Simulation based synthesis, design and optimization of pressureswing adsorption (PSA) processes

R. Rajasree *, A.S. MoharirComputer Aided Design Centre, Indian Institute of Technology, Powai, Bombay 400 076, India

Abstract

Process synthesis is one of the important aspects in the design of adsorption processes, especially in the case of pressure swingadsorption (PSA) processes. A process simulator which can help in taking decisions on the number of beds, operatingconfigurations and operating parameters (pressure ratio, cycle step times, flow velocity, feed/purge ratio, etc.) can minimize thenecessity of pilot plant studies, resolve commissioning problems and help in optimal operation. This work makes an attempt atsimulation based on-line synthesis, design and optimization of PSA processes. A simulation/tuning/optimization software has beendeveloped for PSA processes for multicomponent separation. Adaptive simulation based process synthesis and optimizationstrategies have been demonstrated using a rigorous simulation (called Process) as an emulation of a flexible PSA plant instead ofan actual plant and a less rigorous model as its simulation. The findings based on this study appear to be an effective approachto optimal synthesis, design and optimization of any PSA processes.

Keywords: Adsorption; PSA; Process synthesis; Design; Optimization; Simulation; Air separation

1. Introduction

Pressure swing adsorption (PSA) processes for sepa-ration and purification of gaseous mixtures have be-come important unit operations in the chemical processindustry. A large variety of binary and multicomponentgas mixtures are commercially separated using thistechnology. This remarkable progress has been possibledue to the development of tailor-made adsorbents forgiven separations and various sophisticated cycles em-ploying multiple beds which improve the performanceof the process. The enormous potential of this processis unfortunately not backed up by reliable processsynthesis/design procedures and extensive bench scaleand pilot scale experimentation has therefore been amore acceptable practice in PSA process development.The situation would improve if adaptive simulationbased process synthesis, design and optimization proce-dures were developed.

This study focuses on developing strategies for simu-lation based on-line synthesis, design and optimization

of pressure swing adsorption (PSA) processes wheremodel parameters are regressed and updated using liveexperimental data. For the above objective, generalpurpose simulation/tuning/optimization software hasbeen developed for PSA systems for multicomponentseparation. The proposed strategies have been demon-strated using a rigorous simulation (called Process) asan emulation of a flexible PSA plant instead of theactual plant and a less rigorous model as its simulation.The findings made on the basis of studies show thepotential of the present approach for on-line synthesis,design and optimization of PSA systems.

2. Mathematical modeling

A packed bed model is the core of any PSA processmodel. Simulating any PSA configuration involves in-voking the packed bed model repeatedly and cyclicallywith a sequence of sets of initial and boundary condi-tions compatible with the steps of a PSA cycle. Hencepacked bed models of various rigors have been devel-oped first and models for any PSA process embodimentand operating conditions are developed using thesepacked bed models as unit modules.

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2.1. Modeling of adsorption/desorption during flow in apacked bed

Simulation of adsorption/desorption during flow in apacked bed of adsorbent involves solution of massbalance equations over a differential element in the bed.The differential fluid phase mass balance for each com-ponent i for an isothermal system and assuming anaxial dispersed plug flow model is as follows:

−DL

#2ci

#z2 +#(ci 6)#z

+#ci

#t+�1−o

o

� #q̄i

#t=0;

i=1, 2, . . . , n (1)

The adsorbed phase mass balance or particle massbalance for each component is of the form:

#q̄i

#t= fi(q̄j, cj ; j=1, 2, . . . ,n); i=1, 2, . . . , n (2)

The equilibrium isotherm is:

qi*=gi(cj ; j=1, 2, . . . , n); i=1, 2, . . . , n (3)

in the above fi(=cj ; j=1, 2, . . . , n) and gi(cj ; j=1, 2, . . . , n) represent applicable mathematical formsfor adsorption rate equation and equilibrium isothermrespectively. Depending on the isotherm form, diffusiv-ity–concentration relationship and concentration levelsin the feed mixture, suitable forms of fluid phase andadsorbed phase mass balance equations can be chosen.These equations along with appropriate initial andboundary conditions comprise the mathematical modelsfor packed bed adsorption/desorption.

