The Engineering of Hydrogen Peroxide

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RESEARCH PAPER The Engineering of Hydrogen Peroxide Decontamination Systems Stefan Radl & Stefanie Ortner & Radompon Sungkorn & Johannes G. Khinast Published online: 16 June 2009 # International Society for Pharmaceutical Engineering 2009 Abstract In this article, the latest developments for designing hydrogen peroxide decontamination systems are analyzed. Specifically, focus is given to the accurate calculation of hydrogen peroxide condensation phenomena and discussion of a new correlation for its accurate prediction. A procedure for calculating the condensate composition or the dew point out of this correlation is detailed, and an hx diagram for moist, hydrogen peroxide- laden air, which is of fundamental importance for the rational design of hydrogen peroxide decontamination systems, is proposed. Also presented are theoretical results that illustrate the effect of condensation and evaporation in these systems. Finally, some perspectives for improving hydrogen peroxide systems, and the role computational fluid dynamics (CFD) may have in this field, are provided. Keywords Hydrogen peroxide . Decontamination . Pharmaceutical engineering . Condensation . Mollier hx diagram Notation Latin letters A DHP Total inner surface area of the DHP chamber [m²] B j Parameters of the RedlichKister equation [J/kmol] c i Concentration of species i in the gas phase [mg/l] c i sat Saturation concentration of species i over the liquid film [mg/l] c p,i Heat capacity of species i in the gas phase [kJ/kmol . K] c p,chamber Heat capacity of the chamber wall material [kJ/kg.K] C Dimensionless concentration of inlet gas C μ , C 1ε , C 2ε Constants for the turbulence model D i Diffusion coefficient of species i in air [m 2 /s] f Target function for the dew point iteration [Pa] g Gravitational acceleration [m/s 2 ] h Specific enthalpy [kJ/kmol] h 1+ x Enthalpy [kJ/kg dry air ] ΔH v,i Heat of vaporization of species i [kJ/kmol] k Turbulent kinetic energy [m 2 /s 2 ] MW i Molecular weight of species i [g/mol] m chamber Mass of the DHP chamber walls [kg] N cond;i c i ð Þ Molar condensation rate of species i [kmol/s] N l,i Molar amount of species i in the liquid phase [kmol] Q loss Heat loss [W] p Pressure [Pa] p i Vapor pressure for species i in a liquid mixture [Pa] p i sat Vapor pressure of pure species i [Pa] p tot Total pressure [Pa] ~ R Reynolds stress tensor [m 2 /s 2 ] R Molar gas constant, 8.314472 [J/mol . K] R gas Gas constant for air, 287.05 [J/kg.K] Ra Rayleigh number J Pharm Innov (2009) 4:5162 DOI 10.1007/s12247-009-9057-3 S. Radl : R. Sungkorn : J. G. Khinast (*) Institute for Process and Particle Engineering, Graz University of Technology, 8010 Graz, Austria e-mail: [email protected] URL: S. Ortner Ortner Cleanrooms Unlimited, Uferweg 7, 9500 Villach, Austria

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    The Engineering of Hydrogen PeroxideDecontamination Systems

    Stefan Radl & Stefanie Ortner & Radompon Sungkorn &Johannes G. Khinast

    Published online: 16 June 2009# International Society for Pharmaceutical Engineering 2009

    Abstract In this article, the latest developments fordesigning hydrogen peroxide decontamination systems areanalyzed. Specifically, focus is given to the accuratecalculation of hydrogen peroxide condensation phenomenaand discussion of a new correlation for its accurateprediction. A procedure for calculating the condensatecomposition or the dew point out of this correlation isdetailed, and an hx diagram for moist, hydrogen peroxide-laden air, which is of fundamental importance for therational design of hydrogen peroxide decontaminationsystems, is proposed. Also presented are theoretical resultsthat illustrate the effect of condensation and evaporation inthese systems. Finally, some perspectives for improvinghydrogen peroxide systems, and the role computationalfluid dynamics (CFD) may have in this field, are provided.

    Keywords Hydrogen peroxide . Decontamination .

    Pharmaceutical engineering . Condensation .

    Mollier hx diagram


    Latin lettersADHP Total inner surface area of the DHP

    chamber [m]

    Bj Parameters of the RedlichKister equation[J/kmol]

    ci Concentration of species i in the gas phase[mg/l]

    cisat Saturation concentration of species i over the

    liquid film [mg/l]cp,i Heat capacity of species i in the gas phase

    [kJ/kmol.K]cp,chamber Heat capacity of the chamber wall material

    [kJ/kg.K]C Dimensionless concentration of inlet gasC, C1,C2

    Constants for the turbulence model

    Di Diffusion coefficient of species i in air [m2/s]

    f Target function for the dew point iteration [Pa]g Gravitational acceleration [m/s2]h Specific enthalpy [kJ/kmol]h1+x Enthalpy [kJ/kgdry air]Hv,i Heat of vaporization of species i [kJ/kmol]k Turbulent kinetic energy [m2/s2]MWi Molecular weight of species i [g/mol]mchamber Mass of the DHP chamber walls [kg]N cond;i ci Molar condensation rate of species i [kmol/s]Nl,i Molar amount of species i in the liquid phase

    [kmol]Qloss Heat loss [W]p Pressure [Pa]pi Vapor pressure for species i in a liquid mixture

