Let us first introduce a definition of transmutation rates of individual MA nuclides fueled in a reactor core. The calculation method of the transmutation rate relative to the nuclides is as follows. First, conventional burn-up calculations are carried out and burn-up-dependent flux in each region is calculated, which is used in the second step calculation. In the second step, we consider only the relevant MA in each region and perform burn-up calculations using the flux obtained in the first step.

In this second step of calculation, nuclide k is produced from the original nuclide

l. There are many passes of reactions of transmutation of the initial nuclide (shown by N) and the production of N as is shown in Fig. 17.6. We can calculate the production rate of nuclide k at time T from the initial nuclide l as

Pik = N (T)/Ni (0) (17.1)

where Ni(0) is number density of nuclide l at time 0 and Nk (T) is number density of nuclide k at time T, assuming nuclide l is present alone at t = 0. Using Nk(T), the overall fission (see Fig. 17.6) relative to the initial nuclide l is calculated as

(17.2)

where of(t) is fission cross section of nuclide k at time t, ф(ґ) is neutron flux at time t, and £ is summation over all nuclides k resulting from initial nuclide l; this

includes all the fissions from the initial nuclide l. Furthermore, the production of other MA nuclides except the initial nuclide l can be calculated by

oma1 = NN l (0)

к e MA, k=l

The Pu and U production from nuclide і is given by

PUl = Ni (0) Рік (17.4)

к e U, Pu

The production of MA nuclide l from Pu and U is given by

PUMl = NN к (0)Pki (17.5)

к e U, Pu

The production of MA nuclide l from other MA is given by

MAMl = NN к (0)Pki (17.6)

к e MA, k=l

Using Eqs. (17.2), (17.3), (17.4), (17.5), and (17.6), the net transmutation of nuclide l is calculated by

TR1 = OF1 + OMA1 + PU1 — PUM1 — MAM1 (17.7)

In Fig. 17.6, the individual parameters OMA1, PU1, PUM1, MAM1, and OF1 are shown. PUM1 and MAM1 denote the productions of the relevant MA nuclide from fuel (Pu, U) and other MA nuclides, respectively. Thus, there are minus signs in these parameters in Eq. (17.7), whereas other parameters show the elimination of the relevant MA nuclide, so the signs are positive.

When we consider the total MA transmutation for all MA nuclides, the second and the fifth terms cancel each other, so the whole transmutation is given by

TR = TR1 = ^ (OF1 + PU1 — PUM1) (17.8)

1 e MA 1 e MA

Therefore, we can define the MA transmutation of MA nuclide 1 by

TR1 = OF1 + PU1 — PUM1 (17.9)

Thus, the transmutation rate is composed of two terms: the first is the amount of incineration rate by fission and the second is the net transmutation rate to fuel (U and Pu). The first fission rates of individual nuclides contain the direct fission of the relevant nuclide plus the fission of other nuclides transmuted by decays or neutron reactions as “overall fission” [OF1 in Eq. (17.9)] (Fig. 17.7). It was found that the indirect fission contribution by Pu and 9Pu is remarkably large for

nuclides 239Np and 241Am. The net production rates of U and Pu are calculated from the difference between the production rates of U and Pu from the relevant MA nuclide and the MA production from the initial U and Pu. Figure 17.7 shows the overall fission rate of 237Np in a thermal advanced pressurized water reactor (APWR) and two fast reactors, a MOX-fueled sodium-cooled fast reactor and a metal-fueled lead-cooled fast reactor [8]. In the thermal reactor, the overall fission rate is about 5 % in one cycle and is very small compared with the fast reactors. In fast reactors, the direct fission of Np is rather large, and the Pu fission contribution is also large. The 239Pu fission contribution is small for fast reactors.

We are developing a calculation code system based on the foregoing method and are planning to apply the system to MA transmutation core design. In the core design we consider a homogeneous MA loading core, and a heterogeneous MA loading core, in which MA is loaded in special assemblies with moderators.

21.4.1 Prevention of Criticality by Poison or Dry Process

The boration of coolant water was practiced in TMI-2 and is most preferable. Borated water bounds the criticality characteristics of all debris into a small region, indicated as “Boration” in Fig. 21.3, and keeps the region far from critical condition no matter how much temperature or geometry changes. By securing the lowest boron concentration in water, the subcritical condition can be guaranteed as well. The water issue, however, must be fixed to implement this option. Moreover, a structure made of carbon steel or aluminum will act as the water boundary when a

CV is filled with water. Then, corrosion of such material by boron must be studied to prevent recurrence of the water issue.

