Nuclear Energy in Europe: Uranium Flow Modeling and Fuel Cycle

Jan 28, 2011 - The European nuclear fuel cycle (covering the EU-27, Switzerland and Ukraine) was modeled using material flow analysis (MFA).The analys...
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Nuclear Energy in Europe: Uranium Flow Modeling and Fuel Cycle Scenario Trade-Offs from a Sustainability Perspective Danielle M. Tendall*,† and Claudia R. Binder‡ †

EPF Lausanne, Environmental Science and Engineering, B^atiment GR, Station 2, 1015 Lausanne, Switzerland/ETH Zurich, Institute of Environmental Engineering, Ecological Systems Design, HIF C43, Schafmattstr. 6, 8093 Z€urich, Switzerland ‡ University of Z€urich, Department of Geography, Social and Industrial Ecology Division, Winterthurstrasse 190, 8057 Z€urich, Switzerland/Graz University, Institute for Systems Science, Innovation and Sustainability Research, Merangasse 18/I, 8010 Graz, Austria

bS Supporting Information ABSTRACT: The European nuclear fuel cycle (covering the EU-27, Switzerland and Ukraine) was modeled using material flow analysis (MFA).The analysis was based on publicly available data from nuclear energy agencies and industries, national trade offices, and nongovernmental organizations. Military uranium was not considered due to lack of accessible data. Nuclear fuel cycle scenarios varying spent fuel reprocessing, depleted uranium re-enrichment, enrichment assays, and use of fast neutron reactors, were established. They were then assessed according to environmental, economic and social criteria such as resource depletion, waste production, chemical and radiation emissions, costs, and proliferation risks. The most preferable scenario in the short term is a combination of reduced tails assay and enrichment grade, allowing a 17.9% reduction of uranium demand without significantly increasing environmental, economic, or social risks. In the long term, fast reactors could theoretically achieve a 99.4% decrease in uranium demand and nuclear waste production. However, this involves important costs and proliferation risks. Increasing material efficiency is not systematically correlated with the reduction of other risks. This suggests that an overall optimization of the nuclear fuel cycle is difficult to obtain. Therefore, criteria must be weighted according to stakeholder interests in order to determine the most sustainable solution. This paper models the flows of uranium and associated materials in Europe, and provides a decision support tool for identifying the trade-offs of the alternative nuclear fuel cycles considered.

’ INTRODUCTION Energy supply is currently causing much concern due to the decreasing availability of resources, increasing energy demand, and global warming. This has brought renewable and more CO2 neutral energies into the spotlight. For 50 years, nuclear energy has contributed to the energy supply worldwide.1-4 Although its use is still debatable in many aspects such as safety, security, and disposal of wastes,5-9 many countries in Europe are now increasingly considering nuclear energy as the alternative to fossil fuels.2,3 Many European nuclear reactors will shortly reach end-of-life:2,3 it must be decided whether to maintain, increase, or decrease nuclear capacity. Consequently, there is urgency for developing tools for assessing the sustainability of nuclear energy in Europe. The understanding of uranium flows is essential for such an assessment, as it builds comprehension of material requirements and sources, dependencies, waste generation and build-up, and recycling potential.10 It is important that such information be acquired not only at a national level, but also connected at a global level.11 Overviews of most countries’ nuclear facilities and demands exist 1-4 as well as general descriptions of the nuclear fuel cycle.1-3 Material balances of uranium throughout the world nuclear fuel cycle have been calculated.12 Many estimates of uranium resource durability exist: known and predicted uranium supplies may not last longer than a few hundred years.5,7,13,14 Rudolf 14 identified worldwide uranium flows, and quantified fresh uranium flows. In-depth dynamic modeling of the nuclear fuel cycle at a regional level is possible using the COSI code developed for Europe15 or the VISION code for the U.S.16 These r 2011 American Chemical Society

