Rigorous Kinetic Modeling and Optimization Study of a Modified Claus

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Rigorous Kinetic Modeling and Optimization Study of a Modified Claus Unit for an Integrated Gasification Combined Cycle (IGCC) Power Plant with CO2 Capture Dustin Jones,†,‡ Debangsu Bhattacharyya,*,†,‡ Richard Turton,†,‡ and Stephen E. Zitney† † ‡

U.S. Department of Energy, National Energy Technology Laboratory, Morgantown, West Virginia 26507, United States Department of Chemical Engineering, West Virginia University, Morgantown, West Virginia 26506, United States ABSTRACT: The modified Claus process is one of the most common technologies for sulfur recovery from acid gas streams. Important design criteria for the Claus unit, when part of an Integrated Gasification Combined Cycle (IGCC) power plant, are the ability to destroy ammonia completely and the ability to recover sulfur thoroughly from a relatively low purity acid gas stream without sacrificing flame stability. Because of these criteria, modifications to the conventional process are often required, resulting in a modified Claus process. For the studies discussed here, these modifications include the use of a 95% pure oxygen stream as the oxidant, a split flow configuration, and the preheating of the feeds with the intermediate pressure steam generated in the waste heat boiler (WHB). In the future, for IGCC plants with CO2 capture, the Claus unit must satisfy emission standards without sacrificing the plant efficiency in the face of typical disturbances of an IGCC plant, such as rapid change in the feed flow rates due to loadfollowing and wide changes in the feed composition because of changes in the coal feed to the gasifier. The Claus unit should be adequately designed and efficiently operated to satisfy these objectives. Even though the Claus process has been commercialized for decades, most papers concerned with the modeling of the Claus process treat the key reactions as equilibrium reactions. Such models are validated by manipulating the temperature approach to equilibrium for a set of steady-state operating data, but they are of limited use for dynamic studies. One of the objectives of this study is to develop a model that can be used for dynamic studies. In a Claus process, especially in the furnace and the WHB, many reactions may take place. In this work, a set of linearly independent reactions has been identified, and kinetic models of the furnace flame and anoxic zones, WHB, and catalytic reactors have been developed. To facilitate the modeling of the Claus furnace, a four-stage method was devised so as to determine which set of linearly independent reactions would best describe the product distributions from available plant data. Various approaches are taken to derive the kinetic rate expressions, which are either missing in the open literature or found to be inconsistent. A set of plant data is used for optimal estimation of the kinetic parameters. The final model agrees well with the published plant data. Using the developed kinetics models of the Claus reaction furnace, WHB, and catalytic stages, two optimization studies are carried out. The first study shows that there exists an optimal steam pressure generated in the WHB that balances hydrogen yield, oxygen demand, and power generation. In the second study, it is shown that an optimal H2S/SO2 ratio exists that balances single-pass conversion, hydrogen yield, oxygen demand, and power generation. In addition, an operability study has been carried out to examine the operating envelope in which both the H2S/SO2 ratio and the adiabatic flame temperature can be controlled in the face of disturbances typical for the operation of an IGCC power plant with CO2 capture. Impact of CO2 capture on the Claus process has also been discussed.

’ INTRODUCTION Integrated gasification combined cycle (IGCC) is a promising technology for generating clean, affordable, and secure power. However, a coal-fed IGCC power plant has lower net plant efficiency compared to a conventional natural gas combined cycle (NGCC) power plant. A NETL study shows that the net plant efficiency of a coal-fed IGCC plant with a General Electric Energy (GEE)-type gasifier is 38.2%, compared to 50.8% for NGCC plants.1 Moreover, the net plant efficiency of the IGCC plant further decreases to 32.5% when the CO2-capture option is considered. To make IGCC technology more competitive, efforts must be exerted to improve its efficiency. Bhattacharyya et al.2 carried out an optimization study for improving the overall efficiency of an IGCC plant with CO2 capture. A slightly modified version of the plant layout considered in that study is shown in Figure 1. The study considered a GEE-type entrained flow gasifier mainly because of its high carbon conversion rates and environmental advantages.2,3 r 2011 American Chemical Society

Figure 1 shows that the shifted syngas from the gasifier goes to a dual stage Selexol solvent unit for acid gas removal (AGR). The stripper off-gas from the Selexol unit, rich in hydrogen sulfide, is sent to the Claus unit, and carbon dioxide is sent for compression and sequestration. The off-gas from the sour water treatment unit, rich in ammonia, is also sent to the Claus unit. Environmental targets for the IGCC are shown in Table 1.1 Single pass conversion of hydrogen sulfide to sulfur in a dual stage Claus process is approximately 96%. To meet the environmental target on SO2 given in Table 1, 99.9% sulfur recovery is required. To accomplish this level of sulfur recovery, a hydrogenation unit is required.4 The tail gas from the Claus process is Received: August 2, 2011 Accepted: December 15, 2011 Revised: December 14, 2011 Published: December 15, 2011 2362

dx.doi.org/10.1021/ie201713n | Ind. Eng. Chem. Res. 2012, 51, 2362–2375

Industrial & Engineering Chemistry Research

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Figure 1. Block flow diagram of IGCC power plant with carbon capture.2

Table 1. Environmental Targets of IGCC1 pollutant NOx

environmental target 15 ppmv (dry) @ 15% O2

SO2

0.0128 lb/MMBTU

particulate matter

0.0071 lb/MMBTU

mercury

90% capture

treated in a hydrogenation unit, compressed from about 1 to 50 atm, and recycled back to the AGR unit for recapture. In the AGR plant, a higher operating pressure results in a lower flow rate of the circulating solvent that decreases the refrigeration, pumping, reboiling, and equipment cost by increasing the partial pressures of CO2 and H2S.5 But the compression cost for recycling the tail gas from the Claus process increases. For improving the overall efficiency of the IGCC plant, this compression cost should be optimized (reduced) without violating the environmental targets. Other important design criteria for the Claus unit, when part of an IGCC power plant with CO2 capture, are the ability to destroy ammonia completely and recover sulfur thoroughly from a relatively low purity acid gas stream without sacrificing flame stability. A rigorous kinetic model is needed to design a plant that can satisfy these design criteria. Several papers have been published on the modeling of the Claus process or specific unit operations of the Claus process.1,2,69 However, most of these modeling efforts rely on restricted equilibrium models to predict product distribution from the Claus furnace. One exception that could be found is the work of Nasto et al.6 Nasto et al. focused on the modeling of the waste heat boiler (WHB) with a reaction set consisting of H2S pyrolysis and COS formation reactions. However, considering only these reactions cannot fully describe the WHB, as these reactions would not capture any changes in sulfur dioxide composition across the WHB, which is shown to take place from plant data.10 There have been several papers published on the kinetics of the Claus furnace and WHB that attempt to determine the

