Modeling of iodine radiation chemistry in the presence of organic compounds

Radiation Physics and Chemistry 64 (2002) 203–213

Modeling of iodine radiation chemistry in the presence of organic compounds Fariborz Taghipour, Greg J. Evans* Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College Street, Toronto, Ont., Canada M5S 3E5 Received 6 March 2000; accepted 13 July 2001

Abstract A kinetic-based model was developed that simulates the radiation chemistry of iodine in the presence of organic compounds. The model’s mechanistic description of iodine chemistry and generic semi-mechanistic reactions for various classes of organics, provided a reasonable representation of experimental results. The majority of the model and experimental results of iodine volatilization rates were in agreement within an order of magnitude. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Iodine; Organic iodide; Volatility; Modeling

1. Introduction Radioactive fission products are produced and accumulate in the fuel elements of nuclear reactors. These fission products normally remain in the reactor fuel. However, in the event of a serious accident, these hazardous radionuclides could be released. Some of the radioactivity may become airborne in the reactor containment atmosphere and a portion of this activity could be released to the environment via penetration through any impairment in the containment envelope. All fission product releases from a nuclear reactor contribute to the radiological dose. However, the most potentially significant releases are the radioisotopes of iodine due to their biosensitivity and volatility. Organic impurities, which will arise in the reactor containment aqueous phase from sources such as paints, oils, and insulation material, can have a substantial impact on iodine volatility. The organic compounds react with molecular iodine, in the presence of radiation, to form a variety of organic iodides. In past reactor accidents,

*Corresponding author. Tel.: +1-416-978-8605; fax: +1416-978-1821. E-mail address: [email protected] (G.J. Evans).

organic iodides dominated the gaseous iodine speciation. Although a large number of organic compounds may be present in a reactor containment structure, three types of organic compounds are more likely to be found: carbonyls, aromatics, and alkyl halides. Carbonyl and aromatic compounds are released in significant amounts from paints used in containment while alkyl halides are produced through the degradation of plastics or paints containing vinyl chloride. All of these species could be released into the aqueous pool in considerable quantities after an accident. The impact of radioiodine release can be controlled and minimized by understanding its behavior under reactor accident conditions. From the perspective of reactor safety, it is therefore important that iodine behavior, particularly in the presence of organic compounds, be understood and predicted. The physical and chemical conditions in containment after an accident are beyond those normally encountered and are correspondingly difficult to reproduce and study. Because of the number and range of parameters to be encompassed, it is not practical to experimentally reproduce all the possible accident sequences. Such experiments are expensive and extremely time-consuming to set up and interpret. The only practical way to

0969-806X/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 9 - 8 0 6 X ( 0 1 ) 0 0 4 9 5 - 9

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deal with this problem is a combined strategy of studying separate effects and developing, in parallel, sophisticated models that enable accident progression to be calculated taking into account the interdependence of the relevant phenomena. These separate effects studies produce data and confirm the fundamental scientific modeling, whilst the codes enable that scientific modeling to be applied to specific plants and accidents (Teague and Torgerson, 1990). Using computer codes to predict the radioactivity released following reactor accidents is common. As an example, codes such as FISSCON (Jamieson and Fluke, 1982) and SMART (Quraishi, 1990) have been used to predict the behavior of radionuclides in postulated CANDU reactor accidents. These codes account for the radiological and some of the physical aspects of fission product behavior including: the production and decay of radionuclide isotopes, their adsorption and desorption by various sinks, the retention and desorption of fission products by any emergency filters and the dispersion of radioactive material in the environment. However, these codes do not simulate the complex radiation chemistry of fission products such as radioiodine. A number of empirical and mechanistic models for the simulation of iodine behavior in irradiated systems exist. The most propounded of these models are LIRIC (Canada) (Wren et al., 1992) TRENDS (US) (Beahm et al., 1988), INSPECT (UK) (Burns and Sims, 1989), IMPAIR (Germany) (Furrer et al., 1988), and IODE (France) (Hueber et al., 1992). LIRIC is a kinetic database of iodine reactions developed at AECL. LIRIC consists of approximately 200 reactions with rate constants derived primarily from literature. LIRIC is a valuable tool for understanding iodine chemistry under reactor accident conditions. However, physical phenomena, such as transport between compartments, are not included. TRENDS models both chemical and physical features such as radiolysis effects, hydrolysis, and deposition/revaporization on aerosols and structural surfaces. TRENDS also includes a calculation of the radiation dose rate based on the fission product inventory. INSPECT simulates iodine behavior by considering thermal and radiolytic reactions for reactor accidents where the aqueous medium exists in the forms of aerosol spray, sump and reactor water, mixed and contacted with the large volume of containment air. The IMPAIR code considers six iodine species in 21 differential equations containing relevant physico-chemical behavior such as transfer from containment, droplet carry-over, liquid and gas phase deposition, aerosol phase transfer, and droplet precipitation. IODE considers empirical forms of the thermal and radiolytic reactions of iodine in the aqueous (12 reactions) and gas (2 reactions) phases, as well as physical phenomena such as mass transfer, iodine dragging by steam condensa-

