absorption (Kumar and Rao, 1989). In 1990a model based on kinetic and equilibriumdata was developed to calculate the CO2absorption and desorption rates in a highlyloaded potassium solution (Pohorecki andKuckarski, 1990). A rate-based model for theBenson Process based on effective interfacialarea to calculate the mass transfer was also

developed by Cents (Cents et al., 2001).

74 Vol 85 (A1) 7487

Correspondence to:

R. Thiele,BASFAG, GCT/D

L540, 67056, Ludwigshafen,

Germany.

E-mail: robin.thiele@

basf.com

DOI: 10.1205/cherd06091

02638762/07/$30.00 0.00

Chemical Engineering

Research and Design

Trans IChemE,

Part A, January 2007

# 2007 Institution

of Chemical Engineers


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In contrary to the extensive modelling of the BensonProcess, a model for processes with a low concentratedpotash absorbent for the selective removal of H2S andHCN like the VACASULFw process (Currey, 1995) is stillnot available. Therefore, a rigorous rate-based non-equilibrium model for a more complex electrolyte system(NH3-CO2-H2S-HCN-H2O-KOHNaOH) with chemical reac-

tions taking into account the influence of chemical reactionson mass transfer using enhancement factors was built atthe Technical University of Berlin (Thiele, 2007).

RATE-BASED MODEL

For simulating this absorption/desorption process,commercial simulators based on either equilibrium or non-equilibrium models (without reactions), fail to predict theselective absorption and desorption of H2S and HCN overCO2. CO2-absorption is seriously overestimated by an equili-brium model due to the slow dissociation reactions of CO2inwater and the also kinetically controlled reaction between

NH3 and CO2. A selective absorption of H2S compared toCO2during short contact times in packed towers could there-fore not be predicted with the abovementioned models.Therefore, the development of a complex heat and masstransfer model taking the interactions of the different chemicalspecies and their reactions into account was conducted usingFORTRAN. An overview for one transfer unit of the model isshown in Figure 1 and will briefly be explained in the nextsubchapters. More details on the model can be found inThiele (2007).

A comparison of the simulation results of an equilibriummodel and a rigorous rate-based model with the measure-ments of a pilot plant experiment is shown in Figure 2.

Electrolyte System

The given reactive system of weak and strong electrolytesis described by considering the following reactions with thedissociation constantsKj.

Instantaneous reversible reactions:

2H2O!K1

H3O OH (R:1)

NH3 H2O!K2

NH4 OH (R:2)

HCO3 H2O!K3

CO23 H3O (R:3)

H2S H2O!K4

HS H3O (R:4)

HS H2O!K5

S2 H3O (R:5)

HCN!K6

H CN (R:6)

HCN H2O!K6

CN H3O

Kinetically controlled reactions:

CO2 OH!

K7HCO3 (R:7)

NH3 HCO3 !

K8H2NCOO

H2O (R:8)

Strong electrolyte:

KOH!K9

K OH (R:9)

The also occurring reaction CO2 2H2O!HCO23 H3O

is very slow compared to (R.7) especially in alkaline sol-utions. Its contribution to the overall rate of reaction is there-fore small and can be neglected (Cents et al., 2001).

To calculate the concentrations of molecular and ionicspecies in solution the chemical equilibria are expressedwith their dissociation constants Kj.

Kj Ync

i1 a

ni,j

i Ync

i1 m

ni,j

i g

ni,j

i (1)

where ai is the activity of component i, ni,j is the stoichio-metrical coefficient of component i in reaction jand gi is theactivity coefficient taking non-idealities into account.

Figure 1. One segment of the rate-based model including chemical reactions.

Trans IChemE, Part A, Chemical Engineering Research and Design, 2007, 85(A1): 7487

DESIGN OF INDUSTRIAL REACTIVE ABSORPTION PROCESSES IN SOUR GAS TREATMENT 75


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The chemical equilibrium between the different species inthe bulk liquid is solved by the POWEL routine from the MIN-PACK library. Then the result for the pseudo and molecularcomponents is passed to the equations of the transfer unit(Figure 1) to calculate the mass transfer. Knowledge of theconcentrations of the different dissociated species is alsoespecially important to calculate the enhancement factors.