kinetics of the system.1117 Despite this, there is still uncertainty in reaction pathways and some reactions that lack rate expressions. In the work of Nasto et al.6 for modeling the WHB, the reactions considered were only the hydrogen sulfide pyrolysis and carbonyl sulfide formation reaction of carbon monoxide reacting with sulfur. Additionally, only the WHB was modeled and the feed composition and properties were taken as the measurements of Sames et al.10 from the anoxic region of the furnace. However, as these two reactions do not fully define the system, additional linearly independent reactions must be considered to derive a consistent kinetic model of the Claus furnace. Little or no methodology is available in the open literature that addresses the selection of such a kinetic model. To facilitate the modeling of the Claus furnace, a four-stage method has been proposed, so as to determine which set of linearly independent reactions best describe the product distributions from available plant data. Using this method, an optimal set of linearly independent reactions was determined and used to model the Claus furnace and WHB. Rate expressions were fitted when they were not available within the open literature. There have been no studies found within the open literature that discuss the optimal operation of the Claus plant as part of an IGCC power plant. It is generally accepted that optimal operation occurs when the H2S/SO2 ratio in the feed to the tail gas treatment unit is as close as possible to 2, so as to maximize the single-pass conversion of hydrogen sulfide.18,19 However, in the operation of an IGCC power plant, there are other considerations in addition to single-pass conversion. The Claus process yields hydrogen and raises considerable amounts of steam through the recovery of waste heat. Additionally, as these studies consider the use of a highly enriched oxygen stream (95 mol %) from an air separation unit as the oxidant to the furnace,20 the cost associated with that oxygen production should also be considered. Therefore, the effect of oxygen flow and steam pressure on these variables was examined. Little information could be found in the open literature on the topic of operational limits for the Claus process. It was desired to 2363

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Figure 2. Simplified flowsheet of Claus and hydrogenation units.

determine the limiting conditions for maintaining both adiabatic flame temperature and H2S/SO2 ratio without violating safety constraints. Configuration and Kinetic Modeling of the Modified Claus Process. As shown in Figure 2, the process begins with the Claus reaction furnace, which is also referred to as the thermal stage. In the reaction furnace, the hydrogen sulfide contained in the acid gases is combusted to form sulfur dioxide via reaction 1a. This gas then passes through the WHB to generate intermediate-pressure (IP) steam to be used for the heating duties required in the process. The process gas is then further cooled to condense the sulfur formed during the thermal stage. Approximately 60% of the inlet sulfur gets converted in the thermal stage.18 The gas is then reheated with the steam generated in the WHB and sent to the first of two catalytic stages where hydrogen sulfide and sulfur dioxide react in a 2:1 ratio to form sulfur and water via reaction 1b. The overall sulfur chemistry of the Claus process can be shown as reaction 1c. H2 S þ

3 O2 f SO2 þ H2 O 2

2H2 S þ SO2 T 2H2 O þ H2 S þ

3 Sn n

1 3 O2 f Sn þ H2 O 2 n

ð1aÞ ð1bÞ ð1cÞ

Reactions 1a1c represent the overall chemistry of the Claus process; however, the actual chemistry is much more complicated. One of the major purposes of this paper is the determination of the most likely pathways for the formation and destruction of species important in the Claus process. For operations with acid gases whose hydrogen sulfide composition is greater than 50 mol %, a straight-through configuration is generally chosen.18 However, a split-flow configuration is generally required to address the challenges that can arise when hydrogen sulfide concentrations in the acid gases are less than ∼50 mol %. In the split-flow configuration, a fraction of the acid gas bypasses the burner so that the adiabatic flame temperature is greater than approximately 930 °C to ensure flame stability.18,21 In addition, a split-flow configuration is also sometimes required when processing a sour water stripper gas that has a high concentration of ammonia. To prevent the plugging of downstream reactors caused by ammonia slip through the furnace, high furnace temperatures are required to ensure the complete destruction of ammonia.18 Because a limit exists on the amount of acid gas that can be bypassed, other options are often required

to ensure sufficiently high flame temperatures.18 The options considered here are the preheating of all feed streams and the use of an oxygen-enriched stream instead of air. Because of these modifications to the conventional Claus process, this process is also referred to as a “modified Claus process.” The process gas that exits the waste heat boiler passes to a cooler to condense and separate the sulfur. The process gas is then reheated and sent to the first of two catalytic reactors where hydrogen sulfide and sulfur dioxide react in a 2:1 ratio to form sulfur and water. After reacting in the catalytic reactor, the gas is then cooled to condense and remove sulfur and sent to the second catalytic stage. After the second stage, the tail gas is sent to a hydrogenation unit. This unit converts all remaining sulfur species to hydrogen sulfide. This gas is then cooled to remove the water and compressed and recycled to the Selexol unit. Flame Zone. A zoned approach was taken in the modeling of the Claus furnace. A simplified diagram of the zoned approach is shown in Figure 2. The first zone is an adiabatic flame zone that is modeled as a plug flow reactor. The linearly independent reactions considered for this zone are the oxidation reactions shown in Table 2. Accurately predicting the relative extents of reactions occurring in the flame zone is important for predicting the adiabatic flame temperature and the consumption of oxygen. Ensuring that no oxygen slips into the anoxic region is also an important safety related issue because of the possibility of a flashback in the bypassed acid gas.18 Anoxic Zone and WHB. A wide number of possible reactions that may occur in the anoxic and WHB section of the Claus furnace are reported in the literature.12,24 For this work, a set of linearly independent reactions that would best describe the product distribution at the outlet of the WHB was desired. In the open literature, little or no results have been presented on methodologies to select such a set of linearly independent reactions. In this paper, a four-stage method was devised to determine which set of linearly independent reactions would best describe the product distributions from available plant data. The first stage of this method is to determine the maximum number of linearly independent reactions. The first stage is based on the work of Mah and Aris.25 Let us consider that there are S reactive chemical species consisting of N atomic species in a system. In a data set p, if mi represents the amount of atomic species i, yj is the number of chemical species j ,and bij represents the number of atoms i in species j, then m ¼ By

ð2Þ

Then, the maximum number of linearly independent reactions is Rmax ¼ S rankðΒÞ 2364

ð3Þ



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Table 2. Reactions and Kinetics Occurring in the Flame Zonea reaction H2 S þ

3 O2 f SO2 þ H2 O 2

r ¼

NH3 þ

3 3 1 O2 f H2 O þ N2 4 2 2

r ¼

CH4 þ 2O2 f CO2 þ 2H2 O

H2 þ

1 O2 f H2 O 2

r ¼

CO þ

1 O2 f CO2 2

r ¼

ref 11 12 22 23 22

Activation energies are in kcal mol1, partial pressures are in atm, and concentrations are in mol cm3.

ΔY ¼ αζ

ð4Þ

Note that ζ is unknown and some of the elements of α may be known from experimental studies and/or literature data but, in general, the matrix α is either partially or completely unknown. However, because of experimental errors ΔY exp ¼ αζ þ ε

ð5Þ

where each column in the error matrix ε represents the experimental errors in a data set. An optimization problem is then formulated and solved to find the optimal estimate for the unknown elements in α and ζ min s:t:

kΔY ΔY exp k ΔY ¼ αζ Βα ¼ 0

ð6Þ

where the second constraint is required to ensure the conservation of atoms. Here, no bound on ζ is considered because the equilibrium reactions can yield negative values. More constraints

can be considered in this formulation. In addition, partial or full information of α can be provided, if known. The minimum value of the objective function achieved through this formulation is the best possible solution that can be obtained in the presence of experimental errors. In the third stage of this approach, a database of the possible reactions is prepared on the basis of the open literature and experimental data. From this database, R reactions are considered, thus forming a number of reaction stoichiometric matrices; that is, Q ,q = 1:Q. Then, the following quadratic programming (QP) problem is solved: min s:t:

kΔY q ΔY exp k ΔY q ¼ αq ζq ζij g 0, i ∈ ψ, j ∈ ω ζij e 0, i ∈ ε, j ∈ χ

ð7Þ

where ψ, ω, ε, and χ are finite sets of indices, i ∈ ψ ∪ ε are vectors with w elements, and w e R and j ∈ ω ∪ χ are vectors with n elements and n e S. The bounds on elements of ζ are based on experimental data and/or process knowledge. Here, the constraint on the atom balance is not considered, as it is assumed that it has been satisfied while creating the reaction database. If the relationship shown in eq 8 is satisfied by a stoichiometric coefficient matrix αq, then that stoichiometric coefficient matrix is retained for the fourth stage of the analysis. min kΔY q ΔY exp k min kΔY ΔY exp k e tol