A comparative study of finite difference techniqueand orthogonal collocation technique has been made bysimulating packed bed adsorption behavior and theresults have been validated using the numerical solutionreported by other authors (Garg & Ruthven, 1972,1973a,b). An orthogonal collocation method has beenfound to be superior to finite difference techniques asreported by Raghavan and Ruthven (1983) and furthersimulation has been done using orthogonal collocationtechnique.

2.2. Modeling of pressure surges in a packed bed

For most PSA processes such as oxygen-PSA, nitro-gen-PSA, hydrogen-PSA, etc., the pressurization/coun-tercurrent depressurization steps could be ofcomparable durations with those of adsorption/desorp-tion steps. Therefore ignoring adsorption/desorptionduring pressurization/countercurrent depressurizationsteps through assumption of frozen solid phase (Has-san, Ruthven & Raghavan, 1986; Hassan, Raghavan &Ruthven, 1987; Raghavan & Ruthven, 1985; Raghavan,Hassan & Ruthven, 1985) may be erroneous. In view ofthis, in addition to the models for packed bed adsorp-tion/desorption with flow and at constant pressure,

models for packed bed performance during pressuriza-tion/countercurrent depressurization steps have beendeveloped. The pressurization step has been modeled ascomprising of a recurring sequence of two discreteevents.1. Instantaneous bulk phase pressurization (IBPP): the

gas needed to pressurize the column from P0 to P(P\P0) enters the column instantaneously andpressurize the column. For modeling this step, thecolumn is divided into n finite sections of length ofDX.

2. Adsorption: the column sections are assumed to beisolated from each other and adsorption occurs ineach section for finite time step Dt. This results indecrease in pressure in different sections. AnotherIBPP event then occurs and so on. Thus the cycle ofIBPP and adsorption continues till the completionof pressurization step. Sequential steps in an al-gorithm for pressurization step are listed below.

carry out IBPP for the entire bed com-Step 1:prising of several discrete lengths DX.carry out adsorption step in each sectionStep 2:of the column for a finite time Dt.if the time equals the pressurization time,Step 3:terminate pressurization step.Otherwise return to step 1.

The countercurrent depressurization step is similar tothe pressurization step, except that the flow direction isreversed. The countercurrent depressurization is mod-eled as comprising of recurring sequence of two discreteevents.1. Instantaneous Bulk Phase Depressurization (IBPD)

and2. Desorption.Using these pressurization/countercurrent depressuriza-tion models, steps such as cocurrent depressurizationand pressure equalization have also been modeled (Ra-jasree, 1997).

3. Simulation based synthesis, design and optimization

Fig. 1 shows the flow chart used in the developmentof the on-line synthesis, design and optimization strat-egy. It mainly consists of the following sections:� simulation of pressure swing adsorption process;� tuning of the simulation model;� process synthesis/optimization.

The software allows the user to arrive at the numberof beds, cycle configuration and a set of optimal operat-ing conditions for the desired performance. This isachieved iteratively as follows. The experimental perfor-mance (say percentage purity of the raffinate or extractin terms of the desired component) is compared with

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the performance simulated using a semi-rigorous modelfor the same set of design and operating parameters.The simplifying assumptions made in modeling of aprocess are not necessarily totally realistic and there-fore, the physical system (say pilot plant) behaviorwould deviate from the simulated behavior. The mis-match between the experimental performance and thesimulated performance if any is bridged by tuning thesimulation model. This is same as adapting the simula-tion such that it predicts the known experimental per-formance. The tuned model is then used for processsynthesis/operating parameter optimization to achievethe performance goal, ideally in one and practically in afew sub-optimal policy implementations.

The simulation software in the present study wasdeveloped in a modular fashion. Depending on the userdefined operating parameters and operating configura-tion, the simulator internally calls the correspondingbed modules for performance simulation. The simulatorstores the sequence of operating cycles, temporarilystores the information in each process cycles and uses itto define initial conditions when the bed undergoes nextstep of operation. The tuning algorithm that has beendeveloped leaves the choice of tuning parameter to the

user. One may choose to increase/decrease individualequilibrium constants (not necessarily to the same ex-tent, thereby altering the adsorption capacity for indi-vidual components and also the equilibrium selectivity).Component diffusivities and kinetic selectivities maysimilarly be changed. In the present study, tuning iscarried out by the golden section method ofoptimization.