The dry process without using coolant water will be also a certain criticality control method (Fig. 21.3). There will be, however, other engineering challenges. CVs must be sealed to avoid unexpected intrusion of water. It will be necessary as well to shield radiation and to suppress airborne migration of radioactive materials without water during fuel debris retrieval work.

In each of the courses, the students reached a level that enabled them to maintain a debate during the competition held during at the end of the course. Owing in large part to Fujikawa’s well-constructed topic, the debate proved to be a balanced one, with the result of the debate not overly biased either for or against the proposition.

Perhaps because of their humanities/social sciences background, the students had little background knowledge of radiation. Regarding the storage pool for spent nuclear fuel, for example, a number of students mistakenly thought that the spent fuel was dissolved in the water of the pool (the spent fuel is stored in the form of solid rods). However, because preparing for a debate necessitates clarifying which areas of one’s understanding are lacking, students’ grasp of the subject area gradually improved as the course progressed. During the period of the course various news stories appeared in the press related to nuclear power and NUMO. Students not only responded to this news in class, but also actively gathered information reported in the media, and were able to use such up-to-date information in the debate competition.

Despite the fact that nuclear power was an issue directly related to the provision of energy for their lives, some considered that nuclear energy concerned no more than Fukushima; in other words, their awareness of nuclear power as an issue pertinent to them was low. However, through doing the course, students’ under­standing deepened and they also began to appreciate that the issue was one that directly affected them.

The input from experts was also important for helping students to understand the issues. At the beginning of the course, there were students who, not understanding fundamental facts—for example, the difference between radial rays and radioac­tivity—practically “gave up thinking” about the issues. However, with the tuition of
the guest speakers and their demonstrations of a cloud chamber (a simple device that allows the decay of radioactive materials to be observed) and other experi­ments, students gradually learned more about the science involved, leading them to become more proactive in thinking about issues for themselves.

The NMB code [7] was employed for the scenario analysis. The code calculates material balance of 26 actinides (through Th to Cm, T1/2 > several days) in spent fuels with an accuracy comparable to the ORIGEN2 code. LWR, CANDU, gas-cooled reactor, several sodium-cooled FRs, and lead-bismuth-cooled ADS are available. Each reactor can be coupled with appropriate fuel such as UO2, MOX, ROX, Pu-nitride (PuN), and MA-nitride (MAN). Fission products are estimated by dividing them into several groups (iodine, rare gas, technetium and platinum group metals, strontium, cesium, and others). The number of waste packages and repos­itory size are determined by temperature analysis based on several repository layouts. Potential radiotoxicity that is defined as dose by direct ingestion can be also estimated.

Figure 23.1 shows a prediction of the world population together with its past history. This figure shows that the world population had already reached 7 billion in 2011 and will be 9.2 billion in 2050. Another prediction indicates that the population of the world would be 11 billion by the end of this century.

It is not easy to predict the future demand for primary energy. Let me estimate it taking an extremely naive way. Figure 23.2 shows how much primary energy per capital is consumed annually in each country in terms of tons of oil equivalent. In 2009, the average of consumption of primary energy was 1.8 t/year and the world population was 7 billion. It is a reasonable assumption that everybody in the world hopes to enjoy the American life using 7 t/year, or at least the average of OECD countries by using 4 t/year. Let us assume that in the near future the average will become 4 t/year and the world population will be 10 billion in 2100. Then, simple arithmetic tells us that the total demand for primary energy will be 3.2 times [=(10 x 4)/(7 x 1.8)] more than the present consumption.

More realistically, the International Energy Agency (IEA) predicted the future demand for primary energy. The demand in countries other than OECD in 2035 will be 1.8 times more than in 2010. The demand for primary energy in the world in 2035 will be 1.35 times more than in 2010. We should be careful because this increase of 35 % will occur only 25 years from now. If this increase continues linearly for the next 100 years, we find a 140 % increase, that is, altogether 2.4 times more than the present consumption. According to IEA, the demand for electricity in the world in 2035 will be 1.73 times more than in 2011, which is an increase 2 times as fast as that for primary energy.