tion, and iodine deposition on surfaces. While all these codes consider iodine chemistry, the chemical reaction mechanisms in LIRIC and INSPECT are more detailed. Five iodine codes (LIRIC, TRENDS, INSPECT, IMPAIR, and IODE) participated in the Advanced Containment Experiments (ACE) code comparison exercise (Fluke et al., 1992) which involved prediction of iodine behavior in a number of the Radioiodine Test Facility (RTF) experiments. The RTF is an intermediate scale facility in which iodine behavior under simulated containment accident conditions can be studied (Ball et al., 1996b). The predicted iodine partition coefficient differed by several orders of magnitude amongst the codes. While all the codes consistently predicted a low airborne iodine fraction, the codes often predicted less than the measured airborne concentration. Iodine behavior codes have since been improved and calculations using the newer versions of LIRIC (Wren et al., 1996), INSPECT (Dickinson and Sims, 1996), IMPAIR (Osetek et al., 1996; Cripps and Guntay, 1996), and IODE (Poletiko et al., 1996) are in better agreements. However, in an International Standard Problem (ISP), in which the predictive capabilities of iodine codes from eight countries were compared with results from an RTF experiment, divergence still remained (Ball et al. 1999). Although iodine radiation chemistry models have undergone considerable development, a mechanistic representation of organic iodide formation is still not possible nor is it likely to become available in the near future (Fluke et al., 1997). Semi-mechanistic models for a few individual organic compounds which are included in some models, such as LIRIC (Wren et al., 1996), are incomplete. Existing empirical models for organic iodide formations such as Deane’s model (Deane, 1991) and Postma’s model (Postma and Zavadoski, 1972) are inadequate, as they do not describe the relationships between organic radiolysis, iodine kinetics, and organic iodide formation. More importantly, although some modeling predictions have been compared with intermediate scale experimental results, no self-consistent set of experiments has previously existed for evaluating the modeling of iodine chemistry in the presence of organic compounds. The objective of this research was to develop and evaluate a kinetic-based model that simulates the radiolytic chemistry of iodine in the presence of organic compounds under conditions relevant to a reactor accident. This model was to provide a mechanistic description of iodine radiation chemistry and a semimechanistic model for organic iodide reactions. In irradiated systems the species concentrations are the result of a balance between their rates of production and elimination through relevant chemical reactions. Therefore, a kinetic approach is required for modeling the system in order to describe the relationships between the chemical conditions and steady state concentrations.

F. Taghipour, G.J. Evans / Radiation Physics and Chemistry 64 (2002) 203–213

In addition, the model may be either mechanistic, based on the representation of the underlying reactions, or empirical, based on assumed relationships that are fitted to the observed trends. An empirical approach for modeling the system based on the experimental results has some merits; simplicity is one of the greatest advantages. However, this approach only provides a description of the experimental trends rather than demonstrating the fundamental understanding of the relationships between chemical conditions and volatile iodine concentrations. Furthermore, empirical models may not be extendible beyond the experimental conditions, whereas a mechanistic model, the approach used here, may be applied outside this range with much greater confidence. The model used as a starting point included 16 of the most important radiolytic and thermal reactions of iodine along with 45 reactions representing the radiolysis of water (Taghipour and Evans, 2001b). A semimechanistic representation of approximately 30 reactions of organic iodide formation and destruction was incorporated into this inorganic reaction set. A generic approach was used in which the main reactions representative of given types of organic compounds (alkyl halides, ketones, and aromatics) were considered. The required rate constants were estimated based on representative compounds. Partitioning of volatile species and a simple representation of mass transfer between the liquid and gas phases were also incorporated into the model. An initial dissolved oxygen concentration of 2.5
104 M was assumed and given the rapid air/water mass transfer in the system the model indicated that this value should remain essentially constant. Measurements made in some early experiments confirmed that the water did in fact remain saturated with respect to dissolved oxygen. The model was evaluated and refined by comparison with the experimental results. The overall model, consisting of approximately 100 kinetic expressions representing aqueous phase reactions and interfacial transfer, was applied to the FACSIMILE (FACSIMILE, 1995) computer package. FACSIMILE converts chemical reactions into a set of differential equations and solves them by numerical integration. The FACSIMILE program is well suited to such a simulation as it is designed for modeling of complex chemical processes.