The operating parameter optimization/process syn-thesis can be carried out either interactively or nonin-teractively. In the interactive mode, the user has theoption of adjusting the operating parameters/configura-tions within the range in the direction in which theprocess has to be improved. In the noninteractivemode, the operating parameters and cycle configurationare optimized using the Box complex method of opti-mization. If the desired purity is not possible within thepossible range of parameter values, the software re-sponds with the maximum purity possible within theconstraints of the variables.

In the three areas of application of the adaptivesimulation model, namely, process synthesis, design andoptimization, the last is the simplest to implement andis discussed here to begin with. Although the philoso-

Fig. 1. Flow chart showing the on-line simulation/tuning/optimization strategy.

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Fig. 2. Schematic representation of the two-bed unit used in the demonstration of the simulation based process synthesis.

phy being tested should have general applicability, sep-aration of air using a nitrogen selective adsorbent toobtain enriched oxygen as a product is used as a casestudy. For testing the developed strategy, it was consid-ered desirable to use a rigorous simulation as an emula-tion of a flexible PSA plant instead of the plant itselfand the philosophy is explained below.

4. Optimization of process emulation

Important aspects of a rigorous model used as aprocess emulation are as follows.1. The system is isothermal.2. The rate of adsorption/desorption is assumed to be

controlled by micropore diffusion with concentra-tion independent diffusivity.

3. The equilibrium relationship is nonlinear and de-scribed by Langmuir isotherm.

4. Volumetric flow change due to adsorption/desorp-tion are incorporated.

5. Bulk phase accumulation is accounted for.6. Adsorbent particles are of uniform size and spheri-

cal in shape.

7. Plug flow prevails in the bed with no axial or radialdispersion.

8. Pressure surge is incorporated during pressurizationand depressurization step.

9. Constant pressure is maintained during adsorption/desorption steps.

This model shall henceforth be referred to as Process.A two-bed PSA system (shown in Fig. 2) with the

following design parameters is considered.

Bed length (cm) 65.3:Internal bed diameter (cm) : 12.7

:Bed voidage 0.40

The three step PSA cycle configurations and the steptimings used are as follows.

Time (s)Configuration

Adsorption 45:Countercurrent depressurization : 30

:Purge 15

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The corresponding valve sequencing to implement theabove PSA cycle (Fig. 2) is

Configuration I: adsorption, countercurrent depressur-ization, purge from product tank

Step no. Bed AValves open Bed B

1, 3, 61 Adsorption Countercur-rentdepressurization

2 Adsorptionl, 3, 6, 8 Purge fromproduct tank

3 2, 5, 7 Countercur- AdsorptionrentdepressurizationPurge from2, 4, 5, 7 Adsorption4producttank

The Process is a two-bed PSA system for enrichment ofoxygen from air using 5A zeolite. As oxygen and argonhave similar adsorption characteristics, they are treatedas one species. Thus air is considered as a binarymixture of O2 (21%) and N2 (79%). The details of theadsorbent–adsorbate characteristics are as follows.

SphericalParticle shape0.158 cmParticle diameter

Particle density 1.202 g cm−3

0.75 g cm−3Bulk densityMole fraction of oxygen in air 0.21

0.79Mole fraction of nitrogen in airLangmuir isotherm constants (Chou, Huang &Chiang, 1992)

Oxygen:K1 : 1.63×10−4 g mol g−1 atm−1 (1.61×10−6

kmol kg−1 kPa−1)K2 : 5.70×10−2 atm−1 (5.66×10−4 kPa−1)

Nitrogen:K1 : 3.33×10−4 g mol g−1 atm−1 (3.21×10−6

kmol kg−1 kPa−1)K2 : 1.55×10−2 atm−1 (5.66×10−4 kPa−1)

Diffusivities (Ruthven, Xu & Farooq, 1993)DO2

: 2.0×10−4 cm2 s−1

DN2: 6.2×10−5 cm2 s−1

A semi rigorous model which differs from rigorousmodel in the following aspects was used for the simula-tion of this Process. The important aspects of thismodel are:1. equilibrium relationship is linear;

2. changes in the bulk stream velocity in the columndue to adsorption/desorption are not accounted forin the model;

3. the bulk phase accumulation term is not incorpo-rated in the model.

The optimization objective is to get 93% oxygen atmaximum possible recovery. Process optimization wasthen carried out in a stepwise manner as follows.