    [Pa]pisat Vapor pressure of pure species i [Pa]

    ptot Total pressure [Pa]~R Reynolds stress tensor [m2/s2]R Molar gas constant, 8.314472 [J/mol.K]Rgas Gas constant for air, 287.05 [J/kg.K]Ra Rayleigh number

    J Pharm Innov (2009) 4:5162DOI 10.1007/s12247-009-9057-3

    S. Radl : R. Sungkorn : J. G. Khinast (*)Institute for Process and Particle Engineering,Graz University of Technology,8010 Graz, Austriae-mail: [email protected]:

    S. OrtnerOrtner Cleanrooms Unlimited,Uferweg 7,9500 Villach, Austria

  • Sct turbulent Schmidt numberT temperature [K]~U Velocity vector [m/s]Vc Chamber volume [m

    3]V j Volumetric flow rate [m

    3/s]wi Mass fraction of species iX Absolute moisture content of the air

    [g/kgdry air]xi Molar fraction of species i in the liquid phaseyi Molar fraction of species i in the gas phaseZ Height level [m]

    Greek letterseff Effective energy diffusion coefficient [W/m.K] Heat transfer coefficient [W/m2.K]i Mass transfer coefficient [m

    2/s] Energy dissipation rate [m2/s3]8 Mass flux vector [kg/m2.s]i Activity coefficient for species i Turbulent diffusion coefficient [kg/m.s]air Heat conductivity of air [W/m.K]t Turbulence viscosity [Pa

    .s] Density [kg/m3]k, Constants for the turbulence model


    In the (bio-)pharmaceutical industry, the efficient decon-tamination of surfaces in lock lines, rooms, and laboratoriesis of central importance. The main challenge in theseapplications is finding a reliable decontamination medium.Currently, sterilization approaches based on gas-phasedecontamination are drawing significant interest. Use ofhydrogen peroxide as a decontamination medium since thelate 1980s has been attributed to its simplicity and itsenvironmentally friendly decontamination products ofwater vapor and gaseous oxygen [1, 2].

    In the past decade, decontamination by hydrogenperoxide (DHP) has been used frequently for sterilizationpurposes in a wide range of applications [38]. In theseprocesses, an aqueous hydrogen peroxide solution, typical-ly 35% (w/w) H2O2, is evaporated, brought into contactwith a hot gas stream, and fed into the containment to besterilized. This process is often called gassing. After-wards, the chamber (here, referred to as DHP chamber) ispurged with air until the hydrogen peroxide level is belowthe OSHA safety level of 1 ppm. This process is referred toas aeration.

    There has been much interest in the theoretical analysisof the evaporation of aqueous hydrogen peroxide solutionsdue to its relevance for DHP systems [912]. However,there are only a few practical guidelines for the proper

    design and development of hydrogen peroxide decontam-ination systems. In this paper, we provide a guide on howto accurately calculate the gasliquid equilibrium anddiagrams that may help engineers to successfully implementDHP in cleanrooms. Furthermore, perspectives for improv-ing DHP technology towards a more controllable andreliable method for surface decontamination are presented.

    Improved Prediction of the GasLiquid Equilibrium

    For the description of the gasliquid equilibrium betweenan aqueous hydrogen peroxide solution and its vapor, alarge amount of data exists [13, 14]. Often, the descriptionis based on the concept of activity coefficients for thedescription of non-ideal liquid mixtures. Using this con-cept, the vapor pressure for water and hydrogen peroxideover an aqueous solution of hydrogen peroxide can becalculated following Eq. (1).

    pi psati gi xi 1

    Here, pi, i, and xi are the vapor pressure, activitycoefficient, and mole fraction, respectively, for species i(being either water or hydrogen peroxide) in the mixture,and pi

    sat is the vapor pressure of the pure liquid of species iat a given temperature. Values for pi

    sat for water andhydrogen peroxide can be obtained from various sources,e.g., in Manatt and Manatt [15, 16].

    Having the vapor pressure for species i calculated usingEq. (1), the molar fraction of the species in the gas phase,yi, can be calculated using Daltons law:

    yi piptot 2

    Here, ptot is the measurable total ambient pressure, whichis generally accepted as 1.013105Pa. The assumption ofnon-interacting species required for use of Daltons law isjustified by the fact that in hydrogen peroxide applicationsthe pressure is usually atmospheric or near atmosphericconditions. Using the ideal gas law, the concentration ofspecies i in mg/L can be calculated as:

    ci pi MWiR T 3

    Here, MWi is the molecular weight of species i (g/mol),R is the molar gas constant (8.314472 J/mol.K), and T is theabsolute temperature in K.

    For a given aqueous solution at a certain temperature,pisat and xi are known and the activity coefficients i must

    be calculated; this is most conveniently done by fittingparameters of a theoretically derived function to experi-mental data. Recently, Manatt and Manatt [16] reviewed the

    52 J Pharm Innov (2009) 4:5162

  • available correlations for the activity coefficients ofaqueous hydrogen peroxide solutions and discussed theiraccuracy. They concluded that the initial parameter set ofScatchard et al. [13], frequently used today, is inaccurate,especially at low temperatures. For example, the deviationfrom the exact vapor pressure at 25C and can be as high as14%. This would translate into a 14% error in the hydrogen

    peroxide concentration in the gas phase. Hence, for a moreaccurate analysis of phenomena in DHP systems, it may benecessary to use improved correlations.