Phase Equilibrium

The equilibrium at the interface between the molecularspecies is calculated using the Henry coefficient for dissolveddiluted gases as the standard-state fugacity.

xi gi HeH2O,i yi Fi P (2)

with the Henry coefficient HeH2O,i of the component i (CO2,H2S, HCN, NH3) in water and the fugacity coefficientFithatconsiders the non-ideality of the gas phase. The equilibriumfor water is calculated using the vapour pressure instead ofthe Henry coefficient.

In this work the correlations and parameters of Edwards

(Edwardset al., 1975, 1978) are used to calculate the activitycoefficients using a modified Pitzer approach and the Henrycoefficients as well as the activity-based dissociation con-stants Kj from the previous subsection. In addition binaryinteraction parameters for the different pairs of potassiumsalts are taken from Pitzer and Mayorga (1973), Roy et al.(1984), Simonson et al. (1987) and Engel et al. (1997).

Hoogendoornet al.(1988) found the vapour phase fugacitycoefficients to differ not more than 1% (2% for water) fromunity for pressures less than 0.2 MPa. Therefore, they wereset to unity.

Mass and Heat Transfer

For the non-equilibrium model the column is divided intostages. In each segment the vapour and the liquid phase is

considered separately regarding balances for mass, com-ponents and enthalpy. Mass transfer between both phasesover the interfacial area is evaluated with the film theory,which assumes one-dimensional diffusion in the film and acompletely mixed bulk phase. The cross-interactions of theGeneral Maxwell Stefan theory can be neglected due tothe low concentrations (Peters, 1997). The convective termis also neglected.

The mass transfer rate J of component i for segment j isthen calculated by

Ji,j kvi,j r

vj aj (y

bi,j y

inti,j)

kli,j rl

j aj Ei,j (xinti,j x

bi,j) (3)

i:H2O, CO2, H2S, HCN, NH3:

The mass transfer coefficients ki,jv and ki,j

l for the gas andliquid film are calculated from correlations developed byBillet and Schultes (1999). The interfacial areaajis calculatedfrom the same contributions. The increase in concentrationgradients at the interfacial area due to chemical reactionleads to an enhancement of mass transfer which isaccounted for by the enhancement factor Ei,j, calculatedfrom correlations also applied by Hoogendoorn et al.(1988).He used enhancement factors for reversible reactions offirst order and pseudo-first order derived from the surfacerenewal theory by Danckwerts (1970).

Analogous to the mass transfer models, enthalpybalances are formulated separately for the liquid and thevapour phase and the interface, thus, considering the heattransfer over the interface. Liquid and vapour enthalpiesare calculated from Pawlikowski and Newman (1982) andCoxet al.(1989) and the liquid and vapour side heat transfercoefficients are obtained from the weighted corresponding

component mass transfer coefficients using the Lewisequation.

Figure 2. Comparison experiment-model (rate-based and equilibrium), absorption in 32 g l21KOH, F 1.8 Pa1/2, B 9 m3 m22 h21.


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Reaction Kinetics

The kinetics for the reactions (R.7) and (R.8) are takenfrom Pinsent et al. (1956a). The reaction rate constantsk1OH2are given for infinite dilution.

The reaction rate for (R.7) is then given in the form

_rR:7

kOH

cOH

cCO2

(4)

Pohorecki and Moniuk (1988) measured the influence ofthe ionic strength and the type of the ions in solution onthe reaction rate constant kOH2. For the absorption of CO2in potassium hydroxide solutions he found the followingrelationship:

log(kOH=k1

OH) 0:287I0:013I2 (5)

Since this equation can not be applied to mixed solvents theBronstedBjerrum equation is used. It describes the reactionbetween an ion B having charge zand a neutral molecule Awhich reacts to an ion via an intermediate state (Bronsted,

1922; Bjerrum, 1924; Pohorecki and Moniuk, 1988):