models are based on physical equations and provide detailed isotopic content of waste streams in particular. They are targeted at designing and assessing transition scenarios to future nuclear fuel cycles using fast reactors for example, and provide a level of detail necessary for time and infrastructure planning. However, these models do not provide an overview of additional material flows besides uranium and fissile isotopes, that is, nonfissile materials such as mining and milling wastes. The sustainability of nuclear energy cannot be assessed without considering aspects such as proliferation, irradiation, environmental risks, and costs. Life-cycle assessments of nuclear energy 17-20 show that it compares favorably with any fossil fuel; among renewable energies, only wind and hydro power achieve lower life-cycle impacts. However, these assessments are static in nature and the results may depend on the primary energy content of uranium considered, which is affected by significant uncertainty. An operational proliferation resistance assessment method exists.21 Multicriteria assessment methodologies have been developed in parallel to this paper, notably the INPRO method,22,23 which includes economic, safety, environmental, waste management, proliferation resistance, and infrastructure criteria. However concerning environmental criteria, it principally considers primary energy consumption, greenhouse gas emissions, and ionic radiation but does not include chemical and heavy metal emissions, for example. Received: September 27, 2010 Accepted: January 4, 2011 Revised: December 28, 2010 Published: January 28, 2011 2442

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Environmental Science & Technology The potential of spent fuel reprocessing to reduce uranium demand is indicated in literature.2,17 The effect of future nuclear technologies on waste management is still hypothetical, but detailed isotopic modeling has been done,24-26 and fast reactor or acceleratordriven system fuel cycle scenarios have been modeled for the U.S. 27 and Europe.28 However, a systematic holistic assessment of fuel cycle scenarios covering not only future technologies but also current fuel cycle options, enabling their direct comparison, was not found. This paper presents an assessment of nuclear energy in Europe, combining a material flow analysis with an integrative assessment using environmental, economic, and social criteria. It demonstrates the use of such an approach by assessing several fuel cycle scenarios such as spent fuel reprocessing and fast reactor technology. The following questions are addressed: • What are the main characteristics of the European nuclear fuel cycle? • How do different fuel scenarions compare from an environmental, economic, and social perspective? This study is innovative in that it uses currently available modeling information to provide an overview of the critical sustainability issues regarding nuclear energy. It is meant as a nonnormative decision support tool which allows for identifying the key trade-offs encountered in the different scenarios. A description of the European nuclear context is available in the Supporting Information (SI).

’ MATERIALS AND METHODS Conceptual Framework. A material flow analysis, allowing aggregation of quantitative data into a comprehensive flow model, is established for different nuclear fuel cycle scenarios. It is then associated with an integrative assessment, and used to assess scenarios according to sustainability criteria (SI Figure S2). MFA Model. Background. Material flow analysis (MFA) is a “systematic assessment of the flows and stocks of materials within a system defined in space and time”.29 MFA identifies and quantifies all inputs and outputs of a particular material to a system, as well as the stocks and flows within the system. System Analysis for the Uranium Flows in Europe. Spatial boundaries were the geographical boundaries of EU-27 countries, plus Switzerland and Ukraine. The time boundary was 2007. The model (based on refs 2,11,12,14,16) indicates major inputs, losses, flows and stock variations, and provides sufficient detail to analyze reprocessing, which requires identification of isotopic content of flows. Two flow layers were analyzed: • “Uranium”: considered elemental uranium. It was used as a base layer, from which the material layer was calculated using concentrations. • “Material”: considered the materials containing uranium, covering U3O8 (yellowcake form of uranium), UF6 (converted form of uranium), UO2 (fuel form of uranium), fission wastes, mined rock, etc. This ensured the detection of important waste flows. This layer was not balanced, since this would have involved many additional flows (typically of F), causing unnecessary complexity. (Flow model and description SI Figure S3). Mathematical Formulation of the Quasi-Stationary Model. The model is based on the mass conservation equation: X X inputs outputs ¼ Δstock ð1Þ

Uranium presents a particularity in MFA: mass is not conserved. This was addressed by allowing an exception to the MFA rule of

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modeling only one substance: the weight of heavy metals produced in the reactor, other than uranium, was also included in the flows. The transfer coefficients of each process were calculated from diverse data sources (Table S1 SI), partially based on physical equations. In particular, the equation used to model the enrichment process was:7 tf eed ðproduct_assay - tail_assayÞ ¼ ðf eed_assay - tail_assayÞ tproduct