    A four-parametric activity coefficient model presentedby Manatt and Manatt [16] had been found to fit the datamore accurately. It is based on the RedlichKister equationand can be written as:

    ln gH2O2 x2H2O

    R T B0T B1T 3 4 xH2O B2T 1 2 xH2O 5 6 xH2O B3T 1 2 xH2O 2 7 8 xH2O


    ln gH2O 1 xH2O 2

    R T B0T B1T 1 4 xH2O B2T 1 2 xH2O 1 6 xH2O B3T 1 2 xH2O 2 1 8 xH2O


    The four parameters B0 to B3 are temperature dependentand details can be found in Manatt and Manatt [16]. Itshould also be mentioned that the fitted parameters of theactivity coefficient model depend slightly on the correlationused for determining the vapor pressure pi

    sat. Therefore, oneshould use the same vapor pressure correlations as given inManatt and Manatt [15].

    To illustrate the accuracy of this set of equations, wecompare experimental data to the calculated values. Theexperimental data date back to the work of Scatchard et al.[13]. These researchers used a special adapted equilibriumstill to account for the characteristics of the hydrogenperoxide solutions. The comparison of the total vaporpressure and the molar fraction of hydrogen peroxide in thegas phase is shown in Table 1. For this example, we havekept the mole fraction of hydrogen peroxide in the liquidphase constant (xH2O2=0.7, equal to a mass fraction ofwH2O2=0.815). The error between the calculation and theexperimental data is very small, typically below 0.2%.

    A comparison of the calculated data, the experimentaldata, and the data computed by Hultman et al. is given inTable 2. The comparison is shown for a constant temper-ature of 25C and different compositions of the liquidphase. Clearly, the correlation used by Hultman et al.predicts values significantly below those of the experimen-tal data. This is especially true for low liquid mass fractionsof hydrogen peroxide. Conversely, the correlation ofManatt and Manatt, used in this work, shows much betteragreement with the experimental data.

    Calculation of Condensate Composition and Dew Point

    Using Eqs. (1), (2), (3), (4), and (5), one is able to calculatethe molar fraction of hydrogen peroxide and water vapor inan inert gas using a suitable computer program. Furthermore,it is also possible to calculate the composition of thecondensate in equilibrium with gas having a certaincomposition. However, this can be only done by an iterativeprocedure, as Eqs. (4) and (5) are non-linear with respect tothe liquid-phase composition. For example, starting withactivity coefficients equal to unity, one can estimate theliquid-phase composition using Eqs. (1) and (2). Then, theactivity coefficients can be evaluated and the procedure isrepeated. The exact values are obtained after a few iterations.

    Calculating the dew point, i.e., the temperature where agiven composition of the moist, H2O2-laden air starts tocondense, is a little more demanding. In this case, thefollowing procedure is recommended:

    1. The dew point for water vapor should be calculatedbased on the given molar fraction of water vapor. This

    Table 1 Comparison of experimental data from Scatchard et al. andcalculated values for the total pressure and the gas-phase compositionyH2O2 for a mole fraction of xH2O2=0.7


    Total pressure[bar] (exp.)

    Total pressure[bar] (calc.)



    10 0.002768 0.002768 0.2356 0.2355

    20 0.005457 0.005458 0.2489 0.2487

    30 0.010215 0.010222 0.2616 0.2613

    40 0.018184 0.018204 0.2749 0.2744

    The correlations of Manatt and Manatt for aqueous hydrogen peroxidesolutions has been used for the calculations

    J Pharm Innov (2009) 4:5162 53

  • can be done using Eq. (2) and an appropriate vaporpressure correlation for water. The obtained dew pointtemperature is the starting value for the followingiteration.

    2. The composition of the condensate should be calculat-ed with the previously calculated dew point tempera-ture from step 1. The activity coefficient for this firstcalculation should be chosen to be unity.

    3. The dew point temperature should be calculated using aNewton iteration, i.e., the corrected temperature Tn+1 atiteration n is

    Tn1 Tn ff 0


    The target function f for this iteration is:

    f T ; xi Xi

    pi T ; xi Xi

    yi ptot 7

    If Eq. (7) is equal to zero, the dew point has been found.For evaluating the corrected temperature using the Newtonalgorithm, we need the derivative f of the target function fwith respect to the temperature (see Eq. (6)). As ptot and yiare constant, this involves the calculation of derivatives ofthe pi(T, xi) terms. These derivatives are complex functionsof the temperature and the molar fractions xi which cannotbe evaluated easily. As a suitable approximation for thederivative of the target function f, the derivative of thevapor pressure correlation for water can be taken instead.This converges quickly, although use of an appropriateunder-relaxation factor is suggested, as the algorithm canget unstable in some cases.

    hx Diagram for Hydrogen Peroxide in Air

    As has been shown in the previous section, the evaluationof equilibrium data is a complex task. It is therefore

    instructive to use computer programs for this purpose.However, it is also convenient to use appropriate diagramsthat can be used quickly for solving practical problems.One of the most useful diagrams for solving such problemsin the heating, ventilating, and air conditioning (HVAC)industry is Molliers hx diagram for moist air. Thisdiagram is commonly used for calculating processesinvolving mixing, cooling, heating, or humidification ofairall processes encountered in DHP applications. Fur-thermore, bio-decontamination by hydrogen peroxide andwater vapor is of interest [11]. Hence, an hx diagram formoist, H2O2-laden air would be beneficial for engineeringand developing DHP systems.