ð8Þ

Here, the tolerance, tol, can be low initially but can be relaxed at a later time if performance in the fourth stage is found to be poor. In this way, a list of stoichiometric coefficient matrices is generated, and they are ranked in ascending order based on their ΔY q ΔY exp values. Note that the solution of this QP problem is computationally inexpensive, but it can drastically reduce the computational load in the next stage. If there are V reactions in the database, then the reaction sets ! extracted for V consideration are expected to be Q , because, for R certain chemical species, the choices for reactions will be limited. This point will be further clarified while developing the model of the Claus process. The values of ΔY q were generated without any consideration for the kinetics, thermodynamics, or hydrodynamics of the actual system. Therefore, these values may not be physically realizable or feasible. Hence, in the fourth stage, the

2365

)

If a S S matrix ΔY exp is constructed from the experimental data, where each column represents a particular experimental data set exp exp,in where yexp,in represents the such that Δyexp jp = yjp yjp jp number of the chemical species j at the inlet in the data set p, then the number of reactions actively taking place, R, is given by rank (ΔY exp). However, one may not have S sets of experimental data. In addition, determination of rank is an involved process in the presence of experimental error, especially for large systems. In this case, a qualitative approach can be taken based on the literature data and/or experimental observations. This point will be further clarified as the method is applied to the Claus plant model. Once the independent number of reactions, R, is determined, the important step is to determine a consistent set of reactions that can address the experimental data. In the existing literature, this determination is based mainly on a qualitative argument relying on an inspection of the experimental data. In the discussion that follows, a quantitative method is proposed that systematically addresses the issue of the correct set of reactions, and this method is then applied to the Claus plant. In the second stage, the best possible prediction of the experimental data is determined in the presence of experimental error/ noise. If the extent of a reaction r in a data set p is given by ζrp, and αjr represents the stoichiometric coefficient of chemical species j in reaction r, then

)

a

r ¼

reaction rate [mol/(cm3 s)] 11:0 PH2 S P1:5 14 exp O2 RT 40:0 PNH3 PO0:75 4430 exp 2 RT 48:4 0:2 1:3 CCH4 CO2 6:7 1012 exp RT 30:0 CH2 CO2 1:08 106 exp RT 40:0 0:25 CO2 CCO C0:5 3:98 1014 exp H2 O RT



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)

min ΔY q,act ΔY exp )

)

min ΔY q ΔY exp )

reaction sets considered 10c, 10d, 10f, 10g

3.80

6.26

10b, 10c, 10f, 10g

3.80

5.71

10b, 10f, 10g, 10h

3.80

6.89

10a, 10b, 10f, 10g

3.82

5.98

10b, 10d, 10f, 10g

4.87

6.26

10a, 10d, 10f, 10g

5.02

6.32

10d, 10f, 10g, 10h

5.02

7.47

10a, 10b, 10f, 10h 10b, 10c, 10f, 10h

6.98 6.98

10c, 10d, 10f, 10h

6.98

10a, 10d, 10f, 10h

7.37

10b, 10d, 10f, 10h

9.01

10a, 10c, 10f, 10h

34.60

10a, 10f, 10g, 10h

34.60

10a, 10c, 10f, 10g

34.76

min

kΔY q, act ΔY exp k

s:t:

hðxÞ ¼ 0 υðxÞ e 0 xL e x e xU

x∈Rn

1 3 SO2 S S2 þ H2 O 2 4

H2 S þ

ð10bÞ

1 S2 2

ð10cÞ

H2 þ

ð10dÞ ð10eÞ

CO2 þ H2 S CO þ H2 O

ð10f Þ

1 S2 S COS 2

ð10gÞ

CO þ H2 S S H2 þ COS

ð10hÞ

1 3 NH3 f N2 þ H2 2 2

ð10iÞ

NH3 þ

ð9Þ

where the nonlinear equality constraints are due to the process model that includes the kinetic expressions for those reactions determined by the choice of αq. The inequality constraints may appear based on the process system. The decision variables would be the kinetic parameters of one or more reactions that are bounded by the limits of the reported experimental errors. There can be additional decision variables, for example, some parameters such as heat transfer coefficient, heat loss, etc. that may not be known exactly. The lead coefficient matrix from stage three is examined first. If its corresponding ΔY q,act is lower than the next ΔY q in the list from stage three, a feasible reaction set is found. Otherwise, the search continues. It can take several hours of simulation time to solve the above optimization problem for each of the coefficient matrices, especially if any of the following occur: the number of decision variables is large; there are large plug flow reactors considered, each with multiple reactions; or a sequential modular approach is used to solve the simulation. The list prepared in stage three can significantly reduce the number of reaction sets considered in the fourth stage. For the kinetic modeling of the modified Claus process, all four stages of the approach were applied. In the first stage, it was found that, Rmax = S rank(B) = 13 5 = 8. A total of only five experimental data sets were found in the open literature.8 However, based on the open literature, the number of reactions that are reported to take place for this system is more than eight;13,14,17,24 therefore, it was assumed that R = Rmax. The second stage optimization was carried out in the numerical computing software package MATLAB. In this optimization formulation, the entire matrix α is unknown, along with ζ.

1 1 SO2 S S2 þ H2 O 2 4

CH4 þ H2 O f CO þ 3H2

CO þ

q,act

ð10aÞ

H2 S þ SO2 þ H2 S S2 þ 2H2 O H2 S S H 2 þ

feasible instances of ΔY , denoted by ΔY , are identified by considering the configuration and design of the actual process system along with kinetic modeling of the reactor. Here, the missing kinetic parameters, if any, are estimated first. Then, the nonlinear programming (NLP) problem described in eq 9 is solved: q

)

It was found that min ΔY ΔY exp = 3.53 . In the third stage, a list of possible reactions from the open literature were considered; they are shown as reactions 10a10l.12,24 )

Table 3. Reaction Sets and Objective Function Values

3 3 3 1 SO2 f S2 þ H2 O þ N2 4 8 2 2

ð10jÞ

3S2 S S6

ð10kÞ

4S2 S S8

ð10lÞ

From these reactions, a number of reaction sets, each of which are comprised of four reactions, are considered and are shown in Table 3. Only four reactions are needed for this examination, as there is no methane or ammonia, reducing S by two. Also, the plant data from Sames et al.10 considers all sulfur species as S, reducing the number of chemical species by another two. It should be noted that it was assumed that all sulfur existed as S2 for the studies discussed in this section. The bounds on the elements of ζ are based on plant data from Sames et al.8 and the thermodynamics of the problem. The saturation temperature of water in the WHB is 488 K, and the process gas exit temperature is about 527 K. In addition, the rate of reaction will decrease with a decrease in temperature, and the reactions are not very exothermic. Therefore, the decrease in the temperature of the process gas along the length of the WHB can be considered monotonic. If δ is some tolerance, and if S αjr Δ Q j ¼ 1 Cj eδ ΔðKeq Þr

ð11Þ

then a constraint is imposed that the rth reaction could only proceed in the direction of the equilibrium shift. The above relation was computed from the experimental data for all the equilibrium reactions, assuming a linear temperature and composition profile. However, if the above relation was not true for a given reaction, the constraint was not imposed. The left-hand side of inequality 11 gave values ranging from 0.53 for reaction 10c 2366



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Figure 3. Equilibrium predictions vs plant data from the outlet of the WHB using reaction set 10b, 10c, 10f, and 10g, where dotted lines show (20% error.