ð2Þ

Where tfeed: tons of input uranium with a U235 content defined by the feed_assay (typically that of natural uranium), tproduct: tons of enriched uranium at output, with a U235 content set at product_ assay (typically 3.4%), and tail_assay: the content of U235 in the depleted uranium output stream. The model was set up using STAN software 30 which performs error minimization over the system using Gaussian error propagation, by adapting flow and stock values within their error margins. Data. Data (Table S1, SI) was mainly derived from refs 1,2,3,4,6,7,12, and from trade data supplied by diverse national trade and statistics agencies which provided values and estimates for flows, stocks, and parameters, from which the remaining flows and transfer coefficients were deduced (error estimation described in SI). The values of stocks were not entered in the model (uncertainty over 100%); only stock variation was included. The main assumptions for the European nuclear fuel cycle were (a) the ore grade was 1%, (b) the enrichment grade was 3.4%, (c) the enrichment tails assay was 0.25%.1,2,4,12 Additionally to the status quo (present state of the European uranium flow cycle), we defined 5 scenarios (Table 1) (based on refs 1,2,4,12,24-26,31). All imply modifications of the current nuclear fuel cycle and focus on reducing uranium requirements. R1 and R2 are feasible if reprocessing capacity is increased. Experience with reprocessing and reprocessed fuel is extensive; currently its use is limited due to competitiveness of fresh fuel, although the failures of the reprocessing plant 32 at the Sellafield site (UK) may have caused additional reticence (ex. Moratorium on reprocessing in Switzerland 33). R3 and R4 are technically feasible: currently, enrichers vary tail assays according to market fluctuations,4 and enrichment assays according to reactor requirements.1 R5, promoted by the Generation IV International Forum,31 remains hypothetical. Integrative Assessment. The status quo and scenarios were assessed according to eight environmental, economic, and social criteria (Table 2), representative of issues addressed in literature and by public welfare organizations,5-8,12,18,23,34 such as proliferation resistance, and disposal of nuclear wastes, considered major issues by the European population.9 The criteria cover the key problematic areas of the nuclear fuel cycle, the three dimensions of sustainability, are calculable using publicly available data, and are compatible with the MFA results. Proliferation resistance was quantitatively assessed based on the nuclear security (NS) measurement,21 using only the intrinsic proliferation criteria which is related to material flows (therefore excluding extrinsic criteria related to surveillance, infrastructure and regulation): DOE attractiveness level, heating rate of plutonium, weight fraction of even plutonium isotopes, concentration of fissile material, radiation dose rate, and separability of plutonium or uranium. 2443

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Table 1. Scenario Specifications and References N°

specifications

R1

all recyclable spent fuel is reprocessed to provide MOX fuel, which is then loaded in the reactors.2,24

R2

all depleted uranium (assay 0.25%) produced from fresh uranium enrichment is re-enriched to an assay of 3.4%, with a tails assay of 0.2%.2,12

R3

enrichment tails assay is reduced from 0.25% to 0.2%.1,2,4,12

R4

enriched uranium assay is reduced from 3.4% to 3.2%.2,12

R5

hypothetical replacement of all reactors by fast neutron reactors of the sodium-cooled type, without considering transition phases.2,24,25,31

Table 2. Assessment Criteria criteria environmental criteria

saved uranium

a,b

waste production

a,b,c

energy demandb chemical risksc,d radiation risksc,f

economic criteria

operation costsa,c investment costsa,c

social criteria

proliferation risksb,c,e,g

definition

unit/formula

source

percentage of reduction of uranium demand (W) compared to current situation. increase/decrease of waste quantity (Q)b, combined with increase/decrease of waste radioactivity and half-lifec, compared to current situation. total energy requirement of processes increased/decreased use of chemicals (e.g., strong acids) based on increase/decrease of waste radioactivity, and of production, transport and manipulation of radioactive wastes. for status quo: assessment according to the isotopic content of the flows, and the degree of security of the processes. increase/decrease of cost of nuclear energy production compared to current situation. investments required for implementing the scenario. based on isotopic content of flows, and state of separation of problematic isotopes from other components