    An hx diagram provides information on condensationphenomena (which can be described with equilibrium dataas presented in the section entitled Improved Prediction ofthe GasLiquid Equilibrium) and the enthalpy h1+x ofmoist air in kJ/kgdry air. Here 1+x indicates that enthalpyis calculated for both dry air and the moisture content X, buton the basis of 1 kg of dry air, where X is the absolutehumidity in gH2O/kgdry air. The underlying assumptions ofan hx diagram are that (a) a possible condensate (i.e., fog)is in thermal and thermodynamic equilibrium with thegaseous phase, and (b) all phases, i.e., the gas and apossible condensate, are well mixed.

    For describing the enthalpy of the mixture of air, water,and hydrogen peroxide vapor, we used data on the heatcapacity cp. In this work, cp was accurately modeled usingtemperature-dependent functions. These functions havebeen taken from Liley et al. [17, Table 2-198]. The specificenthalpy can be found from these functions by simpleintegration and subsequent summation over all involvedspecies (hydrogen peroxide, water, nitrogen, and oxygen).The standard conditions for the enthalpy, i.e., the state werethe enthalpy is zero, was chosen to be 0C for both waterand hydrogen peroxide in the liquid state. For the heats ofvaporization at 0C (denoted as Hv,i), values of 45.025and 53.200 MJ/kmol were used for water and hydrogenperoxide, respectively. Hence, the specific enthalpy in kJ/kmol of the moist air is given by

    hT Xi

    yi ZT



    1AHv;w yw Hv;H2O2 yH2O2


    Finally, this specific enthalpy was multiplied with thenumber of moles in 1+X (kg) of moist air to find the valuefor the enthalpy h1+x used in the diagram. It is noted herethat, in the case of fog formation, additional terms for theprecipitating liquid phase have to be considered. However,this is not discussed here in detail as it is of little relevancefor typical DHP applications.

    Table 2 Comparison of data on the gas-phase composition over anaqueous hydrogen peroxide solution at 25C for different liquid phasecompositions

    H2O2 massfractionliquid phase

    H2O2 massfraction gas phase(Hultman et al.)

    H2O2 massfraction gasphase (exp.)

    H2O2 massfraction gasphase (calc.)

    0.321 0.0187 0.0216 0.0216

    0.557 0.0800 0.0964 0.0961

    0.739 0.2410 0.2638 0.2635

    0.778 0.3500 0.3307a 0.3231

    0.883 0.5640 0.5684 0.5679

    a This value has been linearly interpolated from the experimental data

    54 J Pharm Innov (2009) 4:5162

  • In an hx diagram for air and water vapor, lines ofconstant temperature and constant relative humidity areplotted. The enthalpy in hx diagrams for moist air is in kJ/kgdry air and the absolute humidity X is denoted in gH2O/kgdry air. These notations enable one to perform simplecalculations, e.g., those for a mixing process, directly in thediagram. To expand on this concept, we define the absolutemoisture (including water and hydrogen peroxide) of the airin g(H2O + H2O2)/kgdry air. As in the hx diagram for air andwater vapor, the lines of constant enthalpy have beendesigned to have a constant negative slope such that theisotherm at 0C is horizontal.

    An hx diagram for a total pressure of 1105Pa andinjection of an aqueous hydrogen peroxide solution of 35%(w/w) is shown in Fig. 1. We have assumed that the air isabsolutely free of water before the hydrogen peroxide

    solution is injected and that no hydrogen peroxide isdecomposed during evaporation. Only under these assump-tions is the ratio between water and hydrogen peroxidevapor constant, which enables a simple construction of thehx diagram. To facilitate the use of the diagram, thehydrogen peroxide volume fraction in ppm has been addedas a second x-axis.

    An example that illustrates the use of the diagram isshown in Fig. 2. Starting with dry, H2O2-free air at state (1),aqueous hydrogen peroxide solution is injected into this airstream (12). This process is modeled as isenthalpic (i.e.,constant enthalpy), because the enthalpy of the injectedliquid is typically very small compared to the energycontent of the air. Due to evaporation of the solution, thetemperature of the moist air after the injection (2) issignificantly lower than before (1) and is cooled down to

    Fig. 1 hx Diagram for moist,hydrogen peroxide-laden air(1105Pa, 35 wt.% H2O2 in theinjected aqueous solution)

    J Pharm Innov (2009) 4:5162 55

  • ambient temperature (3). This process is observed inpractice when the sterilizing medium (the moist, H2O2-laden air) is blown into the containment to be sterilized. Insuch a case, rapid cooling of the sterilizing medium isachieved by the walls of the containment to be sterilized. Inmost cases, the process is designed to allow a maximumrelative humidity of 90% to ensure that no condensationcan occur; state (3) indicates a case close to 100%saturation. Consequently, the injected amount of hydrogenperoxide solution must be controlled with respect to thetemperature of the walls.