205

radiolysis of water and the G-value of the products are well established. A comparison of a number of reaction sets and G-values indicated that they are almost equivalent (Sunaryo et al., 1994). In this model, the Gvalues of Sunaryo et al. (1994) were used along with the water radiolysis reaction set suggested by Buxton and Elliot, as reported by Sunaryo et al. (1995). 2.2. Iodine reactions Most of the processes involving iodine reactions in irradiated aqueous solutions are understood and the intermediates have been determined. The rate constants for the majority of these reactions are also fairly well established. The effect of radiation on iodine chemistry is through the reactions of water radiolysis products with various iodine species. The most important iodine reactions relevant to calculating iodine concentrations in the gas and liquid phases (Evans, 1996; Wren et al., 1996; Dickinson and Sims, 1996; Ball et al., 1996a) were incorporated into the model. The iodine reaction set can be described as follows: Non-volatile iodide I is oxidized by the hydroxyl radical to produce atomic iodine (Id), which is typically in equilibrium with dI 2: I þdOH-Id þ OH ; k ¼ 7:7
109 dm3 mol1 s1 ;

ð1Þ

kf ¼ 1:2
1010 dm3 mol1 s1 ;

Id þI 2d I 2;

kr ¼ 1:1
105 s1 :

ð2Þ

Atomic iodine and dI 2 can react to form I2: Id þ Id -I2 ; d  I2

k ¼ 1
1010 dm3 mol1 s1 ;

  þd I 2 -I3 þ I ;

k ¼ 4:5
109 dm3 mol1 s1 ;

k ¼ 4:5
109 dm3 mol1 s1 ;

 Id þ d I 2 -I3 ;  I 3 2I2 þ I ;

ð3Þ ð4Þ ð5Þ

kf ¼ 7:5
106 s1 ;

kr ¼ 5:6
10 dm3 mol1 s1 : 9

ð6Þ

 The I2, dI 2 , and I3 are reduced through their reaction with superoxide (dO 2 ):

d  I2 þdO 2 - I2 þ O2 ; d  I2

 þdO 2 -2I þ O2 ;

k ¼ 6
109 dm3 mol1 s1 ; k ¼ 5
108 dm3 mol1 s1 ;

ð7Þ ð8Þ

2. Chemical reactions used in the model d   d  I 3 þ O2 -I þ I2 þ O2 ;

2.1. Water radiolysis reactions Following a reactor accident, iodine exists in dilute solution, and therefore, radiation energy is deposited into water molecules. The set of reactions for the

k ¼ 2:5
108 dm3 mol1 s1 :

ð9Þ

The I2 disproportionates through thermal reactions to produce hypoiodous acid (HOI), which dissociates to hypoiodite (OI):

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I2 þH2 O2I2 OH þHþ ; kf dm3 mol1 s1 ¼ 5:76
102 ; kr ¼ 2
1010 dm3 mol1 s1 ; 



I2 OH 2I2 þOH ; 8

kf s

1

ð10Þ 4

¼ 5
10 ;

1 1

3

kr ¼ 8
10 dm mol I2 OH 2HOI þ I ;

s ;

kf s1 ¼ 1:36
106 ;

kr ¼ 4
108 dm3 mol1 s1 ; HOI2Hþ þOI ; 10

ð11Þ

ð12Þ

kf s1 ¼ 1
101 ; 3

kr ¼ 1
10 dm mol

1 1

s ;

ð13Þ

ð14Þ

The I2 is reduced indirectly through reaction with hydrogen peroxide (H2O2): I2 OH þH2 O2 2I þIO2 H þ H2 O; kf dm3 mol1 s1 ¼ 2
106 ; kr ¼ 2:3
105 dm3 mol1 s1 ;

ð15Þ

IO2 H þ OH -I þO2 þH2 O; k ¼ 3
109 dm3 mol1 s1 :

Alkyl halides (RCl) are rapidly dechlorinated through their reaction with the hydrated electron and hydrogen atom. They can also react with dOH or dO 2 resulting in hydrogen abstraction. The following represent alkyl halide reactions in the model:  d RCl þ e aq -R þ Cl ;

k ¼ 1
109 dm3 mol1 s1 ; ð17Þ

RCl þ Hd -Rd þ Cl þ Hþ ;

HOI þ OH 2H2 O þ OI ; kf dm3 mol1 s1 ¼ 2
1010 ; kr ¼ 1:56
105 dm3 mol1 s1 :

2.4. Alkyl halides

k ¼ 1
109 dm3 mol1 s1 ;