A Bz!(AB)z! products (6)

The reaction rate constant, according to the BronstedBjerrum equation, is then for (R.7) equal to

kOH k1

OHgCO2

gOH

gHCO3

(7)

Since the activity coefficients can also be calculated for com-plex solutions, the influence of the non-ideality of the liquidphase on the rate constant is accounted for by the thermo-dynamic model which is also used for the calculation of the

chemical equilibrium and the phase equilibrium. Thus a con-sistent thermodynamic approach is maintained throughoutthe model. Haubrock et al. (2005) used a similar equationas (7), however he only included the activity coefficients forthe reactants. Using this approach he was able to decreasethe influence of the ionic strength, however a ratio of 2 forkexpOH2/kOH2 for intermediate ionic strength still persists.

Numerical Methods and Implementation

The entire program is written in FORTRAN and inte-grated into ChemCAD

TM

as a User Added Module. This

allows to build the entire process of a plant in the flowsheetsimulator. Standard Units like Heat Exchangers, Splitter,Mixers and Pumps are taken from the ChemCAD library.These units require flash calculations for the given system.Using the built-in methods (Sour water and ELECNRTL)using Version 5.2 the system is either not supported orcannot be reliably calculated regarding computation time and

convergence behaviour. Thus, the above presented chemicaland phase equilibria calculations are used by inserting aUser Added K-Value calculation method (ADDK) implementedin FORTRAN into ChemCAD. Which leads to a consistentthermodynamic model throughout the entire flow sheet.

VALIDATION OF THE PHASE EQUILIBRIUM ANDTHE CALCULATION OF THE REACTION KINETICS

To validate the presented thermodynamic modelling ofthe phase equilibrium and the reaction kinetics data fromthe literature is used. Since absorption behaviour in theVACASULFw process is mainly governed by the interplay of

CO2 and H2S with the alkaline potash solution, thesecomponents are considered in the validation of the partialpressures. Concentrations of NH3 are considerably low inthe entire process. Off-gas specifications for HCN are usuallymet before the specifications for H2S are met.

For the relevant concentration region for KOH of0.5 mol l21 K (resp. of 30 g l21 KOH) experimental liquidvapour-equilibrium data are given in Dryden (1947). InFigure 3 the data in terms of partial pressure match wellwith the simulated results for the phase equilibrium of themodel. Indeed, for higher concentrations of CO2 and H2Sthe accuracy is decreasing. Nevertheless, these resultsshow that regarding the phase equilibrium, as a decisivefactor for column performance prediction, a reliable basis

for a solid simulation of the entire column is provided.For the validation of the reaction kinetics the measure-

ment results from Pohorecki and Moniuk (1988), whichhave been correlated giving equation (5), are used. Theresults for CO2 in a KOH solution with different ionic strengthregarding the reaction rate constant of reaction (R.7) isshown in Figure 4 in comparison to the literature results.

As can be seen, the thermodynamic model using theBronsted Bjerrum equation (7) is able to reproduce thenon-ideality in the liquid phase regarding the reactionkinetics. It can be expected that the model is also able tocalculate the increase in the reaction rate due to the

Figure 3. KOH-CO2-H2S equilibrium data from Dryden compared to simulation results.




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existence of different ions in solution for mixed solvents, as

it occurs in the VACASULFw

Process.

PILOT PLANT

For the experimental validation of the simulation a pilotplant at the TU Berlin was used which has been designedfor both absorption and desorption experiments underatmospheric pressure and vacuum conditions. It consists

of a stainless steel column ( 100 mm) equipped withthree sections of packings each of 856 mm height. Thestructured packing used is a SULZER Mellapak 350.Y. Thepilot plant is described in detail in Brettschneider et al.(2004) for the absorption mode. The entire experimental pro-cedure including measurement errors is described in detailin Thiele (2007).