(initial_U[t] - scenario_U[t])/initial_U[t] * 100 [%] (initial_W[t] - scenario_W[t])/initial_W[t] * 100 [%]

model calculation

[t] * [MJ/t] 1: large decrease, 5: no change, 9: large increase 1: large decrease, 5: no change, 9: large increase

19 6

1: large decrease, 5: no change, 9: large increase 1: large decrease, 5: no change, 9: large increase P P NS = i=1Imi[t] 3 PRi/ i=1Imi[t] 1: large decrease, 5: no change, 9: large increase

2,6,34,35

model calculation; 2,6,23,24,28,31

2,6,23,24

6,22,23,31,34 2,6,21,23,24

a

For scenarios only. b Quantitative. c Qualitative. d Defined as the risk of emissions of harmful chemicals to air, water and soil, thus posing threat to the environment. e Defined as the risk of spread of nuclear weapons (directly or through fissile materials and nuclear technology) to nations which are not internationally recognized as nuclear-authorized countries. f Defined as the risk of exposure of human population to irradiation, and subsequent health damages. g Considered as a social criterion, as it significantly affects the social acceptance of this technology.9

’ RESULTS Status Quo. Material Efficiency. Figure 1 illustrates the uranium flow model for 2007. Major flows are inputs of uranium from outside Europe (11 469 t/y), and depleted uranium produced from enrichment (total 15 673 t/y). The following characteristics are highlighted: • Europe is highly dependent on foreign countries, which supply 95% of its 24 000 t/y natural uranium equivalent requirement (imported as 13 658 t natural uranium and 1415 t enriched uranium). The remaining 5% is mostly produced in Ukraine. Europe’s own identified resources of 306 100 t natural uranium1 would be depleted within 12 years of supplying this demand (identified resources cover reasonably assured resources (RAR) and inferred resources, accessible at less than 130 USD/kgU1). Slightly less than half of the total natural uranium requirement is processed abroad, causing processing wastes and emissions to accumulate outside the system boundaries. • Reprocessing of spent fuel is a minor flow: recovered fuel represents only 5% (173 t/y) of total loaded fuel. • Waste accumulates within the system at a rate of 8958 t/y, mainly in the form of depleted uranium (6428 t/y) and spent fuel (2448 t/y). Depleted uranium waste export (9255 t/y) is larger than accumulation within the system (6248 t/y). An important share of Europe’s low-level waste is thus disposed of outside its own borders, mainly in the form of depleted uranium export from France to Russia (according

to trade data supplied by diverse national trade and statistics agencies and from ref 6). From a material perspective (Figure 2), the major flows are mined rock (766 000 t/y) and waste rock (638 000 t/y), followed by mill tailings (130 000 t/y). Uranium loaded in reactors represents only 0.025% of the rock mass that must be extracted. Since most of Europe’s uranium is mined abroad, a large part of wastes caused by Europe actually accumulate outside the system, at a rate of about 1.1  107 t/y waste rock and 2  106 t/y mill tailings (these values were calculated separately and are not shown in Figure 2, since the corresponding flows occur outside the system boundaries). Energy Requirement. The total energy requirement of the European nuclear fuel cycle for the year 2007 was estimated at 42300 GWh. Enrichment requires 40% of this amount (17 000 GWh/y), followed by conversion (9000 GWh/y), disposal of depleted uranium (7500 GWh/y), and recovery of spent fuel (5500 GWh/y) (SI Figure S4). Chemical Risks. The most important chemical risks are linked to mill tailings, and use of chemicals during front end processing and recycling.6 Mill tailings, when dumped, are often exposed to wind and rain, leading to risk of dispersal of the heavy metals and strong acids they contain; watertightness of dumps is not always respected, increasing risk of leaching of harmful substances. A further result is that the magnitude of chemical risks is not correlated with the magnitude of the uranium or material flows (Figure 3). 2444

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Figure 1. Uranium flow model (all isotopes combined) for Europe, 2007, in tU/y (for flow explanation SI Figure S3 and description, for error margins SI Figure S6). Several flows contain small to moderate amounts of plutonium (the mass of which is included in order to ensure mass conservation), indicated in tHM/y.

Figure 2. Nuclear materials flow model for Europe, 2007. Flows in tmaterials/y (error margins: SI Figure S7).