ð18Þ

RCl þdOH-Rd Cl þ H2 O; k ¼ 1
108 dm3 mol1 s1 ;

ð19Þ

 d RCl þdO 2 -R Cl þ HO2 ;

k ¼ 1
109 dm3 mol1 s1 :

ð20Þ

It should be noted that multi-chloro organic compounds are treated the same as single-chloro molecules in the model. 2.5. Carbonyls

ð16Þ

Numerous other reactions occur and these may be important under some conditions. However, the above reactions typically dominate in aerated solutions (Evans, 1996). 2.3. Organic compound reactions Due to the large variety of different organic compounds that may be present in containment, it would be unfeasible to develop a mechanistic model that represents the contribution of each individual organic compound. Specifically, not enough mechanistic detail is known to develop such a rigorous model and considerable amounts of new experimental data would be required. Hence, in this model a semi-mechanistic approach is taken in which generic reactions are used to represent different types of organic compounds (alkyl halides, carbonyls, and aromatics). The semi-mechanistic model contains the major radiolysis reactions for different classes of organic compounds in aqueous solutions as well as the formation and destruction of organic iodides. The objective was to include the most important processes while simultaneously keeping the model simple. Due to the generic nature of the organic iodide reactions in the model, the rate constants of these reactions are in terms of their approximate magnitudes. The rate constants for the generic reactions were estimated from values obtained for specific organics with similar molecular structures to that of the ‘‘generic’’ organic (Table 1).

The mechanistic details for the radiolytic decomposition of the carbonyl radicals to give alkyl radicals are not known. For simplicity, a pseudo-mechanistic approach to modeling is taken in which the detailed steps required for the formation of alkyl radicals are neglected and, instead, simple two-step reactions are used. The first step describes the scavenging of dOH and Hd, while the second step, with a smaller rate constant, describes the production of alkyl radicals. The following represent carbonyl reactions in the model:  d RCHO þ e aq -R CHOH þ OH ;

k ¼ 5
109 dm3 mol1 s1 ;

ð21Þ

RCHO þdOH-RCOd þ H2 O; k ¼ 5
108 dm3 mol1 s1 ;

ð22Þ

RCHO þ Hd -RCOd þ H2 ; k ¼ 5
107 dm3 mol1 s1 ;

ð23Þ

Rd CHOH þ Rd CHOH-RCOH þ Product; k ¼ 5
108 dm3 mol1 s1 ; RCOd -Rd þCO;

k ¼ 1
103 s1 :

ð24Þ ð25Þ

2.6. Aromatics Aromatic (AR) compounds react with dOH to form the hydroxy-cyclohexadienyl radical (dAROH). This

F. Taghipour, G.J. Evans / Radiation Physics and Chemistry 64 (2002) 203–213

207

Table 1 Rate constant of organic reactionsa Rate constant, k (dm3 mol1 s1)

Reference

Alkyl halides d  17. CH2Cl2+e aq-C H2Cl+Cl 18. CH2Cl2+Hd-CdH2Cl+Cl+H+ 19. CH2Cl2+dOH-CdHCl2+H2O  d 20. C2H4Cl2+dO 2 -C2 H3Cl2+HO2

6
109 4
106 9
107 Value estimated

Balkas (1972) Neta et al. (1971) Haag and Yao (1992) Getoff (1990)

Carbonyls d  21. CH3COC2H5+e aq-CH3C OHC2H5+OH d d 22. CH3COC2H5+ OH-CH3CO C2H4+H2O 23. CH3COC2H5+Hd-CH3COdC2H4+H2 24. 2CH3CdOHCH3-CH3CHOHCH3+CH3COCH3 25. Decomposition of CH3CdOHC2H5-Products

4.9
109 7
108 3
107 7
108 3
103

Mezyk (1994) Mezyk (1994) Mezyk and Bartels (1994a) Spinks and Woods (1990) Finlayson-Pitts (1986)

Aromatics 26. C6H6+dOH-Hydroxylcyclohexadienyl d 27. C6H6+e aq-[C6H6] d 28. C6H6+H -Cyclohexadienyl 29. From: C6H3(OH)3+dO 2 -Dihydroxyphenoxyl 30. Hydroxycyclohexadienyl+O2-Products 31. Cyclohexadienyl+O2-Products 32. C6H5OH+dOH-Dihydroxylcyclohexadienyl 33. C6H5OH+dO 2 -Product

7.9
109 9
106 1.1
109 3.4
105 3.1
108 1.6
109 6.6
109 5.8
102

Ashton et at. (1995) Koehler et al. (1995) Roduner and Bartels (1992) Deeble et al. (1988) Pan et al. (1990) Maillard et al. (1983) Field et al. (1982) Tsujimoto et al. (1993)

Organic 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47.