)

)

)

form. For reaction 10e, rate parameters from Zhang et al.28 were used. The equilibrium constants used for reactions 10b, 10d, and 10h are derived from the equilibrium constants used for reactions 10a,15 10c,16 and 10g14 to ensure consistency between all reactions. The results of these optimizations are shown in Table 3. Each reaction set in Table 3 was optimized until a reaction set was found whose value of min ΔY q,act ΔY exp was lower than the min ΔY q ΔY exp of the next reaction set. The reaction set consisting of reactions 10b, 10c, 10f, and 10g is found to be the best set, as seen in Table 3. A comparison of equilibrium predictions and plant data are shown in Figure 3. The results of this optimization for this best reaction set are shown in Figure 4, and the respective errors are given in Table 4. As shown in Figures 3 and 4, the proposed kinetic model of the furnace and WHB provides superior predictions to a simple equilibrium approach. Rate equations found from the optimization of reaction set consisting of reactions 10b, 10c, 10f, and 10g are shown in Table 5. It should be noted that the degree of constraint does not appear to affect the reaction set that is selected. As an example, the reaction set consisting of reactions 10b, 10d, 10f, and 10g is the least constrained reaction set that is considered. However, this reaction set ranked only fifth out of the seven sets examined. Additionally, the activation energy for reaction 10c that was used for the modeling of the furnace, throughout the rest of this paper, did not change from the value reported by Hawboldt et al.,16 even though the bounds on the activation energy were set equal to those reported in the paper.16 ! ð0:79 lnðReD Þ 1:64Þ2 PrðReD 1000Þ 8 NuD ¼ !1=2 ð0:79 lnðReD Þ 1:64Þ2 1 þ 12:7 ðPr2=3 1Þ 8 )

to 1 1021 for reaction 10h. Neglecting reaction 10c, the average value for the remaining reactions was approximately 103. Using a value of δ = 102, the constraint regarding the direction of the equilibrium shift was not considered for reaction 10c. The constraint was imposed for rest of the reactions. Therefore, the extents of reaction for reactions 10a, 10f, and 10h were constrained to values less than zero. Reactions 10b, 10d, and 10g were constrained to values greater than zero. A list of all the possible linearly independent combinations of reactions from the list above is shown in Table 3. As the plant data showed no methane or ammonia entering the WHB and all sulfur species were considered as S, reactions 10e and 10i10l were not considered. The reaction sets are arranged in Table 3 in ascending order on the basis of the minimum values of the objective functions, as explained previously. By using a value of tol = 1.5, only the first seven reactions are considered in the fourth stage analysis, but the values for all possible reaction sets are listed in Table 3 for completeness. In the fourth stage, the WHB was modeled in Aspen Plus as an ideal, multitube, plug flow reactor with constant coolant temperature and constant heat transfer coefficient. The optimization problem in eq 9 was solved by using the Aspen Plus’s optimization toolbox. The decision variables were the pre-exponential factors and activation energies of the reactions considered, shown in Table 3. In addition, the heat transfer coefficient of the WHB was also considered to be a decision variable. An initial estimate of the heat transfer coefficient was calculated using the Gnielinski correlation26 assuming smooth pipes, eq 12. An initial estimate was found by assuming that all resistance to heat transfer was on the tube side. The limits for the heat transfer coefficients were considered to be (20% of the calculated values from eq 12. No bounds were given on the pre-exponential factors of any reaction. For reactions 10c,16 10f,13 10g,27 and 10h,27 activation energies were allowed to vary between the error bounds given in the papers in which they were reported. For reactions 10b and 10d, which have no rate expressions available within the open literature, no bounds were provided for the activation energy. It was also assumed that reactions 10b and 10d had an elementary

ð12Þ Kinetic Modeling of Ammonia Destruction Reactions. The experimental data sets from Sames et al.10 that were used above 2367



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Figure 4. Model predictions vs plant data from the outlet of the WHB using reaction set 10b, 10c, 10f, and 10g, where dotted lines show (20% error.

Table 4. Percentage Differences between Model Predictions and Plant Data data set

1

2

H2S

10.5

6.4

27.6

7.2

SO2

15.8

2.5

7.7

18.1

0.8

2.5

20.1

39.3

20.5

6.0

19.6

3.8

32.4

15.4

30.4

0.8

11.5

2.3

0.9

11.6

15.0

10.8

2.7

5.2

12.6

H2 CO COS S

3

4

5 6.0

did not contain ammonia in its feed. However, complete ammonia conversion is one of the critical aspects of a Claus process design whenever it is present in the feed to the Claus process. In this section, the appropriate reaction(s) and its (their) kinetic parameters to capture the ammonia conversion precisely are sought. However, the two ammonia conversion reactions, reactions 10i and 10j, in the set above are not linearly independent in the reacting system. The work of Clark et al.17 has shown that the reaction of ammonia with sulfur dioxide, reaction 10j, is more rapid than the pyrolysis of ammonia, reaction 10i, which they have also shown to be inhibited by the presence of water and hydrogen sulfide, two components that are in high concentrations in the Claus furnace. Therefore, reaction 10j was chosen, as Clark et al.17 have shown that this reaction is initiated at lower temperatures and that reaction 10i is strongly inhibited by water and hydrogen sulfide. The missing kinetic parameters for reaction 10j, an ammonia destruction reaction, are estimated first. For deriving a rate expression for reaction 10j, the published data from Clark et al.17 are used. As these experiments are carried out in a heated tubular reactor, an ideal plug flow reactor is used to model the reactor. For reaction 10j, the form of the rate expression is assumed to be that given in eq 13 where concentrations are in kmol/m3. rNH3

Ea1 m1 n1 ¼ A1 exp CNH3 CSO2 RT

ð13Þ

An optimal set of parameters for this rate expression were found using the Aspen Plus’s Datafit toolbox. The optimization algorithm in the toolbox attempts to minimize a sum of squares error function by manipulating specified decision variables. The reaction rate found is given as eq 14 and the comparison of the simulated ammonia conversion and experimental ammonia conversion is shown in Figure 5. 0 1 kcal 27:5 B kmol C 0:25 0:5 B mol C CCNH3 CSO2 B ¼ 22860 exp rNH3 3 A @ ms RT ð14Þ Catalytic Reactors. Unlike the reactions taking place in the reaction furnace, the reactions occurring in the catalytic reactors have been more thoroughly studied.7,29,30 The catalytic reactors convert the hydrogen sulfide and sulfur dioxide into sulfur and water. In addition, COS formed in the WHB is converted to hydrogen sulfide by hydrolysis. Reactions and rate expressions used for modeling the catalytic reactors are shown in Table 6. From Figure 2, it can be seen that any COS in the syngas will pass through the AGR and will be vented to atmosphere after passing through the combustion turbine and HRSG. In addition, any COS produced in the Claus unit that slips through the Claus process and hydrogenation unit will likewise be recycled to the AGR and be vented to the atmosphere. This represents a major source of sulfur emissions from the Claus plant. Therefore, the first reactor is generally run at higher temperatures than the proceeding reactors to ensure COS hydrolysis, even though this decreases the conversion of hydrogen sulfide by the Claus reaction due to equilibrium. For this study, it was assumed that these reactions are kinetically controlled. The sizing of the reactors is based on heuristics from Tong et al.7 Hydrogenation Unit. Inevitably, some sulfur species will not be converted to sulfur in the Claus unit. To mitigate sulfur emissions to the atmosphere, it is common to consider a tail-gas 2368