Figure 27.1 shows the experimental procedure for the solution samples. Sodium iodide-129 (129I: 0.1 Bq = 15 ng) and K127IO3~ (127I: 1 Fg) were added to 50 ml 3 M NaOH solution or 50 ml diluted HCl solution (pH = 2) with and without reductant (0.1 ml 0.1 M NaHSO3) to study iodine species behavior in the analysis using Anion-SR. After addition of the iodine species, the solutions were allowed to stand for 1 day before separation with Anion-SR. The reductant was added approx­imately 20 min before the separation. The operation of Anion-SR was based on Shimada et al. [3]. Briefly, the Anion SR disk was centered on the base of the filtration funnel and the reservoir was clamped on the top of the disk. The appro­priate solution was poured into the reservoir followed by suction filtration The Anion-SR disk was conditioned with acetone, methanol, ultrapure water, 4 w/v% NaOH, and ultrapure water. After conditioning, the sample solution was introduced into the Anion-SR disk and washed with ultrapure water. The extracted I was recovered with 9.5 ml 1 M HNO3. To oxidize I_ to IO3~, 0.1 ml NaClO solution (effective Cl concentration, >5 %) was added to the recovered solution. Addition­ally, 0.1 ml 2 ppm Rh standard solution was added to the recovered solution as an internal standard. Finally, 1 M HNO3 was added to the recovered solution to a final volume of 10 ml. The concentration of I was measured by inductively coupled plasma mass spectrometry with dynamic reaction cell (DRC-ICP-MS). In the reaction cell, oxygen gas was collided with ions. Because the order of ionization potential is I > O > Xe, O reacts with Xe to neutralize but I does not react with O. As a result, the count of 129Xe, impurity of Ar gas, was decreased. The experimental conditions of DRC-ICP-MS were consistent with the conditions reported by Kameo et al. [7]. Percent recovery was calculated as in Eq. (27.1).

Fig. 27.1 Schematic diagram of analysis of 127IO;T and 129Г in the solution samples

Amountof I in the recovered solution Percentrecovery = x 100

Added amount of I

The separation experiment to determine percent recovery was carried out twice, and the uncertainty was quantified by the dispersion in these two measurements.

This section evaluates the effects of measures taken to reduce burnup reactivity swing of the uranium-free TRU metallic fuel core. In the parameter surveys, the operation cycle length, that is, 150 days, the core volume, and the core power density were kept constant to compare the effect of each countermeasure. The average fuel burnup was also kept constant, save for the survey of the number

of refueling batches. The adjusting parameter to increase the fissile amount was the zirconium content in TRU-Zr alloy fuel to keep k-effective = 1.0 at the end of the cycle.

Table 15.2 shows the summary of the analysis results. The reduction of the core height from 93 cm to 65 cm resulted in a 12 % decrease of burn-up reactivity swing. The introduction of a B4C shield, where natural boron was assumed, at the core periphery region resulted in only about a 5 % decrease in burn-up reactivity swing. On the other hand, the penalty of this countermeasure is the increase of core power peaking because the leakage of neutrons from the core surface increases. Hence, this measure was not adopted in the subsequent core design. Regarding the effect of the number of refueling batches, the larger is the number of refueling batches, the smaller the burn-up reactivity swing becomes. The effect was approximately a 5 % decrease in burnup reactivity swing for a 40 % increase in the number of refueling batches. This measure was not adopted in the subsequent core design because its effect on the burn-up reactivity swing is small and it leads to significant increase of core power peaking because of the increased difference of burn-up between most burnt fuel and fresh fuel.

Kento Yamamoto, Keisuke Okumura, Kensuke Kojima, and Tsutomu Okamoto

Abstract Sensitivity analyses of initial compositions and cross sections were conducted to quantitatively clarify the source elements and the nuclear reactions dominating the generation of activation products. In these analyses, the ORI — GEN2.2 code was used with ORLIBJ40, a set of the cross-section libraries based on JENDL-4.0. Analyses were conducted for the activations of cladding tubes, end plugs, and spacers of fuel assemblies and channel boxes in BWR that are composed of zirconium alloy, stainless steel, and nickel-chromium-based alloy. From about 50 representative radioactive nuclides, several nuclides were selected as the targets of sensitivity analyses for the aspect of their large concentrations in the target materials.

The results of sensitivity coefficients clarified the source elements and the nuclear reactions dominating the generation of activation products even for the nuclides generated through complicated pathways. These results could be utilized to select the objectives of the impurity elements for measurements and of nuclear data for the improvement of accuracy. These results will contribute to improve­ments in the accuracy of numerical evaluations of activation product concentrations.