2.75
109 1
109 1
109 1.4
1010 1.8
1010 1.2
1010 2
10–9 9
108 9
108 4
109 Value estimated 4.4
108 1.2
1010 1.5
109

Mezyk and Madden (1996) Evans et al. (1990) Evans et al. (1990) Mohan and Asmus (1988) Mezyk (1997) Mezyk and Bartels (1994b) Evans et al. (1990) Getoff (1989) Getoff (1989) Marchaj et al. (1991) Wren et al. (1986) Getoff and Solar (1986) Getoff and Solar (1986) Getoff and Solar (1986)

No.

a

Reaction

iodides CdH3+I2-CH3I+Id CdH3+Id-CH3I CdH3+HOI-CH3I+OH CH3I+dOH-Product d  CH3I+e aq-C H3+I CH3I+Hd-CdH3+I+H+ CH3I+H2O-CH3OH+I+H+ CdH3+CH3-CH3CH3 From: CdH3+CdH3-CH3CH3 CdH3+O2-CH3OO C6H5OH+HOI-C6H4OHI  From: ClC6H4OH+e aq-Cl +HOC6H4 d From: ClC6H4OH+ OH-Addition From: ClC6H4OH+Hd-Addition

The numbers of the reactions in this table correspond to those of similar generic reactions in the text.

radical reacts with O2 usually leading to the formation of hydroxylated species. The reaction of aromatics with d  O2 is included in the model to compensate for the scavenging of all the dOH by aromatics. In the absence of this reaction, the scavenging of more than 99% of d OH by aromatics (depending on the I– concentration) leads to an unrealistic decrease in iodine volatilization by a few orders of magnitude. The reaction of dO 2 with phenolic (PHOH) compounds at relatively high rates has been reported (von Sonntag and Schuchmann, 1991). The rate constant used in the model for dO 2 reaction with aromatics is much higher than that reported for its reaction with phenolic compounds and hence this value,

taken in isolation, is likely not correct. The effect of this somewhat arbitrary selection of the rate constant was examined in terms of a sensitivity analysis and is discussed in a later section. The following represent aromatic reactions in the model: AR þdOH-dAROH;

k ¼ 5
109 dm3 mol1 s1 ; ð26Þ

 d AR þ e aq - ARH þ OH ;

k ¼ 1
107 dm3 mol1 s1 ; AR þ Hd -dARH;

k ¼ 1
109 dm3 mol1 s1 ;

ð27Þ ð28Þ

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using a reverse reaction for reaction (43):

AR þdO 2 -Product; k ¼ 1
107 dm3 mol1 s1 ;

ð29Þ

k ¼ 5
108 dm3 mol1 s1 ;

ð30Þ

k ¼ 1
109 dm3 mol1 s1 ;

ð31Þ

PHOHþdOH-Product; k ¼ 1
1010 dm3 mol1 s1 ;

ð32Þ

PHOHþd O 2 -Product; k ¼ 1
103 dm3 mol1 s1 :

ð33Þ

2.7. Organic iodides The organic iodide reaction set simulates the formation and destruction of organic iodides. The following reactions were incorporated in the model to represent these processes. Alkyl radicals and alkyl halide radicals that are produced from the radiolysis of aqueous carbonyls and alkyl halides react with iodine species in the following manner:

R þ I -RI; d

k ¼ 3
109 dm3 mol1 s1 ; 9

3

1 1

k ¼ 1
10 dm mol

Rd þ HOI-RIþd OH;

Rd þO2 2Rd O2 ;

k ¼ 1
109 dm3 mol1 s1 ;

s ;

ð34Þ ð35Þ

k ¼ 1
109 dm3 mol1 s1 : ð36Þ

The organic iodides formed from the above reactions are decomposed via reaction with water radiolysis products and by hydrolysis:

ð41Þ ð42Þ

kf ¼ 3
109 dm3 mol1 s1 ;

kr ¼ 1 s1 :

ARH þ O2 -AR þ HOd2 ;

d

d

k ¼ 1
109 dm3 mol1 s1 ;

Rd þHd -RH;

AROH þ O2 -PHOH þ HOd2 ;

d

Rd þ I2 -RI þ I d ;

Rd þRd -R2 ;