The pilot plant is automated by the process control systemFreelance2000

TM

by ABB. Liquid samples can be taken atthree different heights and are analysed under wet conditions(measurement errors for CO2: 1%, H2S: 2%, NH3: 5%, KOH:1%). Gas samples can also be drawn at four different pos-itions and are analysed by a gas chromatograph (measure-ment error: 10%). In addition to the analytical methodpresented in Brettschneider et al. (2004) potassium isanalysed by titration with sodiumtetraphenylborate. Thecolumn is equipped with a glass section for optical examin-ations. A vacuum pump behind the exhaust gas scrubbersallows pressures down to 70 mbar in desorption mode.

In absorption configuration (Figure 5) the gas stream is

mixed inline from gas bottles. The CO2 concentration ismeasured online using an infrared CO2 analyser fromMAIHAK and is kept constant by a controller. In desorptionmode (Figure 6) steam is generated by a reboiler fromdesalinated water and a condenser at the top of thecolumn partially condenses the vapours coming from thetop of the column. Inerts, mostly CO2, are withdrawn fromthe condenser to maintain a constant pressure at the topof the column.

Figure 4. Reaction rate constants for absorption of CO2 in KOH forreaction (R.7) and activity coefficients.

Figure 5. Flowsheet of the pilot plant in absorption mode. Desorption equipment is hidden.


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DATA RECONCILIATION METHOD

Since all concentrations of both the liquid and the gasphase are known for the three packing segments componentbalances can be made (Figure 7). This allows the reconcilia-tion of the measurement variables involved, which doesnot require any process knowledge except that mass ispreserved.

fj,k(x) Ljcj,k Lj1cj1,k Vj1yj1,k Vjyj,k (8)

kKOH, CO2, H2S, NH3, L liquid flow,

cliquid concentration, jenvelope 1, 2 and 3,

Vgas flow, ygas concentration

Large deviations in the component balances of up to 20%which cannot be attributed to measurement errors suggestthat the measurements might contain gross errors. It wasfurther assumed that the measurement values which are

subject to error are not normally distributed. Thus, a utiliz-ation of weighted least squares is not recommendable.

Figure 6. Flowsheet of the pilot plant in desorption mode. Absorption equipment hidden.

Figure 7. Balance envelopes for the data reconciliation method.




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Instead a robust estimator, the Fair-function is used (Ozyurtand Pike, 2004). The Data Reconciliation problem can thenbe formulated as follows:

min JminXnmvi1

c2j1ij

cln 1

j1ij

c

1i xi ^xi

sixi [ V,L,c,y (9)

s.t. f(x) 0

Lb xUb

The Fair function introduces simultaneous gross error detec-tion into the data reconciliation (DR) process. This can beshown by the influence function (Figure 8). The contributions

from the Fair function for larger standard errors are signifi-cantly lower than for the weighted least squares (WLS)function.

The resulting optimization problem [equations (8) and (9)]consists of 31 variables and 12 equality constraints and isnon-linear. It can be solved using an NLP-Solver like theSQP-Method. The DR problem including the solver methodhas been implemented in MATLAB (Faberet al., 2004).

By applying the DR method the quality of the measurementdata is expected to be improved, which will be validated in thenext section.

MODEL VALIDATION USING PILOTPLANT EXPERIMENTS

The validation of the developed model was carried out usingeight desorption experiments and eight absorption experi-ments for which the DR was carried out. Experiments wereeither conducted under vacuum conditions at 150 mbar or at

mean overpressure of about 1100 mbar (abs.), thus, enablingto validate the model in a wide pressure and therefore also in awide temperature range. The concentration ranges of thecomponents KOH and CO2 were chosen according to theVACASULFw process. At first only a H2O-KOH-CO2 solutionwas used as an absorbent. In further experiments thesystem was extended with H2S and finally with NH3. Thus,the influence of individual components on the model accuracyfor both absorption and desorption can also be identified. Allexperiments conducted within the project are presented indetail in (Thiele, 2007). In this paper, three desorption andthree absorption experiments are exemplarily presented.