4.1. Step 1: the process is put on operation

It is assumed that the two adsorbent beds are initiallysaturated with air at the ambient conditions. With thisinitial condition, the simulation of start up leading, tocyclic steady state is done with the following operatingconditions.

Temperature : 298 K:Adsorption pressure 6 atm (abs)

1 atm (abs):Desorption pressure:Velocity of feed stream 0.68 cm s−1

2.50 cm s−1Velocity of purge stream :90 s:Cycle time

:Adsorption step time 45 sCountercurrent depressurization : 30 s

step time15 sPurge step time :

The Process reaches a cyclic steady state after theproduction rate (in terms of recovery) and purity ofoxygen go through the transient phase as shown in Fig.3.

4.2. Step II: simulation of the process is carried out

As mentioned earlier, a semi-rigorous model is usedto simulate the Process. The operating conditions anddesign parameters of the Process are used in the simula-tion model to simulate the dynamic and cyclic steadystate of the Process. The first simulation was carriedout with the following characteristic parameters of theadsorbent–adsorbate system.

Linear equilibrium constant, K (g molComponentadsorbed cm−3 of zeolite crystalatm−1)

4.70Oxygen14.80Nitrogen

The transient approach of oxygen production (in termsof recovery) and purity simulated with these values arealso shown in Fig. 3 for comparison. The cyclic steadystate performance comparison is given below.

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ProcessSimulationPerformance parameters

421.82Oxygen purity (%) 69.3615.3419.57Oxygen recovery (%)

4.3. Step III: tuning (adapting) the simulation model

There is a distinct mismatch between the Processperformance and the simulated performance. Thereforetuning of the model to bring the simulation closer toexperimentally (Process in this case) observed perfor-mance is necessary. In the present exercise, the equi-librium constant of N2 is used to tune the model. Thetuned values are as follows.

K (g mol adsorbed cm−1 of zeoliteComponentcrystal atm−1)

After tuningBefore tuning

4.70 4.70Oxygen14.80Nitrogen 8.82

With the original simulation parameters changed to theabove given new values, the simulated cyclic steadystate performance matched with Process performanceas shown in Fig. 4. The cyclic steady state performancevalues are given below. Only oxygen purity was used asa tuning criterion. There is thus a deviation in oxygenrecovery even after tuning (Fig. 4).

ProcessPerformance parameters Simulation

43.03Oxygen purity (%) 42.8215.3414.75Oxygen recovery (%)

4.4. Step IV: off-line Process optimization

The first run of the Process did not produce thedesired performance. Operating conditions, therefore,need to be changed to achieve the performance goal.Off-line process optimization using the tuned/adaptedsimulation model is carried out so as to achieve theperformance goal, ideally in one and practically in fewoptimum policy implementations.

The objective function is defined as the squareddeviation between the desired and simulated oxygenpurity. The requirement of a maximum production rateat a desired purity is not explicitly imposed at thisstage. It can be done if necessary by defining a com-posite objective function with proper weights to theproduction rate and purity levels. The off-line optimiza-tion in the present case is carried out interactively, i.e.adjusting the operating parameters in the direction inwhich minimization of the objective function occurs.

The optimization results indicate that the Processwould give a purity level of 92.87% and a productionrate of 18.13% at the following operating point.

298 KTemperature :6 atm (abs):Adsorption pressure1 atm (abs)Desorption pressure :0.39 cm s−1Velocity of feed stream :

Fig. 3. Comparison of recovery and purity of oxygen through transient approach to steady state between Process and simulation.

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Fig. 4. Comparison of recovery and purity of oxygen through transient approach to steady state between Process and tuned simulation.

: 1.50 cm s−1Velocity of purge stream:Cycle time 60 s:Adsorption step time 30 s

Countercurrent depressurization 15 s:step time

:Purge step time 15 s

4.5. Step V: implementation of optimal operating policyon the process

The steady state Process performance after imple-menting the optimal policy is as shown below

Oxygen purity (%) 91.0818.00Oxygen recovery (%)

It may be seen that the steady state performance fallsshort of the desired goal although there has beenimprovement over the previous Process performance.For the optimal parameter set, the tuned simulationmodel had predicted a better performance. With thenew operating and performance data available from theProcess, another round of simulation, tuning, off-lineoptimization and implementation may be initiated andso on. The improvement in the Process performancewith successive iteration of these adaptive simulationbased optimization is as shown below.