Keywords Activation products • Burn-up calculation • INCONEL alloy • ORI — GEN2.2 • ORLIBJ40 • Sensitivity study • Stainless steel • Zircaloy

20.1 Introduction

In the research on the back-end of nuclear cycles, improvement of the accuracy of predicting concentrations of activation products is important for various evalua­tions. Providing the accurate initial compositions and nuclear data leading to the generation of activation products is necessary for accurate predictions of
concentrations of the activation products. An effective first step to achieving this is to identify the dominant generation pathways of activation products. Sensitivity analyses of initial compositions and cross sections for activation products, which involve understanding the effects of initial compositions and cross sections on the concentrations of the target activation products, are powerful methods for quanti­tatively investigating the generation pathways. Thus, in the present study, sensitiv­ity analyses focusing on the generation pathways for activation products were conducted.

The ORIGEN2.2 [1] code was used in the analyses; this code has been widely used for evaluating the concentrations of activation products. The one-group cross sections made with the appropriate neutron spectrum are required for the accuracy of the ORIGEN2.2 calculation. With respect to the activations of in-core structure materials, the existing ORIGEN2.2 cross-section libraries made with the in-core neutron spectrum are available. Thus, the target of the analyses in the present study is the activation of such in-core structure materials.

This chapter presents the method, calculation conditions, and results of the sensitivity analyses of initial compositions and cross sections for activation prod­ucts in the materials of in-core structures, such as zirconium alloy, stainless steel, and nickel-chromium-based alloy. The results of the sensitivity analyses identify the elements and the nuclear reactions leading to the generation of activation products. These results will be effective in improving the accuracy of numerical evaluations of the concentrations of activation products.

According to the current basic policy for HLW disposal in Japan, the siting process is to be carried out with three stages (Fig. 24.5). The first stage is “literature survey,” the second is “preliminary investigation,” and the third is “detailed investigation.” Then, construction of the repository will start. At each stage,

decisions will be made by selection criteria, taking into account the opinions of the local mayor (municipality) and local governor (prefecture).

In reality, there has been no occurrence of the first literature survey, although more than 10 years have passed since the siting process started. As mentioned before, a scheme of open solicitation was adopted for volunteers to apply for the literature survey, but after the failed attempt of Toyo Town in 2007, another scheme was added in which the government invited municipalities for the literature survey. However, the situation did not improve; rather, after the Fukushima accident the situation is becoming worse.

Facing these difficult situations, the government of Japan decided to take a more positive role in site selection. It is expected that a promising area could be more narrowly defined by screening sites on the basis of existing geological and geo­graphical information.

Metal concentrations in waste samples extracted with oxalic acid (samples 3-6, Table 29.1) were in general high. The scrubber wastewater from an industrial waste incinerator (sample 7, Table 29.1) showed very high Zn concentration. Not well metals in samples 8-12 (Table 29.1) could be measured because of the constraint on elemental analyses of rad-Cs-contaminated samples. Nevertheless,

the data suggest that metal concentrations in rad-Cs-contaminated sewage sludge (samples 10-12, Table 29.1) were similar to those in the extracts of uncontaminated sewage sludge (samples 1, 2, 4, 5, and 6, Table 29.1). Samples 8 and 9 (Table 29.1) showed low metal concentrations as they were stabilized waste materials that were treated to reduce heavy metal leaching.

The removals of Cs in different samples and test conditions are summarized in Tables 29.2, 29.3, and 29.4. Table 29.2 shows Cs removal efficiencies (%) for samples 1-7 under different pH and K-Fer concentrations without an addition of metals. The tests were conducted for pH 3-10 and soluble Fer salt concentrations 0.1-1.0 mM. The results indicated that insoluble Fer complexes were formed with metals present in the waste extracts (Table 29.1) upon addition of soluble K-Fer salts, resulting in high Cs removal efficiencies. Fer complexes could be formed with any of the metals such as Fe, Mn, Cu, and Zn present in sufficient concentrations (Table 29.1) to precipitate 0.1 mM Fer ions. Cs removal in sample 7 was lower (e. g., 74 % at pH 5) than those in other samples, although transition metals (particularly Zn) in the sample were abundant for the formation of insoluble Fer complexes. In a control experiment discussed in our previous work [4], we inves­tigated on the effect of Zn concentration on Cs removal, and found that Cs removal by Fer solids tends to be low when Zn is present at pH 5. Formation of the Zn-Fer complex, which is known to have a comparatively low Cs distribution factor, probably reduced Cs removal in the sample. The Cs removal in sample 7 at pH 3 increased to 92 %, possibly because of the formation of iron-Fer complex, which has a high Cs distribution factor [4]. Moreover, Cs removal increased with increas­ing K-Fer concentration. However, this leads to increased amount of precipitate in the solution, which is not preferable from the aspect of waste volume reduction.