    To illustrate a mixing process, we now focus on themixing of a high-moisture air stream (4) with air of state(3). The rule for determining the mixing point (5) is that wefirst have to connect the two initial states with a straightline. This rule follows from the energy balance and is notdiscussed here in detail. Second, the absolute moisturecontent Xmix at the mixing point can be calculated from asimple mass balance:

    Xmix X1 mdry air;1 X2 mdry air;2mdry air;1 mdry air;2


    Here, mdry air;j denotes the mass flow of dry air in thestream j. In our case, we assumed to have equal amounts ofdry air in each of the two streams to be mixed. Here, state(5) has an absolute moisture content of 12 g(H2O+H2O2)/kgdry air and is fully defined. In this example, the mixingpoint is in the fog region and condensate will form at thewalls of the containment. Thus, this example shows that itis possible to obtain condensing conditions even if the twoinitial gas streams are below saturation. This situation mayoccur if gas with very high moisture content is mixed with

    air in a compartment that is already filled with sterilant.Hence, care has to be taken in such cases.

    Mass and Heat Balances for a Well-Mixed Chamber

    Despite the fact that the hx diagram presented in theprevious section may be beneficial for everyday practice, ithas its limitations. For example, it is valid only for aconstant ratio between water and hydrogen peroxide vapor.As a consequence, the effect of pure water injection cannotbe studied. Also, changes of the gas composition duringcondensation cannot be predicted, because more hydrogenperoxide will condense compared to water vapor due to thechemical equilibrium described in previous sections.Hence, the ratio of these two species in the gas phase willalso change during condensation. In these cases, it isnecessary to use computer programs based on exact heatand mass balances. Such programs have been developed in-house and validated with experimental data. The novelty ofour computer program is that we take into account heat andmass transfer to surrounding objects, e.g., walls.

    In order to predict the conditions in the chamber, massand energy balances have to be solved in addition to thechemical equilibrium discussed before. In DHP applica-tions, the accumulation of hydrogen peroxide and watervapor in the containment is important. Unsteady mass andenergy balances have to be used, and it is necessary tomodel gas mixing in the containment, as mixing willinfluence the concentration of the gas leaving the chamber.Furthermore, heat and mass transfer has to be modeled. Inthe current work, we have decided to follow two routes formodeling these phenomena:

    1. A simplified analysis in which we assume a perfectlymixed gas phase, a uniform composition of a possiblecondensate that forms, as well as constant heat andmass transfer coefficients.

    2. A refined analysis where we use computational fluiddynamics (CFD) to predict gas-phase mixing, as well asheat transfer to walls. In this analysis, we exclude masstransfer, i.e., condensation, from our analysis.

    In this section, we expand on the simplified analysis (therefined analysis is described in the next section of ourpaper) by assuming the simplest case of mixing, i.e., aperfectly mixed chamber. In this case, the decontaminationchamber behaves similar to a continuously stirred tankreactor (CSTR). In this case, the time-dependent materialbalance for species i can be written as:

    Vc dcidt Xj

    V j ci;j X


    Vk ci;k N cond;i ci 10










    0 2 4 6 8 10 12 14 16 18 20X [g(H2O + H2O2) / kgdry air]

    h 1+x


    / kg d

    ry a


    20 [C]

    30 [C]

    40 [C]

    50 [C]

    60 [C]

    70 [C]

    100 [%]80 [%] 60 [%]

    40 [%]30 [%]

    20 [%]15 [%]10 [%]5 [%]saturation




    60 50

    40 30



    50 [%]2






    110 1

    Fig. 2 Injection of H2O2 (12), cooling (23), and mixing (345) inthe hx diagram for moist, hydrogen peroxide-laden air

    56 J Pharm Innov (2009) 4:5162

  • Here, Vc stands for the gas volume inside the chamberand dci/dt is the time derivative of the molar concentrationof species i in the gas phase.

    V j and

    Vk denote the

    volumetric flow rate of the gas feed j and the effluent k,respectively. ci,j and ci,k denote the molar concentrations inthose gas streams. We have included condensation effectsvia

    N cond;i ci , which is the molar condensation rate for

    species i. This is relevant for some DHP applications wherecondensation is allowed. The condensation rate of hydro-gen peroxide is

    N cond;H2O2 ci ADHP bH2O2 cH2O2 csatH2O2


    The condensation and evaporation rates of water can becalculated in a similar way. Hence, the model describedhere is applicable to each phase of the DHP process, i.e.,gassing as well as aeration.

    In Eq. (11), ADHP denotes the total inner surface area tobe decontaminated. The underlying assumption for re-evaporation of possible condensation is that this total innersurface is completely wetted with condensate. In Eq. (11),H2O2 is the mass transfer coefficient, and cH2O2

    sat is thesaturation concentration of hydrogen peroxide above thecondensate film. Note that cH2O2

    sat can be calculated basedon the chemical equilibrium and the actual composition ofthe condensate film. For the calculation of H2O2, however,further approximations have to be made. In the currentwork, we have calculated the mass transfer coefficientusing an analogy relation between heat and mass transfer[18]. Specifically, we assume that the Nusselt and Sher-wood numbers are equal for all species. This is a reasonableapproximation for the gases and conditions typicallyobserved in DHP chambers. Under this assumption wecan calculate H2O2 as:

    bH2O2 a DH2O2


    where denotes the heat transfer coefficient, DH2O2 is thediffusion coefficient of hydrogen peroxide in air, and lair isthe heat conductivity of air. While DH2O2 and lair can betaken from numerous chemical engineering textbooks orcorrelations [17, 19], the heat transfer coefficient dependson the specific flow conditions within the chamber.However, the flow conditions are not known and can bepredicted only with the use of specialized techniques, e.g.,CFD. In the current work, we have used a value for mixedconvection of =10 W/m2K.