The entire feed and operating data, which are required tocarry out the simulation and the measured flows and concen-

trations of the outlet streams are presented in Table 1 (deso-rption experiments) and Table 2 (absorption experiments).During the absorption experiments the gas stream enteringthe column at the bottom is saturated in the exhaust gasscrubbers at the temperature called Presat. temp. inTable 2. Using this temperature the water concentrationwhich is also important for the absorption process and theresulting temperatures can be calculated.

The liquid concentration profiles and the temperatureprofiles for the desorption experiments in comparison tothe simulated values achieved by the model are given inFigures 9 11. For all experiments a good agreement isobtained between the experiments and the simulation results

both the concentrations and the temperature measurements.For the complete system H2O-KOH-CO2-H2S-NH3and deso-rption under vacuum which is relevant for the VACASULFw-process (Figure 10) the simulation results match well withthe experimental data within an average relative errorbelow 20%. The model is able to reproduce the selective des-orption of H2S over CO2. The simulated temperature profilealso fits closely to the experimental values, hence, confirmingenthalpy and heat transfer model.

The results for the absorption experiments are shown inFigures 1214. Besides the liquid concentrations and

Figure 8. Influence function for different objective functions.

Table 1. Experimental parameters for the three desorption experiments (D1, D7 and D8 from Thiele, 2007).

Case 1 (D1) Case 2 (D7) Case 3 (D8)

Process data Feed Outlet Feed Outlet Feed Outlet

VapourFlow (kg h21) 20 15 34Temp. (8C) 56.87 53.62 56.37 53.45 103.22 102.05

LiquidFlow (l h21) 99.98 101.9 99.78 99.35 99.82 103.52Temp. (8C) 45.04 54.58 45.14 54.59 80.2 103.0

CO2 (g l21) 27.52 23.55 19.89 18.08 20.15 14.58

H2S (g l21) 2.12 0.049 2.53 0.624

NH3 (g l21) 4.93 0.096 5.11 0.0017

KOH (g l21) 35.73 33.89 30.98 30.3 29.90 28.09Plant data

Top pres. (mbar) 148.8 152.9 1123.8

Pres. drop (mbar) 9.7 5.9 3.12F-factor (Pa0.5) 2.16 1.61 1.47


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Table 2. Experimental parameters for three absorption experiments, results of the DR in brackets (A2, A6, A8 from Thiele, 2007).

Case 4 (A2) Case 5 (A6) Case 6 (A8)

Process data Feed Outlet Feed Outlet Feed Outlet

VapourFlow (Nm3 h21) 47.09 (47.07) 46.55 (47.33) 46.88 (48.84) Temp. (8C) 25.38 26.92 25.06 24.88 25.00 25.39Presat. temp (8C) 23.77 24.35 19.78 CO2 (Vol%) 2.383 (2.209) 1.754 (1.749) 2.393 (2.335) 2.369 (2.323) 2.012 (2.131) 2.173 (2.072)H2S (Vol%) 0.410 (0.404) 0.0100 (0.0035) 0.514 (0.561) 0.095 (0.088)NH3 (Vol%) 0.9 (1.26) 0.0050 (0.0025)

LiquidFlow (l h21) 69.95 (69.82) 70.37 (70.50) 69.95 (70.00) 70.83 (70.75) 69.94 (70.00) 70.59 (70.47)Temp. (8C) 25.02 27.87 25.02 25.81 24.98 28.93

CO2 (g l21) 5.97 (5.97) 12.04 (12.04) 12.38 (12.38) 12.59 (12.59) 17.33 (17.33) 18.45 (18.44)

H2S (g l21) 0.25 (0.25) 4.32 (4.32) 0.87 (0.92) 6.94 (5.92)

NH3 (g l21) 0.00 (0.00) 7.27 (6.60)

KOH (g l21) 35.84 (34.42) 33.42 (34.09) 28.48 (28.58) 28.35 (28.28) 31.98 (32.19) 31.92 (31.97)Plant data

Top pres. (mbar) 1123 1094 1110Pres. drop (mbar) 6.34 6.45 6.11F-factor (Pa0.5) 1.80 1.80 1.80

Figure 9. Desorption experiment case 1 (D1).