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Table 5. Reactions and Reaction Rates Used in Further Modelinga reaction rate [kmol/(m3 s)]

reaction H2 S þ SO2 þ H2 T S2 þ 2H2 O

! 26:0 1 2 PH2 S PSO2 PH2 r ¼ 3:583 107 exp PS2 PH 2O RT Keq

1 H2 S T S2 þ H2 2

! 45:0 1 PH2 S PS0:5 r ¼ 9:169 105 exp P P S H 2 RT Keq 2 2 ! 60:3 1 CCO CH2 O C0:5 r ¼ 1:515 1012 exp C H2 CO2 RT Keq C0:5 H2

CO2 þ H2 T CO þ H2 O

1 CO þ S2 T COS 2 a

r ¼ 1:0242 106 exp

13:5 RT

CCO CS2

1 0:5 C CCOS Keq S2

!

Activation energies are in kcal mol1, partial pressures are in atm, and concentrations are in kmol m3.

Figure 5. Comparison of simulated versus experimental ammonia conversion.

treatment unit. In this study, a hydrogenation unit is considered immediately following the Claus plant. The hydrogenation unit’s primary function is to convert all remaining sulfur species in the tail gas of the Claus unit back to hydrogen sulfide so that it may be recovered in the AGR. No rate expressions or experimental data for the reactions taking place in the hydrogenation unit could be found within the open literature. However, Massie31 claims that reactions will convert all of the sulfur compounds to hydrogen sulfide. For these reasons, the hydrogenation reactor is modeled as an equilibrium reactor comprised of reactions 15a15e. CO þ H2 O T CO2 þ H2

ð15aÞ

1 H2 S T H2 þ S2 2

ð15bÞ

1 H2 S T H2 þ S6 6

ð15cÞ

1 H2 S T H2 þ S8 8

ð15dÞ

3H2 þ SO2 T H2 S þ 2H2 O

ð15eÞ

It should be noted that it is unclear from the literature whether reaction 15a is catalyzed in the hydrogenation reactor. To clarify

the importance of reaction 15a in the hydrogenation reactor, an examination of its effect on the performance of the hydrogenation reactor was undertaken. As shown in Table 7, the effect of reaction 15a is quite significant on the performance of the hydrogenation reactor. If reaction 15a is catalyzed in the hydrogenation reactor, the hydrogenation of the remaining sulfur species present in the tail gas is nearly complete and requires no externally supplied source of hydrogen. In fact, the Claus and hydrogenation units are net hydrogen producers. However, if reaction 15a is not catalyzed, nearly all of the hydrogen produced in the furnace and WHB is consumed and a substantial quantity of the sulfur dioxide and elemental sulfur species still remain. Therefore, to attain the high level of sulfur capture required, an external source of hydrogen would be required to ensure the complete hydrogenation of all remaining sulfur species. Because of the need for an externally supplied source of hydrogen, the Claus and hydrogenation units will be hydrogen consumers. From the literature, it appears that a cobalt molybdenum based catalyst is often used in the hydrogenation reactor.31,32 From the work of Massie,31 it also appears that reaction 15a is not catalyzed. However, Rameshni32 reports that the cobalt molybdenum catalyst does catalyze reaction 15a. Traditionally, the catalyst used for the sour shift reactors is a cobalt molybdenum based catalyst.2 As the operating temperature of the hydrogenation reactor in this work (about 558 K) is higher than the inlet temperature of the sour shift reactors (about 490 K in the work of Bhattacharyya et al.2), it is anticipated that reaction 15a will take place in the hydrogenator. For this reason, reaction 15a is modeled in this work. Base Case Model Development of the Claus plant in an IGCC plant with CO2 capture. With the developed kinetic models of the Claus reaction furnace, catalytic reactors, and equilibrium models of the hydrogenation unit, operations of the Claus unit in an IGCC plant were examined. The base case feeds and compositions were based on ref 2 and are given in Table 8. The sizing of the reaction furnace is based upon the size of the furnace reported by Sames et al.,10 so that the base case residence time for this case is approximately the same as for the furnace in Sames et al.10 The WHB was sized based upon the information given by Nasato et al.6 The tube diameter was taken as 2 in., and the number of tubes was adjusted to give the same mass flux as reported by Nasato et al.6 As shown in Figure 6, the heat transfer coefficient of the WHB has a strong effect on the product distribution in the WHB. To capture the effects caused by changes in flow, composition, and temperature, a calculator block was used to determine the heat transfer coefficient of the 2369



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Table 6. Reactions and Rates Considered in Catalytic Reactorsa reaction

reaction rate [mol/(s kgcat)] 0 1 1 0:1875 0:5 B PH2 S PSO P P H O C 2 S 2 8 Keq 7:35 B C r ¼ 5364 exp 2 C B A RT @ 0:6 PH2 O 1 þ 1:140 exp RT 0 1 B C 13:84 B PCOS PH2 O C B C r ¼ 8:258 10 4 exp A 19:9 RT @ 5 PH2 O 1 þ 1:141 10 exp RT

1 3 H2 S þ SO2 T H2 O þ S8 2 16

COS þ H2 O T CO2 þ H2 S

a

ref 29

30

Activation energies are in kcal mol1, and partial pressures are in atm.

Table 7. Effect of Reaction 15a on Exit Flow Rates [kmol h1] from the Hydrogenation Reactor H2S

SO2

Svap 12

with reaction 15a

7.28

7.43 10

9.55 10

without reaction 15a

6.08

0.754

0.451

H2 3

17.7 9.73 104

Table 8. Base Case Feeds and Compositions for the Claus Furnace AGR off-gas

sour water stripper gas

temp. [°C]

48.9

119

pressure [atm] molar flow [kmol h1]

2.51 388

2.38 87.2

Composition [mol %] H2S

41.0

27.3

CO2

44.8

19.3

NH3

2.0

36.1

N2

8.1

1.5

CO

0.0

2.4

Ar

0.0

1.5

H2O H2

4.0 0.0

0.0 11.6

WHB using the Gnielinski correlation, eq 12. It was assumed that all resistance to heat transfer was from the process gas side and that the heat transfer coefficient was constant along the length of the WHB. The heat transfer coefficient was calculated from the inlet properties of the process gas. This gave a heat transfer coefficient that was approximately 49 W m2 s1 in most of the cases investigated and is marked with the dotted line in Figure 6. Figures 7 and 8 show the composition and temperature profiles of the WHB when the oxygen flow is manipulated to give an H2S/SO2 ratio of 2 at the outlet of the WHB, for the base case feed flows, compositions, and temperatures. These figures show that the reactions get quenched within about 40% of the WHB length, after approximately 0.29 s, as the temperature decreases below 873 K. The heat transfer coefficient can vary along the length of the WHB. The maximum variation is found to be about 40%. However, the majority of the composition changes occurring in the WHB occur at the entrance, where the heat transfer coefficient is calculated in this work. To keep the optimization study tractable, the heat transfer coefficient is assumed to be constant along the length of the WHB

Figure 6. Sensitivity of hydrogen yield and oxygen demand to maintain H2S/SO2 ratio to heat transfer of WHB.