    To account for the accumulation of water and hydrogenperoxide in the condensate film, we define a mass balancein the liquid film for each species:


    N cond;i ci 13

    Here Nl,i stands for the molar amount of species i in theliquid film. Using Eq. (13), the composition of thecondensate film xi is:

    xi Nl;iPiNl;i


    When using Eq. (14), we assume a uniform compositionof the condensate film, i.e., an ideally mixed liquid phase.Using the composition of the condensate film, the satura-tion concentration of hydrogen peroxide cH2O2

    sat can becalculated and used in Eq. (11). By numerically integratingEqs. (10), (11), (12), (13), and (14), the concentration timeprofiles in the gas phase, as well as in the condensate film,can be determined.

    Finally, the energy balance should be considered toaccount for heating of surrounding walls and the gas in thecontainment. This is important, as the chemical equilibriumdepends strongly on the temperature. For this purpose, wehave made the assumption that the gas in containment hasthe same temperature as the surrounding walls. This is areasonable assumption, as the convective heat transfer tothe walls is relatively fast. Also, CFD simulations (to bedetailed in the final section of this paper) show that themean temperature is only negligibly higher than the walltemperature. Hence, we can write an integral energybalance over the control volume and the walls:

    mchamber cp;chamber dTdt Xj

    hTj V j ci;j


    hTk Vk ci;k

    15Here, mchamber denotes the total mass of the surrounding

    walls in thermal equilibrium with the gas, whereas the massof the contained gas is negligible. In Eq. (15), cp is theknown heat capacity of the surrounding walls (usuallymade of steel). Furthermore, h(T)j and h(T)k denote thespecific enthalpy of the streams j and k. Also, Eq. (15) hasto be integrated over time to calculate the temperatureduring the decontamination cycle. Furthermore, this inte-gral energy balance accounts for the heat of condensation,if any, as the latter is already considered in the calculationof the specific enthalpies h(T)j and h(T)k.

    Figure 3 shows example time profiles of condensate andgas composition during aeration of a DHP chamber.Figure 3a is a result of the calculation with the simplifiedapproach presented in this paper, whereas Fig. 3b refers tomeasurements in an experimental setup involving a lab-scaleDHP chamber. The lab-scale DHP chamber consisted of atemperature-controlled box made of stainless steel ( m in dimension) connected to a H2O2 generator(Geschko MLT 07, PEA GmbH, Calw, Germany). The

    J Pharm Innov (2009) 4:5162 57

  • chamber was designed and operated such that it resembledthe situation in a real industrial application of a lock line.The H2O2 concentration was measured using an electro-chemical sensor (an ATI C16 PortaSens II Portable GasDetector from Analytical Technology Inc., Collegeville, PA,USA). The results shown in Fig. 3 refer to a situation wherecondensation of hydrogen peroxide and water vapor hasalready occurred in the DHP chamber. Consequently, thecondensate has to re-evaporate during aeration. As can beseen from Fig. 3a, the predicted hydrogen peroxide andwater content of the gas phase reach their maximum afterabout 1.5 min due to evaporation at the wall interface. Asimilar phenomenon, i.e., an increase of the hydrogenperoxide concentration during the aeration phase, has beenobserved in the experimental setup (see Fig. 3b). However,the time scale for evaporation in the experimental setup isapproximately twice that of the simulation, possibly attrib-uted to only partial wetting of the walls and, consequently, asmaller interfacial area for evaporation in the experiment. Asa result, the mass transfer rate to the air is smaller (refer toEq. (11)), and it takes longer for the condensate to re-evaporate. Furthermore, as was illustrated with our calcu-lations, as water evaporates more rapidly from the liquidphase, the hydrogen peroxide content therein increasesabruptly and theoretically approaches 100% (see Fig. 3a). It

    should be noted that our model was neither calibrated withnor fitted to any experimental data. Hence, there is thepossibility to further improve the reliability of our model,which is currently ongoing.

    Perspectives for Improved DHP System Design

    The program described is beneficial for quick estimates ofthe conditions in a decontamination chamber. However, dueto the underlying assumption of an ideally mixed gas insidethe chamber, it cannot provide answers regarding the spatialdistribution of the decontamination medium and effectssuch as local condensation. However, the local concentra-tion and the existence of localized liquid phases determinethe sterilization kinetics of the microorganisms and,consequently, the cycle time needed for a reliable decon-tamination. Clearly, for optimizing the decontaminationcycle, this spatial distribution is critical. For example, if thegas distribution inside the containment to be decontami-nated is unsatisfactory (e.g., due to dead zones), some areasmay not be treated with the decontamination mediumsufficiently. As a result, cycle times may need to increasesignificantly based on the worst case (i.e., the locationwhere the concentration is lowest).