Table 29.3 shows Cs removal from oxalic acid extracts. In a separate experi­ment, we examined the effect of oxalic acid concentration on Fer coprecipitation method and concluded that for 0.1 mM Fer concentration, oxalic acid concentration should be 0.01 M or less for precipitation of insoluble Fer compounds. In actual waste extracts, oxalic acid is consumed by calcium present in the wastes, and hence actual oxalic acid concentrations are lower than those in the original reagents. The data in Table 29.3 show that Cs removal is possible with 20 times (nominal concentration = 0.05 M) as well as 10 times (nominal concentration = 0.1 M) dilutions of 1.0 M oxalic acid extract.

Table 29.4 shows results of coprecipitation tests conducted with fly ash extracts contaminated with rad-Cs as the result of the F1 accident. On-site analysis of samples 8 and 9 using a portable voltammetry instrument revealed that metal concentrations in the samples were relatively low. Addition of K-Fer alone in sample 8 resulted in 23 % rad-Cs removal. In fact, metal salts had to be added before the addition of soluble Fer salts for the formation of insoluble of Fer complexes in the samples. We, therefore, compared removals of rad-Cs with different metals (e. g., Ni(II), Fe(II), Fe(III) or Zn(II)). In sample 9, light green — colored Ni-Fer precipitate was formed when Ni and soluble Fer salt were added, but rad-Cs removals were only 58-62 %. The removal increased to almost 100 % only when 1.8 mM Fe(II) (in excess to 0.1 mM Fer) was used. Apparently, the removal of rad-Cs changed significantly for sample no. 9 depending on the type of iron salt (ferric or ferrous iron) used with K-Fer, but the reason remains unknown at this point. In contrast, the extract of molten fly ash sample 8 showed almost complete removal of rad-Cs with Fer-Fe(II), Fer-Fe(III), and Fer-Ni coprecipitation. The results may be explained by the existence of colloidal, nonionic rad-Cs (e. g., sorbed on suspended particles, but passed through 0.45-pm-pore-size filter) in the inciner­ator fly ash extract sample 9, because Cs in such form does not precipitate with Fer-Ni, but it precipitates with Fe through coagulation-precipitation mechanisms.

For rad-Cs-contaminated sewage wastes (samples 10-12, Table 29.4), on-site analysis for metal contents showed rather high Zn concentrations for samples 11 and 12 whereas Fe was prevalent in sample 13. We, therefore, conducted co-precipitation tests at pH 3 to produce Fe-Fer rather than Zn-Fer, that has a low Cs distribution factor, or added Ni-Fer in place of K-Fer to prevent formation of Zn-Fer in the samples.

Overall, very high rad-Cs removals (>95 %) were observed for contaminated waste extracts (samples 8-11), although we did not have enough time to optimize coprecipitation conditions for sample 12.

29.2 Conclusion

Selective removal of Cs using Fer precipitation was conducted with extracts of sludge and fly ashes generated from municipal water treatment plants and waste incineration plants in the areas affected by the F1 accident. More than 95 % rad-Cs removals were achieved for an optimized combination of pH, Fer concentration, and type of added metal salts. The chemical form (ionic or particulate) of Cs in waste extracts, heavy metal leaching from the wastes (i. e., whether the waste had undergone stabilization treatment), and Zn concentration influenced Cs removal. The results undoubtedly suggest that knowledge of principal metal content is very important for successful application of the Fer coprecipitation technique to remove rad-Cs from contaminated wastes.

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

C. H. Pyeon (*)

Nuclear Engineering Science Division, Research Reactor Institute, Kyoto University,

Asashiro-nishi, Kumatori-cho, Sennan-gun, Osaka 590-0494, Japan e-mail: pyeon@rri. kyoto-u. ac. jp

© The Author(s) 2015

K. Nakajima (ed.), Nuclear Back-end and Transmutation Technology for Waste Disposal, DOI 10.1007/978-4-431-55111-9_9

[6]

where Vm is the covariance of the calculation method used for the MA transmu-

12

tation rate in the target reactor core, Vm is the correlation between the calculation method errors for the critical assemblies and the target core, and N is defined by

N = G(2) WG(1)t{G(1)WG(1)t + Ve1} + Vm1^} 1 (17.47)

We will estimate the MA transmutation amount and the uncertainties by using these methods.