Figure 7. Composition profiles in the WHB (with a heat transfer coefficient of 49 W/m2 K).

’ RESULTS AND DISCUSSIONS Little discussion is available within the open literature about the optimal operation or limiting operational conditions of a Claus unit as part of an IGCC power plant with carbon capture. Using the developed kinetic model, two optimization studies are 2370



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Figure 8. Temperature profile in WHB using a steam temperature of 268 °C.

presented that investigate the effect of the WHB steam pressure and H2S/SO2 ratio on the power output of the IGCC plant. An investigation of the limiting operational conditions of the Claus unit is also examined. Optimal WHB Steam Pressure and H2S/SO2 Ratio to the Tail Gas Treatment Unit. Equation 16 describes the effect the Claus unit has on the power requirements in the IGCC plant. It should be noted that eq 16 does not account for all power requirements and losses associated with the Claus unit in an IGCC plant with carbon capture. For example, it does not consider the baseline power requirement for recovering the hydrogen sulfide in the AGR, power loss associated with burning the carbon monoxide and hydrogen from the sour water shift in the furnace rather than the gas turbine, etc. However, eq 16 captures most of the trade-offs in the operation of a Claus unit. kmol PClaus ¼ 11:72_nO2 h kmol kmol þ 47:96_nH2 61:19_nH2 S h h þ Wtailcomp ½kW þ Wsteamturb ½kW

ð16Þ

The first term of the function describes the power requirement in the air separation unit (ASU) for producing the 95 mol % pure oxygen used as the oxidant for the Claus furnace. This power requirement is based on the power requirements of the main air compressors and oxygen compressors of the ASU. As the Claus process yields hydrogen as a product, the second term is associated with the power production possible by sending the generated hydrogen to the gas turbine (GT) and steam turbines for power production. This is a unique option for the Claus process as part of an IGCC. In more conventional applications of the Claus process, that is, the petroleum industry, not much benefit may be realized by recycling the hydrogen to the AGR, as it is usually burned in a furnace. However, in the IGCC application, there exists the possibility of efficiently recovering the heat of combustion of the hydrogen in the GT and steam turbines rather than burning it in the furnace to raise steam. The power production from hydrogen is based on the power production of the syngas expander, GT, and steam turbines. The third

Figure 9. Effect of steam pressure on Claus power production.

term represents the power requirement for recovering the unconverted hydrogen sulfide from the tail gas recycle stream in the AGR. The power requirement of the AGR is based upon the power requirements of the stripped gas compressor, lean Selexol pump, refrigeration duty in the solvent cooler, and the stripper reboiler.2 Using this approximation yields an expected power requirement of 87.41 kW kmol1 H2S. It is anticipated that the true value will be lower than this because this value is based largely on the recovery cost from the raw syngas. As the partial pressure of both H2S and CO2 are higher in the tail gas than in the syngas, it is anticipated that the incremental cost for recovery from the tail gas will be lower. Comparing the partial pressure of carbon dioxide in the tail gas with that of the raw syngas, it is expected that the power requirement will be approximately 70% of value previously calculated, giving approximately 61.19 kW kmol1 H2S. Carbon dioxide is chosen as the scaling factor because, with the deep recovery of carbon dioxide that is considered in the case of an IGCC plant with CO2 capture, the recovery of hydrogen sulfide is automatically satisfied. The fourth term represents the power requirement to compress the treated tail gas from approximately 1 atm to the operation pressure of the AGR, approximately 50 atm. This term is dependent upon the flow of tailgas from the hydrogenation reactor, which depends upon the operation of the Claus process. The fifth term represents the power production possible by using the remainder of the steam produced in the WHB to power steam turbines. For determining the amount of steam generated in the WHB, it was assumed that boiler feedwater’s temperature was constant at 411 K and that 5% of the total heating duty of the WHB is lost to the environment. After the steam is used to provide the heating duty for the Claus process and hydrogenation unit, the remainder is taken for power production in the steam turbines. The numbers used in eq 16 are derived from Bhattacharyya et al.2 Additionally, all sulfur condensers in the process generate steam at 4.4 atm, which is also sent to the steam turbines. The first optimization study was done on the effect of the pressure of the steam raised in the WHB on the objective function given above. The results of this study are given in Figure 9. The pressure of the various stages of the steam turbine, to which the steam generated in the WHB can be sent, is considered to be fixed at the level set in the previous study from our group.2 This results in discontinuities at 124.0, 96.4, and 59.5 atm. This study finds that there exists an optimal steam pressure 2371



Figure 10. Effect of steam pressure on power production from steam turbines, hydrogen, and power requirement for oxygen production.

Figure 11. Effect of H2S/SO2 ratio at inlet to hydrogenation unit on power production of the Claus unit.

of 61 atm that should be generated in the WHB. Generating steam at very high pressures has several negative impacts in a Claus plant. As hydrogen and sulfur dioxide are thermodynamically preferred at high temperatures, one would like to freeze their composition at this higher temperature, where they are thermodynamically preferred. This means increasing the quench rate by decreasing the temperature of the generated steam and thus the pressure. As shown in Figure 10, decreasing steam pressure decreases the quench time, increasing hydrogen yield and decreasing the quantity of oxygen required to maintain a specified H2S/SO2 ratio. Also, at very high pressures, less power generation in the steam turbines is achieved. This is because less steam is generated in the WHB and a large portion of this high quality steam is used for the heating duties of the process. With the pressure of the steam generated fixed at 61 atm, an optimization study was done to determine the optimal H2S/SO2 ratio to run the process in an IGCC application. The H2S/SO2 ratio is maintained by manipulating the oxygen flow to the Claus process. The results of this study are shown in Figures 11 and 12. Traditionally, the process is run at an H2S/SO2 ratio as close as possible to 2 at the inlet of the tail gas treatment unit19 to

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Figure 12. Effect of H2S/SO2 ratio on hydrogen and hydrogen sulfide flow to the AGR.

Figure 13. Effect of H2S/SO2 ratio at inlet to hydrogenation unit on H2S/SO2 ratio at inlet of first and second catalytic reactor.

maximize the conversion of hydrogen sulfide. However, when one considers the possibility of hydrogen production for power generation,6 the optimal ratio increases. Additionally, as an enriched oxygen stream is used for oxidation, there is an associated cost for its production. Also important is that though hydrogen sulfide conversion is not particularly sensitive to the H2S/SO2 ratio beyond about 2, hydrogen flow is fairly sensitive to changes in H2S/SO2 ratios above 2. For example, from a ratio of 2.1 to 6.4, the hydrogen sulfide flow increases by 0.597 kmol h1; this corresponds to single pass conversion decreasing from 96.6% to 96.3%. However, over the same interval, hydrogen flow increases by 13.1%. For these reasons, the IGCC power output is maximized when the Claus unit operates at an H2S/SO2 ratio of approximately 0.8 at the inlet of the first catalytic reactor, 2.5 at the inlet of the second catalytic reactor, and 4.3 at the inlet of the tail gas concentration unit. The corresponding H2S/SO2 ratios at the inlet of the first and second catalytic reactors at varying ratios at the inlet of the hydrogenation unit are provided in Figure 13. Operability Study. There are two important variables that one would like to maintain while operating a Claus process: the 2372



Figure 14. Effect of sour water gas flow on the limiting case of AGR gas purity.