ð43Þ

The majority of the peroxy radicals react to form aldehydes, alcohols, and organic acids, which also undergo decomposition until they are eventually converted to carbon dioxide. During this process further alkyl radicals may be formed. However, the concentration of these secondary radicals, containing fewer carbons than the alkyl functional groups in the original carbonyl, will be quite low. Hence only alkyl iodides corresponding to the original alkyl functional groups are expected to be present in significant amounts (Taghipour and Evans, 2000). The hydroxylated compounds, formed through the radiolysis of aqueous aromatic solutions, react with iodine and form non-volatile iodophenolic compounds. The iodophenolic compounds then dissociate by reacting with water radiolysis products: PHOH þ HOI-IPHOH; k ¼ 1
102 dm3 mol1 s1 ;

ð44Þ

 IPHOH þ e aq -I þProduct;

k ¼ 1
109 dm3 mol1 s1 ;

ð45Þ

IPHOHþd OH-Product; k ¼ 1
1010 dm3 mol1 s1 ;

ð46Þ

IPHOH þ Hd -Product; k ¼ 1
109 dm3 mol1 s1 :

ð47Þ

RI þd OH-Rd þHOI; k ¼ 1
1010 dm3 mol1 s1 ; d  RI þ e aq -R þI ;

k ¼ 2
1010 dm3 mol1 s1 ;

ð37Þ ð38Þ

RI þ Hd -Rd þI þHþ ; k ¼ 5
106 dm3 mol1 s1 ;

ð39Þ

RI þ H2 O-ROH þ I þHþ ; k ¼ 2
109 dm3 mol1 s1 :

ð40Þ

There are other organic radical reactions that are important, such as organic radical dimerisation and reactions with hydrogen atoms. In the presence of oxygen, organic radicals will react rapidly with O2 to produce peroxy and then alkoxy radicals, a fraction of which may decompose to give back an alkyl radical. This process was represented in a greatly simplified form

3. Model evaluation The reaction set in the model was evaluated by comparing iodine volatilization rates observed through modeling and experiments under various conditions. The detailed description of the experiments have been provided elsewhere (Taghipour and Evans, 2001a). The experimental conditions and results can be summarized as follows: The rate of production of volatile iodine was evaluated in the presence of 10–3 M concentrations of carbonyl, alkyl halide, and aromatic compounds. A 0.85 L stainless steel vessel, installed in the irradiation chamber of a Gammacell, was used to measure the rate of iodine volatilization from well stirred, 131I labeled, 10–6–10–4 M CsI solutions with volumes of 250 ml and pH values from 5 to 9. The rate of volatile iodine production was evaluated by continuously measuring

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the release of 131I from the vessel, in an air stream flowing over the well-mixed irradiated CsI solutions. Iodine volatilization rate was calculated from the slope of a graph of cumulative iodine released vs. time. The liquid side mass transfer coefficient of the system (kl ) was determined by examining the mass transfer of oxygen from the gas to the liquid phase, using a dissolved oxygen concentration probe, and was corrected for volatile iodine species. The gas side mass transfer coefficient (kg ) was determined by measuring the change in the relative humidity of the air passing through the apparatus, using a relative humidity and temperature probe, and was corrected for iodine volatile species. The liquid side and gas side mass transfer coefficients were found to be 1.5
10–5 and 1
10–2 m s1, respectively. The results indicated that organic compounds could be classified into groups, based on their distinct effects on iodine volatility. In the presence of carbonyls and alkyl chlorides iodine volatilization increased significantly, up to two orders of magnitude. In the presence of aromatics the volatilization rate decreased at higher iodine concentrations and lower pH values, while it increased at lower iodine concentrations and higher pH values. Molecular iodine and organic iodide in the gas phase were separated using species selective adsorbents. In the presence of alkyl halides a significant increase in organic iodide formation and in molecular iodine formation occurred and airborne iodine was predominantly in an organic form. In the presence of carbonyls a considerable increase in organic iodide formation, but a decrease in molecular iodine formation occurred and the majority of the airborne iodine was in organic forms. No significant formation of organic iodides was observed in the systems containing aromatics. 3.1. Iodine concentration and solution pH A strong dependence of the volatilization rate on iodine concentration was predicted by the model under acidic and neutral conditions (Table 2). This trend was