    Gas distribution and mixing of gases is difficult topredict, especially for complex geometries. Furthermore,these processes are dominated by turbulence and, mostimportantly, by buoyancy forces due to temperaturegradients. The only way to safely predict mixing in locksand rooms is the use of sophisticated computational fluiddynamics (CFD) simulations in combination with scalartransport codes. To underline the effects of buoyancy inDHP applications, we detail here on our studies involvingCFD simulations with an accurate modeling of buoyancyeffects. Our code is based on the open-source softwareOpenFOAM [20] augmented by the meshing tool Cubit[21]. In addition, a modified solution strategy has beendeveloped to handle buoyancy-driven flows more efficient-ly. We have used the steady-state Reynolds-AveragedNavierStokes (RANS) equations to model the multicom-ponent gas flow in a typical decontamination chamber. Themodel equations consist of the continuity equation:

    r r~U 0 16

    as well as the steady-state momentum conservationequation:

    r ~U r r~R rprrgz 17Here, p, g, , and


    R refer to the pressure, thegravitational acceleration, the mass flux, and the Reynoldsstress tensor, respectively, and z denotes the local height








    011:33 11:36 11:39 11:42 11:45 11:48 11:51

    Time [hh:min]










    Fig. 3 Time profiles for gas and liquid phase composition duringaeration (condensation has occurred during the gassing phase). aCalculation, b experimental data for the hydrogen peroxide concen-tration in the gas

    58 J Pharm Innov (2009) 4:5162

  • above an arbitrary reference level. The term gz modelsthe buoyancy force, i.e., the force due to differences in thedensity . The mass flux vector is the product of thevelocity vector ~U and the density. The Reynolds stresstensor ~R mimics the turbulent fluid motion, which is notdirectly calculated. Hence, the Reynolds stress term has tobe modeled. In our work, the k turbulence model hasbeen used. The selection of this turbulence model ismotivated by its economy and reasonable accuracy androbustness for a wide range of turbulent flows. For the kturbulence model, two additional transport equations for theturbulent kinetic energy k, as well as for its dissipation rate, have to be solved:

    r r *Uk

    r mtsk


    2mt r*

    U r *U T



    r r *U"

    r mts"


    C1" "k 2mt r*

    U r *U T

    C2"r "2


    19Here, the turbulence viscosity t is defined as:

    mt r Cm k2


    The Reynolds stresses can be calculated using theextended Boussinesq relationship:

    ~R 23 k I 2 mt

    rr *U r *U

    T 21

    where I denotes the unity tensor. The model constants forthe k turbulence model were C=0.09, k=1.0, =1.3,C1=1.44, and C2=1.92.

    We have used wall functions to approximate the velocityprofile near the wall and to set the boundary conditions forthe turbulence model. With this approach, the wall shearstress is calculated from the assumption of a logarithmicvelocity profile near the wall. This approach is suitable formost industrial applications of CFD and is widely used inthat field.

    In order to model buoyancy effects, the local density has to be known. In our work, we have used the ideal gaslaw to calculate :

    p r Rair T 22Here, Rair denotes the specific gas constant for air,

    which has a value of 287.05 J/kgK. As pressure differ-ences in the chamber will be very small (usually in theorder of several Pa), the influence of the pressure can besafely neglected. Hence, the air in the chamber will betreated as incompressible. Furthermore, the concentration

    of water and hydrogen peroxide vapor is very small intypical DHP applications (in the order of 0.1% to 0.3%)and the local concentration will have only a smallinfluence on the density. This can also be seen from thehx diagram (Fig. 1), in which lines of constant density arealmost parallel to lines with constant temperature. Thus, thecomposition of the gas does not significantly alter the densityof the multicomponent gas mixture. The major influence onthe density will be from temperature differences, which ismodeled directly via the ideal gas law.

    The information on the local temperature T is providedfrom the steady-state energy conservation equation:

    cp r T r aeffrT 0 23Here, cp is the specific heat capacity under constant

    pressure for air, which is assumed to be constant in ourCFD studies. The effective energy diffusion coefficient effis defined as:

    aeff cpmt lair 24In our study, we have used constant temperature

    boundary conditions on all walls and the outlet, as well asconstant temperature at the inlet. Steady-state equations forgas flow and temperature distribution in the chamber havebeen used. This enables very time-efficient simulationsneeded in industrial practice. We have also quantified theeffect of the transient development of the velocity andtemperature field in a DHP chamber and found that theflow is fully developed within a few minutes. This is muchshorter than usual gassing times that can be up to a fewhours for large rooms.

    To study the concentration distribution of H2O2 vaporinjected into the chamber, the assumption that H2O2 vaporis inert has been adopted. This limits our work to DHPsystems where condensation and re-evaporation in thechamber is excluded, i.e., so-called dry systems. However,from the local distribution of hydrogen peroxide vapor inthe gas phase predicted with our CFD simulations, criticalareas prone to condensation effects can be anticipated.Consequently, we write the species transport equation foran inert scalar C as follows:


    r C r rC 0 25

    Here, C and represent the dimensionless concentration,i.e., the concentration of H2O2 normalized with the inletconcentration, and the turbulent diffusion coefficient,respectively. is represented by the following equation:



    where the value of the turbulent Schmidt number Sct is 0.7[22]. We have used the zero gradient boundary condition on

    J Pharm Innov (2009) 4:5162 59

  • all walls and the outlet, as well as a constant compositionboundary condition at the inlet. After successful implemen-tation of the algorithm into the CFD software, validation ofthe code was performed. This was done by comparison ofour results with experimental results, as well as with areference numerical solution of Corvaro and Paroncini [23]for a buoyancy-driven flow. Details on this validation aredocumented in the Appendix of this paper.