Performance parametersIteration no.

Recovery (%)Purity (%)

18.0091.0812 92.85 16.91

5. Simulation based process synthesis

Process synthesis of a PSA process for desired sepa-ration could involve:1. seeking a proper choice between PSA, VSA or

combination;2. selecting an optimum number of beds and;3. selecting a proper configuration of the PSA (or

VSA) cycle for a given number of beds.While (i) and (ii) could be more appropriate at aconceptual design level of the process evolution, step (iii)could be seen even as an operating optimization because,in a flexibly designed set up, it merely amounts tochanging valve sequencing and, thereby, effectively im-plementing different PSA cycle configurations. It may beconsidered, more appropriately, as a synthesis problembecause one is seeking not just optimal values of processparameters such as pressure levels, flow rates and PSAcycle step times, but the cycle configuration itself. Bothphilosophies are explained below with Process.

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6. Synthesis of a PSA cycle configuration for fixednumber of beds

With different valve sequencing patterns, it is possi-ble to implement several PSA cycle configurations inthe two-bed set-up shown in Fig. 2. Some possibilitiesother than configuration I are configurations II–IV.

In the case of process optimization, the number ofbeds and PSA cycle configurations were considered asfixed and only the operating variables (pressure, flowrates, and step timings) were varied to seek optimalperformance. During the off-line process optimization,if the optimum is sought not only for a fixed PSA cycleconfiguration, but allowing, say four different discreteforms as above, the same logic would be applied to theprocess synthesis task.

Configuration II: pressurization, adsorption, counter-current depressurization, purge from product tank

ValvesStep no. Bed A Bed B

open

Pressurization1 Countercurrent1, 6

depressurization

2 1, 3, 6 Adsorption Countercurrent

depressurization

3 Adsorption1, 3, 6, 8 Purge from

product tank

Countercurrent4 Pressurization2, 5

Depressurization

5 2, 5, 7 Countercurrent Adsorption

Depressurization

2, 4, 5, 7 Purge from6 Adsorption

product tank

Configuration III: Adsorption, pressure equalization,countercurrent depressurization, purge from producttank, Pressure equalization

ValvesStep Bed A Bed Bno. open

Adsorption1 Countercur-1, 3, 6rentdepressuriz-ation

1, 3, 6, 82 Adsorption Purge fromproduct tank

Pressure9, 103 Pressureequalizationequalization

4 Countercurrent2, 5, 7 AdsorptionDepressuriz-ation

Purge from2, 4, 5, 75 AdsorptionproductTank

6 Pressure equal-9, 10 Pressureequalizationization

Configuration IV: pressurization, adsorption, pressureequalization, countercurrent depressurization, purgefrom product tank, pressure equalization

Valves Bed A Bed BStepno. open

Pressurization1 Countercur-1, 6rentdepressurizationPurge from1, 3, 6, 8 Adsorption2product tank

Pressure equal-9, 10 Pressure3ization equalization

4 2, 5 Countercurrent Pressurization

DepressurizationPurge from2, 4, 5, 7 Adsorption5productTank

6 Pressure equal-9, 10 Pressureequalizationization

The synthesis/optimization objective is to obtain 93%oxygen at a maximum possible recovery level. Thesimulation of the start up is done with two beds usingthe simplest configuration (configuration 1). Step I–IIIis the same as in the case of Process optimizationdescribed above.

6.1. Step IV: off-line process synthesis/optimization

The process variables such as cycle step times, pres-sures and flow rates are treated as continuous variableswhile the cycle configuration is treated as discrete vari-ables taking one or more of the earlier given options.The optimization results show that the Process wouldgive following performances with the aboveconfigurations.

Configuration Performance parameters

Oxygen recoveryOxygen purity(%) (%)

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I 92.87 18.1320.2792.79II

III 92.85 18.8530.2592.95IV

at the following operating points respectively.