in agreement with the experiments (Fig. 1). However, unlike the experiments, the model did not show this dependence under basic conditions (Table 2). The model predicted the dependence of the volatilization rate on the solution pH with reasonable accuracy for an I concentration of 105 M (Fig. 2). However, the model overestimated the volatilization rates for acidic 104 M iodine solutions. The rate of iodine volatilization predicted by the model at pH 5 changed dramatically over the iodine concentration range of 1
105 to 0.5
105 M. The model overestimated the volatilization rate at 1
105 M iodine concentration but once the concentration decreased to 0.5
105 M, the volatilization rate approached a constant value (the value used in Fig. 2) and model generated results were in agreement with those of the experiments. The dramatic changes in iodine volatilization rates predicted by the model for pH 5 solutions, with I concentrations ranging from 1
105 to 0.5
105 M is probably due to an incomplete representation of one of the processes in the model which is particularly important under these conditions. For example phosphate which was used in the test solutions as a pH buffer may play an important role in determining the volatilization rate under acidic conditions. In particular, phosphate likely catalyses the reduction of I2 by H2O2 (Eq. (15)) under acidic conditions. A sensitivity analysis was performed in which the rate constant of reaction (15) was increased by two orders of magnitude. This increase eliminated the dramatic change in iodine volatilization rate over the concentration range of 1
105 to 0.5
105 M. This indicated that the representation of the I2 reduction by H2O2 used in the model was likely incomplete, causing the model to overestimate volatilization rate for pH 5 solution with concentrations above 0.5
105 M. This possibility was further explored by adding the mechanism proposed by Ball et al. (1996a) to represent the catalysis of this process by phosphate. Addition of these reactions into the model also reduced the volatilization rate for pH 5

Table 2 Modeling results of iodine volatilization rates (mol min1) in the absence and presence of organics at various I concentrations and pH values CsI conc. (M)

pH

No organic

RCl 1E3 M

RCHO 1E3 M

Ar 1E3 M

1E4 1E4 1E4 1E5 1E5 1E5 1E6 1E6 1E6

5 7 9 5 7 9 5 7 9

1E08 3E10 1.5E11 1.5E10 5E11 4E12 1E11 7E12 1.5E11

2E08 1.5E08 1E09 1E08 5E09 5E10 5E10 4.5E10 4E10

4E09 2E09 1E09 3E10 7E11 6E12 2E10 4E11 1E11

1E09 4E10 1E11 9E11 3E11 1E11 1E12 1E11 1E11

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F. Taghipour, G.J. Evans / Radiation Physics and Chemistry 64 (2002) 203–213

1.00E-07 Volatilization Rate (mol/min)

Volatilization Rate (mol/min)

1.00E-09

1.00E-10

1.00E-11

1.00E-12

1.00E-09

1.00E-10

1.00E-11 CsI 1E-4 M Modeling

CsI 1E-5 M

CsI 1E-6 M Experimental

Fig. 1. Comparison of the modeling (left) and experimental (right) results of iodine volatilization rate at different I concentrations (pH 7).

1.00E-09 Volatilization Rate (mol/min)

1.00E-08

Halides Modeling

Carbonyls

Aromatics Experimental

Fig. 3. Comparison of the modeling (left) and experimental (right) results of iodine volatilization rate in the presence of organics (iodine concentration 10–5 M, pH 5).

1.00E-10

organic iodide formation were in reasonable agreement with the experimental results for all the types of organics examined (Fig. 3).

1.00E-11

3.3. Alkyl halides

1.00E-12 pH 5 Modeling

pH 7

pH 9 Experimental

Fig. 2. Comparison of the modeling (left) and experimental (right) results of iodine volatilization rate at different pH values (iodine concentration 10–5 M).

solution with concentrations above 0.5
105 M, thereby eliminating the dramatic change over the concentration range of 1
105 to 0.5
105 M. However, these reactions also caused the volatilization rate at pH 7 to decrease by over an order of magnitude. This finding was inconsistent with the observation in this work that adding phosphate to a pH 7 solution only decreased the volatilization rate by 40%. As a result, further work is required on understanding the role of phosphate and hence the phosphate catalysis reactions were not incorporated into the model. The majority of the experiments in the presence of organics were performed at pH 5, with an I concentration of 105 M. To avoid the model’s initial variability under these conditions, the model values used for comparison were taken when the volatilization rate achieved a steady value, once the I– concentration had decreased to below 0.5
10–5 M. This allowed a better examination of the model’s ability to simulate the impact of organic compounds, the focus of the study. 3.2. Presence of organics In general, the predicted iodine volatilization rate and changes in molecular iodine concentration, as well as

The significant increases in the iodine volatilization rate and airborne molecular iodine concentration at all pH values and iodine concentrations observed experimentally were well reflected by the model. The model also predicted the predominance of organic iodides in the gas phase, in agreement with the experiments. However, the model tended to over estimate the total airborne concentration for neutral and basic pH values, mostly due to extensive organic iodide formation under these conditions. Sensitivity analysis indicated that the volatilization rate does not strongly depend on the rate constant of the reaction of alkylhalides with dO–2 (Eq. (20)). For example, increasing the rate constant of this reaction from 1
107 to 1
109 dm3 mol–1 s–1 only caused an increase in the volatilization rate by a factor of two for the I– 10–5 MI, pH 5 solution. 3.4. Carbonyls The model predicted an increase in total iodine volatilization and a decrease in airborne molecular iodine concentration, as was observed experimentally. The majority of the airborne iodine was predicted to be in an organic form, in agreement with the experiments. Quantitative agreement between the model and experiments was also reasonable for 105 M iodine at all pH values. It should be mentioned that organic iodide concentration, and therefore the volatilization rates predicted by the model were not constant during the initial addition of carbonyls. However, the values reached a steady state as time progressed. Sensitivity analysis indicated that the reverse reaction for the organic radical reaction with O2 (Eq. (43)) is important