H2S/SO2 ratio and the adiabatic flame temperature. The adiabatic flame temperature is important because it affects both the flame stability and the level of destruction of ammonia. Maintaining the H2S/SO2 ratio is important for satisfying the sulfur yield in the catalytic reactors. A study was carried out investigating the operating envelope in which the H2S/SO2 ratio and adiabatic flame temperature can be maintained by manipulating the oxygen flow rate and the furnace bypass flow in the face of disturbances that may be encountered in the operation of the Claus process as part of an IGCC. These disturbances include changes in flow and composition of the feeds to the Claus process. For this study, the following assumptions were used: the sour water gas composition, given in Table 7, and the hydrogen sulfide flow from the AGR are both constant. Hydrogen sulfide flow from the AGR was held constant at 159 kmol h1. A constraint was considered to ensure that no oxygen can slip from the flame zone of the furnace to the anoxic region where the bypassed acid gas is injected. This was to avoid safety related issues concerned with flashback in the bypassed acid gas line. As approximately 78% of the sour water gas is combustible, the adiabatic flame temperature would always be greater than the minimum required flame temperature if sour water gas were the only gas fed to the furnace. It was found that, for a given sour water gas flow, there is a minimum purity requirement of the acid gas from the AGR and a maximum allowable acid gas bypass. The results of this study are shown in Figure 14. These results are obviously dependent on the composition of the sour water gas, hydrogen sulfide flow, and the H2S/SO2 ratio that is being maintained, but this study does show that there is a strong dependency of the minimum acid gas purity from the AGR and the maximum allowable bypass on the flow of sour water gas. As shown in Figure 14, as the flow of sour water gas increases, the minimum allowable acid gas purity required decreases. Also, as shown in Figure 15, as the sour water gas flow increases, the oxygen flow increases and the acid gas bypass required for maintaining the flame temperature increases. These results show that given a sour water gas flow, the acid gas purity has a lower bound, below which flame temperature is below the minimum value, and the acid gas bypass has an upper bound, above which oxygen will slip to the anoxic region.

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Figure 15. Effect of sour water gas flow on the limiting case of AGR gas bypass and oxygen flow.

Impact of Carbon Capture on the Claus Process. As mentioned before, the Claus unit considered here is part of an IGCC plant with CO2 capture. Due to the high partial pressure of carbon dioxide in the Selexol-based AGR unit,2 a relatively impure acid gas is obtained from the Selexol unit. CO2 concentration in this acid gas is about 40%. This is very different than a typical Claus feed available in a refinery.10 It should also be noted that most of the results available in the open literature for Claus processes relate to refinery operation. Because of this impure acid gas, the use of feed preheating, enriched oxygen from an ASU as the oxidant, and an acid gas bypass are required to ensure sufficiently high furnace temperatures. Ensuring the furnace is maintained above a critical temperature is necessary to ensure complete destruction of ammonia and methane, as well as ensuring a stable flame. Another strong impact of the CO2 capture is that the high partial pressure of carbon dioxide allows the hydrogenation unit to be operated without an external supply of hydrogen and allows the process to be a net hydrogen producer. This is due to the water gas shift reaction, reaction 15a, occurring in the hydrogenation reactor. Whether the Claus and hydrogenation units are net hydrogen consumers or producers is dependent not only upon whether reaction 15a is or is not catalyzed but also upon the process operation and feed gas compositions. From Rameshni,32 it is stated that reaction 15a can yield 70% of the hydrogen required in the hydrogenation reactor; however, this number is based upon some assumed feed gas composition. The studies presented in this paper show that substantial production of carbon monoxide from carbon dioxide occurs in the furnace and WHB. This carbon monoxide, upon entering the hydrogenation unit yields hydrogen via reaction 15a. This hydrogen production, because of the high concentration of carbon monoxide, is greater than the hydrogen required for complete hydrogenation of the remaining sulfur species present in the tail gas. Because of this, the Claus and hydrogenation units are net hydrogen producers in this instance. Experimental data with similar feed composition would be very helpful to validate these results, but unfortunately such data are scarce in the open literature.

’ CONCLUSION To facilitate the effective modeling of a Claus furnace, a fourstage method has been proposed to determine an optimal set of 2373


Industrial & Engineering Chemistry Research linearly independent reactions that would best describe the product distributions from available plant data. The first stage involves the determination of the number of linearly independent reactions that will describe the reacting system. The second stage determines the best possible fit that one can obtain in the presence of experimental error/noise. The third stage requires a database of possible reactions acquired from process knowledge or literature. A quadratic programming (QP) problem is then solved, subject to constraints on the elements of the matrix ζ based upon thermodynamics and process knowledge. Solving this QP problem is computationally inexpensive and allows one to rank the possible reaction sets. The fourth stage requires solving a nonlinear programming problem for each possible reaction set starting with the highest ranked reaction set from the third stage. This stage considers thermodynamics, kinetics, and any additional phenomena considered in the actual model. The solution of this NLP problem is computationally expensive and can take hours to solve, especially using a sequential modular method. The minima of the objective function of this NLP problem is compared against the results attained in the third stage. If it is found to be less than the minima of the next reaction set in the third stage, an optimal reaction set had been found, else the search continues. This method has been demonstrated on the Claus furnace and has shown that it significantly decreases the number of reaction sets to be considered in the fourth stage. After developing the model of the Claus furnace, a kinetic model of the entire modified Claus process has been developed to determine the effect of the unit’s operation on the power output of an IGCC power plant with CO2 capture. With the developed kinetic model, a sensitivity study was undertaken to determine the optimal steam pressure of the WHB. At lower pressures of steam generated in the WHB, more hydrogen is yielded from the process and less oxygen is required for maintaining a specified H2S/SO2 ratio; however, less power is generated from the excess steam not required for heating duties in the process. At higher pressures, less power is generated from the excess steam as less heat is recovered in the WHB and hightemperature high-pressure steam is largely used for heating duties in the process. This study showed that there exists an optimal steam pressure generated in the WHB that balances hydrogen yield, oxygen demand, and power generation. Additionally, with the developed kinetic model, a sensitivity study was undertaken to determine optimal H2S/SO2 ratio to the tail gas treatment unit. At H2S/SO2 ratios close to 2, single-pass conversion is maximized, thus decreasing the cost associated with recapture of hydrogen sulfide in the AGR. However, by operating at H2S/SO2 ratios greater than 2, higher yields of hydrogen are possible, which can be used for high efficiency power generation in the gas turbine and steam turbines. This study showed that there exists an optimal H2S/SO2 ratio that balances single pass conversion, hydrogen yield, oxygen demand, and power generation. A study was undertaken to examine the operating envelope in which both H2S/SO2 ratio and adiabatic flame temperature can be controlled in the face of typical disturbances possible in the operation of an IGCC power plant with CO2 capture. It has been shown here that, for a given flow of the sour water gas, there is a minimum H2S concentration in the acid gas stream from the AGR to maintain the desired H2S/SO2 ratio and the adiabatic flame temperature while ensuring no oxygen slips to the anoxic region of the furnace. This study shows that a Claus unit as part of an IGCC plant with CO2 capture is a net hydrogen producer unlike the typical

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Claus units as part of a refinery, which are hydrogen consumers. This happens because of a Claus feed that is very rich in CO2. The authors believe that experimental data considering a feed gas that is similar in composition to the feed gas considered in this study would be very beneficial for comparison with our results and further enhancement of the reaction kinetics, if needed.