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Figure 12. Absorption experiment case 4 (A2).


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temperatures the gas concentrations are also measured forthe absorption experiments. Thus, the data reconciliationcan be carried out and the figures show both the originalmeasurements, as well as the reconciled variables. It canbe seen that the data reconciliation leads especially for thegas concentration measurements of CO2 to significantimprovements. The measurement error for the gas chromato-graph is approx. 10% which leads to large deviations inthe mass balances [equation (8)], which is corrected bythe DR method. The DR method does not only change themeasurement values throughout the column but alsothe feed streams. Thus, the simulations for comparison withthe DR results have also to be conducted using these recon-ciled feed streams. Due to the complex interactions in theelectrolyte system the change in the feed composition ofone component can also lead to improvements for othercomponents.

Table 3 shows for all 8 absorption experiments the averageand maximum deviations between the simulation and themeasurements for both the original measurements and thereconciled measurement data. The DR method leads to sig-

nificant improvements for nearly all components: e.g., themaximum deviation for the gas concentration of CO2 is

reduced from 14.66% to 3.27% and the average deviationfor KOH is reduced to only 0.37%.

It can be concluded, that the newly developed experimen-tation concept using redundant measurements and the DRcan provide an improved data basis for validating modelsor to estimate important model parameters.

The absorption results show that a reliable prediction ofthe chemical absorption using the model is possible. Again,the model is able to predict the selective absorption ofH2S over CO2. Average deviations are below the range of510% (compare Table 3).

Further investigations also focus on the influence ofsurface active components on hydrodynamics in packedtowers which can cause foaming (Thiele et al., 2003b, 2004).

INDUSTRIAL PLANT

In addition to our own pilot plant experiments, validation ofthe model was also done using measurement data from anindustrial VACASULF process of Citizens Gas & Coke Utilityin Indianapolis, Indiana. The plant has been described in

Currey (1995). Measurements were taken in 1997 during astart-up phase by Uhde GmbH. The data was then processed





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using component balances showing that these industrialmeasurement data are questionable. The best data set,which still showed deviations in the different component bal-ances of up to 30%, was chosen to be used for the simulationusing a flowsheet simulation with CHEMCAD with the pre-sented rate-based model as tower units. All design par-ameters from the process (tower diameters, heights and

internal types) were known and put into the simulation. Theprocess can be seen in Figure 15.

The results show that, considering the obvious gross errorsin the measurements the model is able to predict the indus-trial process with a good accuracy. The deviations betweenthe predicted and the measured data can be seen inTable 4. Larger deviations do occur for small concentrations,but the associated component streams are considerablysmall. Since the simulation results are in good agreement

with the expectations of the process engineers, the modelfor the entire process can also be considered as validated.

PROCESS OPTIMIZATION BASED ONOVERALL COSTS

The validated rate-based model was then used in thecommercial flowsheet simulator CHEMCAD to optimise theentire process. Therefore the flowsheet simulator wasconnected to an external evolutionary strategy implementedin Visual Basic and Excel which had been developedbefore in Brettschneider (2003) and Thiele et al. (2003c)and is shown in Figure 16. Ten process parameters (flows,

KOH-conc., temp, stripper pressure) and five design par-ameters (absorber and stripper heights, feed side stream


Table 3. Improvements using redundant measurements and the datareconciliation method for eight absorption experiments.

Rel. deviation liquid (%) Rel. deviation gas (%)

DR exp MaxDR Maxexp DR exp MaxDR Maxexp

KOH 0.37 1.24 1.09 8.25CO2 3.37 3.08 12.24 12.16 1.28 4.53 3.27 14.66H2S 4.94 6.85 13.96 32.24 21.41 36.91

a 63.92 168.41NH3 6.66 37.66 18.44 54.57

aSmall concentration (,20 ppm) neglected.