Opening parameters Configuration

II IIII IV

Temperature (K) 298 298 298 2986 6Adsorption pressure (atm) 661 1 1Desorption pressure (atm) 1

0.39 0.24Velocity of feed stream 0.41 0.21(cm s−1)

1.50 1.47 1.25Velocity of purge stream 1.20(cm s−1)

130Cycle time (s) 6060 150Pressurization step time 0 5 0 5

(s)60 25Adsorption step time (s) 6530

0 0 5Pressure equalization step 5time (s)

Countercurrent depressur- 15 50 10 55ization step time (s)

15Purge step time (s) 1515 150 0 5 5Pressure equalization step

time (s)

6.2. Step V: implementation of optimal operating policyon the process

While only the best strategy (configuration IV)should be implemented, the steady state Process perfor-mances for all the above configurations after imple-menting the individual optimal policies are shownbelow.

Configuration Performance parameters

Oxygen purity Oxygen recovery(%)(%)

91.08I 18.0036.8187.83II

79.95III 28.51IV 32.7989.32

The iterative improvement in the Process performancefor these configurations with these adaptive simulationbased optimization steps is as shown below.

Configuration Performance parametersIterationno.

Oxygen pu- Oxygen re-rity (%) covery (%)

91.081 18.00I87.83 36.81II79.95III 28.51

IV 89.32 32.7992.85I 16.912

II 92.89 29.1393.39 18.17III92.53 30.59IV

The above demonstration shows that configuration IVgives more recovery at almost the same purity as com-pared to other configurations and is the optimumconfiguration among the above configurations.

7. Synthesis of a PSA cycle configuration for avariable number of beds

In the process synthesis considered earlier, the num-ber of beds were considered fixed and the PSA cycleconfigurations and operating variables were varied toachieve the optimal performance. Here the processsynthesis task is extended by allowing limited flexibilityon the number of beds during the off-line optimizationstep. For demonstrating this strategy, the two-bed set-up used earlier (Fig. 2) is extended to three beds asshown in Fig. 5 and the corresponding configurationsare V–VIII.

Configuration V: adsorption, countercurrent depressur-ization, purge from product tank

ValvesStep no. Bed A Bed B Bed Copen

1 1, 3, 6, 12, Adsorp- PurgeCounter-14 tion current from

productdepres-tanksurizat-

ion2, 6, 8, 11, PurgeCounter- Adsorp-213 current tionfrom

productdepres-suriza- tanktion

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Counter-Adsorp-3 2, 4, 5, 7, Purge12 tionfrom current

product depres-tank suriza-

tion

The synthesis/optimization objective is to obtain 93%purity of oxygen at a maximum possible recovery level.The simulation of the start up is done with two bedsusing the simplest configuration (Configuration I). Thevalves 11–15 (Fig. 5) are closed at all times.

Steps I–III are the same as in the case of the two-bedsystem described above.

Configuration VI: pressurization, adsorption, counter-current depressurization, purge from product tank

Bed A Bed CStep no. Bed BValvesopen

Counter-1 1, 6, 12, PurgePressur-current from14 izationdepres- productsuriza- tanktion

PurgeAdsorp-2 Counter-1, 3, 6,tion from12, 14 current

productdepressurization tankPurge3 2, 6, 8, Pressur-Counter-from11 izationcurrentproductdepres-tanksuriza-

tionAdsorp-4 Coun-2, 6, 8, Purge

tercur- tionfrom11, 13productrent

depres- tanksurization

Pressur-Purge5 2, 4, 5, Counter-from12 ization currentproduct depres-

surizationtank

Fig. 5. Schematic representation of the three-bed unit used in the demonstration of the simulation based process synthesis.

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Counter-Adsorp-Purge6 2, 4 , 5, 7,tionfrom current12

product depressuritank zation

Configuration VII: adsorption, pressure equalization,countercurrent depressurization

Bed B Bed CBed AStep no. Valvesopen

Adsorp- Purge1 1, 3, 6, Counter-12, 14 currenttion from

productdepressu-tankrization

Counter-Pressure Pressure2 9, 6, 15equaliza- equalizat-current

depressu- iontionrization

Adsorpt-Purge3 2, 6, 8, 11, Counter-13 fromcurrent ion

depressu- productrization tankCounter- PressurePressure4 2, 10, 15

equaliza- equalizat-currentiondepressu- tion

rization2, 4, Adsorpt-Purge5 Counter-

from currention5, 7, 12product depressu-tank rizationPressure Pressure Counter-6 9, 10, 12

equaliza- currentequaliza-depressu-tion tionrization

Configuration VIII: pressurization, adsorption, pressureequalization, countercurrent depressurization, purgefrom product tank, pressure equalization