    Selected snapshots of some simulations conducted by usare shown in Fig. 4 and Fig. 5. For this purpose, we havemodeled a typical industrial DHP chamber with a nominalvolume of 3 m3 and a gassing flow rate of 20 m3/h. Thesimulations were performed on a sufficiently fine numer-ical grid to give a grid-independent solution. In Fig. 4a,the flow field for an isothermal inlet condition, i.e., the gasflowing into the DHP chamber has the same temperatureas the chamber itself, is illustrated. A plot of the localdistribution of the magnitude of the velocity fieldillustrates convective transport in the system. In contrastto this isothermal flow, we show the flow field for an inlettemperature of 75C in Fig. 4b. This temperature is typicalfor most dry hydrogen peroxide decontamination sys-tems where the air is preheated to avoid condensation inthe gas piping and the DHP chamber. Clearly, this changein the inlet temperature causes a significant alteration ofthe flow field. In Fig. 4b, the main flow direction istowards the top region of the chamber due to the lowerdensity of the warm inlet air. In the bottom region of thechamber, there is almost no gas motion; here, noconvective transport is possible.

    The corresponding distribution of the inlet gas isshown in Fig. 5. As expected, the distribution of the inletgas reflects also the low convective transport in the case ofa higher gas inlet temperature. In conclusion, oursimulations show that it is critical to take buoyancyeffects into account when dealing with gas flow in DHPchambers.


    The engineering of an optimal DHP system is a difficulttask. As has been shown in this work, buoyancy effectsplay a significant role in DHP chambers operated at typicalprocess conditions, i.e., at inlet temperatures of approxi-mately 75C. Such effects completely change the flowpattern and, consequently, the distribution of the inlet gas.Hence, we suggest paying attention to given processparameters like temperature, flow rates, and the H2O2 inletconcentration during the engineering phase of DHPsystems.

    In the current work, we have focused on differentmodeling strategies for DHP systems. In our models, wehave incorporated an advanced description of the chemicalequilibrium of aqueous hydrogen peroxide solutions. Wedemonstrated how this improvement for previously pub-lished models leads to more reliable calculations for thewater/hydrogen peroxide system. Furthermore, we haveillustrated that even a relatively simple model assuming awell-mixed gas phase in a DHP chamber can reproduceexperimental data qualitatively well, and that an hxdiagram can be constructed for moist, hydrogen peroxide-laden air. This diagram will be of benefit for both engineersand end users of DHP systems. However, future work willhave to consider partial wetting of the chambers internalsto quantitatively predict complex phenomena like re-evaporation of condensate.

    For an optimized design and inclusion of DHP systemsin clean rooms, it is clear that CFD must become a standardtool to predict flow and mixing in those systems. As inother industries, where CFD is already well established, thecomplex interactions between fluid dynamics, heat transfer,turbulence, and buoyancy forces will force engineers to usemore sophisticated tools to design DHP systems. However,as the current work is one of the first systematic studies inthis area, we were not able to incorporate all aspects of flow


    U [m/s]U [m/s]

    Fig. 4 Comparison of the mag-nitude of the velocity vector inan industrial lock line duringgassing using CFD. In the leftfigure (a), the gas has beeninjected with the same tempera-ture as the air in the lock line(no buoyancy effects), whereasin the right figure (b), the inlettemperature was 75C (strongbuoyancy effects)

    60 J Pharm Innov (2009) 4:5162

  • as well as heat and mass transfer into our CFD simulations.Clearly, the detailed modeling of the condensation of thehydrogen peroxide/water mixture out of the inert gas (i.e.,air) still remains a challenge. Furthermore, more elaboratevalidation of CFD codes, as well as more advanced studieson the effect of different turbulence models, will beimportant to increase the reliability of such engineeringtools.

    Acknowledgement The authors acknowledge financial support ofOrtner Cleanrooms Unlimited GmbH.


    In order to validate the developed solver, a comparison ofnumerical and experimental has been performed. Therefore,

    our results have been compared with the results fromCorvaro and Paroncini [23]. The setup consisted of a squarecavity (with height of 0.05 m and a total length of 0.41 m)with a heated strip placed on the bottom wall. In the casestudied in our work, the heated strip was positioned at thecenter of the bottom wall. The fluid in the cavity is air andthe heated strip was made of brass. The side walls of thecavity were cooled and had a constant temperature of285.5 K. The temperature of the heated strip was 302.1 K,such that the resulting Rayleigh number was Ra=2.02105.

    Figure 6 shows the temperature and velocity distributionobtained with the simulations code detailed in this work.These results agree very well qualitatively with theexperimental measurements, as well as the simulationresults of Corvaro and Paroncini [23]. The computed meandimensionless heat transfer coefficient (i.e., the meanNusselt number Num) averaged over the heated strip using

    (a) (b)


    Fig. 5 Comparison of thedimensionless concentration ofthe inlet gas in an industrial lockline during gassing using CFD.a Isothermal inlet conditions(no buoyancy effects), b inlettemperature of 75C (strongbuoyancy effects)

    Fig. 6 Numerical results for athe temperature distribution andb the magnitude of the velocityfield in a heated cavity

    J Pharm Innov (2009) 4:5162 61

  • our calculations was determined as Num=6.16. Fromsimulations reported by Corvaro and Paroncini [23],Num=6.28, whereas the experimental result was Num=6.45. Hence, the differences are below 5%, which isacceptable.


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    The Engineering of Hydrogen Peroxide Decontamination SystemsAbstractIntroductionImproved Prediction of the GasLiquid EquilibriumCalculation of Condensate Composition and Dew Pointhx Diagram for Hydrogen Peroxide in AirMass and Heat Balances for a Well-Mixed ChamberPerspectives for Improved DHP System DesignConclusionAppendixReferences

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