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1.00E-08 Volatilization Rate (mol/min)

in modeling a system containing carbonyls. In the absence of this back reaction, the majority of organic radicals are scavenged by oxygen, resulting in no appreciable organic iodide formation. Omitting the reverse reaction caused the model to under predict the volatilization rate for carbonyl systems, by more than an order of magnitude. 3.5. Aromatics

1.00E-09

1.00E-10

1.00E-11 Ar + RCOH

Ar + RCl

Modeling

Fig. 4. Comparison of the modeling (left) and experimental (right) results of iodine volatilization rate in the presence of the mixtures of organics (iodine concentration 10–5 M, pH 5).

1.00E-07 Volatilization Rate (modeling)

The model predicted that the iodine volatilization rate decreases under acidic conditions and increases under basic conditions. The same behavior was observed in the experiments. Furthermore, the model did not show any significant formation of iodo-organics in the system under acidic conditions, also in agreement with the experiments. Quantitative agreement between the model and experiments was reasonable for 105 M iodine at all pH values. Sensitivity analysis indicated that the reaction of aromatics with dO 2 (Eq. (29)) plays an important role in determining the volatilization rate predicted by the model. The rate constants for the reactions of aromatics and I with hydroxyl radicals are similar. Thus, in the absence of this reaction scavenging of the majority of dOH by aromatics leads to a decrease in iodine volatilization rate. For example, in the absence of this reaction, the volatilization rate predicted by the model decreases by three orders of magnitude for the I– 10–5 M, pH 5 solution. The need for this dO 2 reaction in the model indicated that understanding of the radiolytic iodine chemistry in the presence of aromatics is incomplete. Scavenging of dOH was expected to result in a large drop in iodine volatility, whereas only a small decrease was observed. This suggests that either some other pathway for I– oxidation exists or else that the rate of I2 reduction to I– is suppressed. It is possible that aromatic peroxide compounds contribute to iodide oxidation but the rate of such processes is unknown. For simplicity the rate of I2 reduction was decreased by postulating scavenging of dO 2 , thereby decreasing the rate of reactions (7) and (8), the predominant pathway for dI2 reduction to I2.

Ar + RCl + RCOH

Experimental

1.00E-08 1.00E-09 1.00E-10 1.00E-11 1.00E-12 1.00E-13 1.00E-13

1.00E-12

1.00E-11

1.00E-10

1.00E-09

1.00E-08

1.00E-07

Volatilization Rate (experiments) No Org.

RCl

RCHO

Ar

Fig. 5. Comparison of the modeling and experimental results of iodine volatilization rate (mol min1), for a wide range of iodine concentration (10–4, 10–5, 10–6 M) and pH (5, 7, 9), in the absence and presence of organics (10–3 M).

(5, 7, and 9), in the presence and absence of organics (10–3 M) is provided in Fig. 5. It is seen that the majority of the data points lie between the two diagonal lines, indicating the results are in agreement within an order of magnitude. These results are satisfactory, considering the relatively simple nature of the model.

3.6. Mixture of various organics

4. Application of the results to reactor accidents

Examination of the predicted iodine volatilization rates in the presence of a mixture of organics can be a valuable way for model evaluation. A comparison of the model predictions and experimentally obtained results were in reasonable agreement (Fig. 4).

The kinetic-based model, containing a mechanistic description of iodine chemistry and generic semimechanistic reactions for various types of organics, provides a reasonable description of the experimental results. However, it remains to be demonstrated that this model will satisfactorily simulate iodine behavior for conditions beyond those examined in this study. Given the mechanistic basis of the model, there is reason to believe that it will perform well over a wide range of conditions. However, further validation against experimental data obtained in other laboratories is needed

3.7. Summary of the results A comparison of the results obtained through modeling and experimentation, for a wide range of iodine concentrations (10–4, 10–5, and 10–6 M) and pH

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since empirical ‘‘fixes’’ were required to address a few areas where the existing understanding is incomplete. Once validated, the model can be incorporated into a more elaborated code, containing all the pertinent physical phenomena, in order to predict the radiological consequences following a reactor accident.

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