Bed A Bed CBed BStep no. Valvesopen

Pressuri- Purge1 1, 6, 12, Counter-14 fromzation current

productdepressu-rization tank

PurgeAdsorp- Counter-1, 3, 6, 12,2current from14 tiondepressu- productrization tank

Counter-Pressure Pressure3 9, 6, 15equaliza-equaliza- currenttiontion depressu-

rizationPurgeCounter- Pressuriza4 2, 6, 8, 11fromcurrent tion

depress- producttankurization

Adsorp-Purge5 2, 6, 8, 11, Counter-13 tionfromcurrent

productdepressu-tankrization

Counter- Pressure Pressure6 2, 10, 15equaliza-current equaliza-tion tiondepressu-

rization2, 4, 5, 12 Purge Counter-7 Pressuri-

zationfrom currentdepressu-productrizationtank

2, 4, 5, 7,8 Counter-Adsorp-Purge12 currenttionfrom

depressu-producttank rizationPressure Counter-9 Pressure9, 10, 12

equaliz-equaliza- currenttion ation depressu-

rization

7.1. Step IV: off-line process synthesis/optimization

The off-line process synthesis/optimization is carriedout using the two-bed configurations (ConfigurationsI–IV) as well as three-bed configurations (configura-tions V–VIII). The process synthesis/optimization re-sults on the two-bed set-up show that it would give thefollowing best possible performances with Configura-tion IV.

Oxygen purity (%) 92.53Oxygen recovery (%) 30.59

The process synthesis/optimization results also showthat the three-bed set-up would give the followingperformances with Configurations V–VIII.

Performance parametersConfigurationOxygen purity Oxygen recovery(%) (%)

14.97V 93.0428.3992.83VI

VII 92.95 26.07VIII 93.55 43.55

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at the following operating points, respectively.

Opening parameters Configuration

II IIII IV

Temperature (K) 298 298 298 2986 66 6Adsorption pressure (atm)1 1Desorption pressure (atm) 11

0.45 0.20 0.25Velocity of feed stream 0.26(cm s−1)

0.95 0.26 0.34 0.38Velocity of purge stream(cm s−1)

300Cycle time (s) 9090 270Pressurization step time 0 5 0 5(s)Adsorption step time (s) 30 95 25 80

0 0 5Pressure equalization step 5time (s)Countercurrent depressur- 30 100 30 90ization step time (s)

100Purge step time (s) 2530 850 0 5Pressure equalization step 5

time (s)

7.2. Step V: implementation of optimal operating policyon the process

The best strategy, configuration VIII is implemented.The steady state process performances for configurationVIII is as follows.

Oxygen purity (%) 90.8147.66Oxygen recovery (%)

The iterative improvement in the process performancewith the adaptive simulation based synthesis/optimiza-tion steps is as shown below.

Iteration no. Performance parameters

Oxygen recoveryOxygen purity(%) (%)

47.6690.81142.932 93.59

The above demonstration shows that using three beds,it is possible to achieve an increase in recovery of12.34% over the best possible in a two-bed set-up.

8. Conclusion

The present study was undertaken as a step towardsthe development of strategy for simulation based on-line synthesis, design and optimization of any PSAprocess for multicomponent separation. The developedsimulation/tuning/optimization software can be usedfor:� simulating any PSA process embodiments and op-

erating conditions� preliminary design of a PSA column;� operating parameter optimization;� process synthesis.To facilitate the full scope of the software, a higherorder model was used as a mimic (called Process) ofthe process and synthesis, design and optimization (intem-is of process parameters) of this Process was car-ried out using a relatively less rigorous model as asimulation.

In the examples presented here, the design parame-ters, such as bed diameter, bed height were nottreated as optimization variables. In that sense, opti-mization was only with respect to the processparameters such as pressure levels, flow rates etc. Al-though it would not be difficult to incorporate beddimensions as optimization variables, the above wasconsidered appropriate in the proposed on-line syn-thesis, design and optimization approach becausechanging beds and switching over from one size ofbeds to another is impractical.

9. Nomenclature

sorbate concentration in the bulk phaseCDL axial dispersion coefficient

number of componentsnq local sorbate concentration in zeolite crystal

value of q averaged over crystalq̄timetlinear velocity6axial coordinatezvoid fraction of the adsorption bedo

species ii

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