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heights) where chosen as decision variables for the optimiz-ation routine. The objective function was based on annual-

ized costs comprising both investment and operating costs.Costs functions were derived using confidential cost datafrom the industrial partner for running plants regarding oper-ating costs and from realised projects regarding investmentcosts. Investment cost functions are simple linear or degres-sive functions. Parameters for the evolutionary algorithmwere 10 individuals per generation, out of which two parentswere selected which gave by linear recombination the newparent. The new generation was obtained by normal distribu-ted mutation of the parent.

For an operated industrial VACASULFw plant similar toFigure 15 the entire optimization resulted in a decrease ofthe operation costs by 30% as can be seen from Figure 17.The optimizer decreased the concentration of the potash

solution which leads to a more selective absorption of H2Sin the process due to the lower concentrations of OH- and

thus lower CO2 reaction and absorption rates in the lowerconcentrated solution. The flow rate of the normal-strippedsolution is increased which is responsible for the bulkremoval of H2S. In contrary, the flow of the highly-strippedsolution is decreased. The resulting high ratio of strippingsteam to liquid leads to a efficient use of reboiler duty anda high regeneration efficiency for the highly-stripped solutionin the bottom of the stripper. The highly-stripped solution isthen used for fine removal of H2S in the top of the absorberto met off-gas specifications.

RESULTS

For the design and the optimization of the VACASULFw-process, a selective absorption and desorption-process, anew model has been presented. The model was successfullyvalidated by vapourliquid equilibrium data and reactionkinetics from the literature and by the conducted eightdesorption experiments and eight absorption experiments inthe packed tower. The developed experimental procedureincluded redundant measurements and a data reconciliationmethod to improve the accuracy of the measurement dataand also automatically detected gross errors. The modeluses a thermodynamically consistent approach for both thechemical and phase equilibria as well as the reaction kinetics.For the first time, it was shown that the BronstedBjerrumequation is able to reproduce the non-ideality in the liquid

phase regarding the reaction kinetics for the formation ofbicarbonate using a modified Pitzer approach.

Figure 15. VACASULF process.

Table 4. Validation using data from the industrial process.

DeviationsSimulation $ Process

Rich potash solution CCO2 29%CH2S 1%CHCN 23%

Normal stripped CCO2 213%Potash solution CH2S 6%

CHCN 65% (low conc.)

Highly stripped CCO2 219%Potash solution CH2S 219%

CHCN 113% (low conc.)

COG H2S gas exit 253.8% (low conc.)




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In addition, the model was used in the commerciallyavailable flowsheet simulator CHEMCAD as a User AddedModule to simulate the entire VACASULFw process. The pre-dicted values compare well with the industrial data. It is nowbeing used in industrial practice for the design of the individ-ual units and the overall process and can also be utilizedonline for model predictive control. Thus, the project allowedfor the first time the simulation of the entire process (VACA-SULFw) with recycles using rigorous models and itsapplication in industrial practice.

Furthermore, the industrial process was systematicallyoptimized regarding annual costs using an evolutionary strat-egy. This resulted in a decrease of 30% in operating costs stillcomplying with the restrictions for the gas outlet concen-tration. For a new process the heights of the absorber and

stripper were significantly reduced resulting also in adecrease in investment costs.

NOMENCLATURE

ai activity of component Iaj effective interfacial area for segmentj, m

2 m23

E enhancement factorJ mass transfer rate, mol s21

k reaction velocity constant, 1 s21

K equilibrium constantL liquid molar flow, mol h21

mi molality of component I, mol kg21P pressure, PaV vapour molar flow, mol h21

xi mole fraction of component iin liquidyi mole fraction of component iin vapour

Greek symbols1 standard errorgi activity coefficient of component Ini,j stoichiometric coefficient of component iin reaction jr density, kg m23

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ACKNOWLEDGEMENTS

The support provided by VDKF e.V. and Chemstations Inc. isgratefully acknowledged, as well as founding by DeutscheForschungsgemeinschaft for the partner project DFG LI806/4-3that partially supported this work. The work presented in this paperhas been conducted during the previous position of Robin Thiele atthe TU Berlin. Statements and opinions presented in this paper arenot done in the name of BASF AG.

The manuscript was received 11 July 2006 and accepted for

publication after revision 24 October 2006.



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