’ AUTHOR INFORMATION Corresponding Author

*Phone: 3042939335. E-mail: Debangsu.Bhattacharyya@ mail.wvu.edu.

’ ACKNOWLEDGMENT This technical effort was performed in support of the National Energy Technology Laboratory’s ongoing research in Process and Dynamic Systems Research under the RES contract DE-FE0004000. ’ REFERENCES (1) National Energy Technology Laboratory. Cost and Performance Baseline for Fossil Energy Power Plants Study, Volume 1: Bituminous coal and natural gas to electricity, DOE/NETL-2007/1281; Aug 2007; Available online: http://www.netl.doe.gov. (2) Bhattacharyya, D.; Turton, R.; Zitney, S. Steady-state simulation and optimization of an integrated gasification combined cycle (IGCC) power plant with CO2 capture. Ind. Eng. Chem. Res. 2011, 50, 1674– 1690. (3) Encyclopedia of Energy Engineering and Technology; Capehart, B. L., Ed.; CRC Press: Boca Raton, FL, 2007; Vol. 23. (4) Rameshni, M.; Street, R. PROClaus: The New Standard for Claus PerformanceWorleyParsons: 2001 (5) Jacobs Consultancy U.K. Impact of CO2 removal on coal gasification based fuel plants; Feb 2006. (6) Nasto, L. V.; Karan, K.; Mehrotra, A. K.; Behie, L. A. Modeling reaction quench times in the waste heat boiler of a Claus plant. Ind. Eng. Chem. Res. 1994, 33, 4–13. (7) Tong, S.; Dalla Lana, I. G.; Chuang, K. T. Effect of catalyst shape on the hydrolysis of COS and CS2 in a simulated Claus converter. Ind. Eng. Chem. Res. 1997, 36, 4087–4093. (8) Puchyr, D. M. J.; Mehrotra, A. K.; Behie, L. A.; Kalogerakis, N. Hydrodynamics and kinetic modeling of circulating fluidized bed reactors applied to a modified Claus Plant. Chem. Eng. Sci. 1996, 51, 5251–5262. (9) ZareNezhad, B; Hosseinpour, N. Evaluation of different alternatives for increasing the reaction furnace temperature of Claus SRU by chemical equilibrium calculations. Appl. Therm. Eng. 2008, 28, 738–744. (10) Sames, J. A.; Paskall, H. G.; Brown, D. M.; Chen, M. S. K.; Sulkowski, D.Field measurements of hydrogen production in an oxygenenriched Claus furnace.Proceedings of Sulfur 1990 International Conference, Cancun, Mexico, April 14, 1990. (11) Hawboldt, K. A. Kinetic modelling of key reactions in the modified Claus plant front end furnace. Ph.D. Thesis, Department of Chemical and Petroleum Engineering, University of Calgary, Canada, 1998. (12) Monnery, W. D.; Hawboldt, K. A.; Pollock, A. E.; Svrcek, W. Y. Ammonia pyrolysis and oxidation in the Claus furnace. Ind. Eng. Chem. Res. 2001, 40, 144–151. (13) Karan, K.; Mehrotra, A. K.; Behie, L. A. A high-temperature experimental and modeling study of homogeneous gas-phase COS reactions applied to Claus plants. Chem. Eng. Sci. 1999, 54, 2999–3006. (14) Clark, P. D.; Dowling, N. I.; Huang, M.; Svrcek, W. Y.; Monnery, W. D. Mechanisms of CO and COS formation in the Claus furnace. Ind. Eng. Chem. Res. 2001, 40, 497–508. 2374



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(15) Monnery, W. D.; Hawboldt, K. A.; Pollock, A.; Svrcek, W. Y. New experimental data and kinetic rate expression for the Claus reaction. Chem. Eng. Sci. 2000, 55, 5141–5148. (16) Hawboldt, K. A.; Monnery, W. D.; Svrcek, W. Y. New experimental data and kinetic rate expression for H2S pyrolysis and reassociation. Chem. Eng. Sci. 2000, 55, 957–966. (17) Clark, P. D.; Dowling, N. I.; Huang, M. Mechanisms of ammonia destruction in the Claus furnace. Proceedings of the Laurance Reid Gas Conditioning Conference 2001, 301–318. (18) Kohl, A. L.; Nielson, R. Gas Purification, 5th ed.; Gulf Professional Publishing: Houston, TX, 1997. (19) Taggart, G. W. Optimize Claus control. Hydrocarbon Process. 1980, 133–138. (20) Jones, D.; Bhattacharyya, D.; Turton, R.; Zitney, S. E. Optimal design and integration of an air separation unit (ASU) for an integrated gasification combined cycle (IGCC) power plant with CO2 capture. Fuel Process. Technol. 2011, 92, 1685–1695. (21) Sassi, M.; Gupta, A. K. Sulfur recovery from acid gas using the Claus process and high tempeature air combustion (HiTAC) technology. Am. J. Environ. Sci. 2008, 5, 502–511. (22) Westbrook, C. K.; Dryer, F. L. Simplified mechanisms for the oxidation of hydrocarbon fuels in flames. Combust. Sci. Technol. 1981, 27, 31–43. (23) Peters, N. Premixed burning in diffusion flames—The Flame Zone Model of Libby and Economos. Int. J. Heat Mass Transfer 1979, 22, 691–703. (24) Clark, P Chemistry of the Claus furnace. Sulfur 2003, 285, 24–26. (25) Aris, R.; Mah, R. H. S. Independence of chemical reactions. Ind. Eng. Chem. Fundam. 1963, 2, 90–94. (26) Gnielinski, V. Equations for heat and mass transfer in turbulent pipe and channel flow. Int. Chem. Eng 1976, 16, 359–368. (27) Karan, K.; Mehrotra, A. K.; Behie, L. A. COS-forming reaction between CO and sulfur: A high-temperature intrinsic kinetics study. Ind. Eng. Chem. Res. 1998, 37, 4609–4616. (28) Jia-yuan, Zhang; Jie-min, Zhou; Hong-jie, Yan Kinetic model on coke oven gas with steam reforming. J. Cent. South Univ. Technol. 2008, 15, 127–131. (29) Birkholz, R.; Behie, L.; Dalla Lana, I. Kinetic modeling of a fluidized bed Claus plant. Can. J. Chem. Eng. 1987, 65, 778–784. (30) Tong, S.; Dalla Lana, I.; Chuang, K. Kinetic modeling of the hydrolysis of carbonyl sulfide catalyzed by either titania or alumina. Can. J. Chem. Eng. 1993, 71, 392–400. (31) Massie, S. N.. Less fuel, less fire, less pollution using low temperature tail gas catalyst and catalytic incineration in sulfur plants. Gas Processors Association Europe Spring Conference, 2006. (32) Rameshni, M. Selection Criteria for Claus Tail Gas Treating Processes; WorleyParsons: 2010.

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