Proliferation Risks. Proliferation risks are mainly linked with spent fuel reprocessing (Figure 3), in particular the separation of plutonium from other highly radioactive isotopes in spent fuel. This facilitates handling, transport, and processing of recovered plutonium, which contains about 60% of fissile plutonium, a grade high enough to present a serious proliferation risk.6,21

Radiation Risks. The most important radiation risks are linked to all flows following burning in the reactor: conditioning, reprocessing, and disposal of spent fuel, which takes some 100 000 years to reach the radiation level of natural uranium ore,17 and requires heavy shielding (several meters of concrete for example) to avoid radiation reaching the environment.6 2445

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Figure 3. Uranium flow model with indication of the relative importance of chemical and radiation risks associated with processes (bar graphs), and a qualitative indication of the proliferation risk associated with flows and processes, identified according to the isotopic content of the flows (colors). The widths of the arrows reflect flow magnitudes from Figure 1.

Figure 4. Integrative assessment of scenarios according to the sustainability criteria (Table 2). Rating: 1 = large decrease, 5 = no change (similar to status quo), 9 = large increase.

Scenarios. Figure 4 presents the results of the integrative assessment for the status quo and scenarios. Environmental Perspective. R1 to R4 allow a reduction of natural uranium demand between 6% (maximal depleted uranium re-enrichment) to 15% (maximal spent fuel recovery). Fast reactor technology, the availability of which is still uncertain and unlikely to occur before 2050,31 could make a major breakthrough in material efficiency by reducing not only fresh uranium requirements and waste production by 99.4%, but also the radioactivity and half-life of wastes 24,31 (SI, Figure S5). Economic Perspective. R1 and R2 involve similar or higher costs than the current situation: any step taken toward material efficiency will imply additional costs. Fast reactors (R5) require a heavy and somewhat risky investment in research and development:6,23,31 a

transition to fast reactors in Russia for example is estimated to cost around $5 bio, in addition to the $6 bio planned in the next stages of generation-IV fast reactor development.2 In comparison, estimates for deep geological repositories reach around $6 bio for Switzerland 36 and $96 bio for the U.S.37 However, both the costs of fast reactor development and deep geological repositories appear difficult to predict, and are thus not easily comparable. It may be added that fast reactors would not remove the need for repositories, but would decrease the required volume. Social Perspective. A key issue is social acceptance of a technology, linked to the related risks. In the current international political setting, proliferation risk might become an important aspect of social acceptance.9 2446

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Environmental Science & Technology Fast reactors are capable of breeding plutonium from nonfissile uranium. This may be problematic if the associated fuel cycle does not reprocess spent fuel without separating plutonium, in which case, proliferation risks could increase significantly.6,21 Although several assessments consider fast reactors capable of achieving a similar proliferation resistance to the current fuel cycle, this depends largely on the application of extrinsic resistance factors such as government regulations and compliance,23 which appear more difficult to control.38 Fast reactors induce a decrease in intrinsic proliferation resistance (SI Table S2) due to increased flows of reprocessed fuel with a lower resistance than fresh or MOX fuels. However, they can also decrease plutonium stocks, if tuned to burning rather than breeding, which would in turn increase proliferation resistance.21,23,28 Trade-Offs. In general, reduction of material demand and waste is not correlated with other criteria such as costs, environmental and proliferation risks (Figure 4). Maximal recovery of spent fuel (R4) is actually undesirable in all aspects other than demand reduction. In the short term, an option for reducing uranium demand without increasing other impacts is the combination of R3 and R4 (Figure 4), which could lead to a 17.9% reduction of natural uranium demand. However, the effect of such adjustments on the efficiency of energy production must be considered. R3þR4 could also be combined with depleted uranium reenrichment, although once the tails assay is reduced to 0.2%, the depleted uranium can technically and economically no longer be re-enriched: the two scenarios are thus partly mutually exclusive. Due to the necessity of an additional enrichment stage, without a significant waste decrease, re-enrichment of depleted uranium is less desirable than reduction of enrichment tails assay.

’ DISCUSSION Although uncertainties were sometimes large, our results are unambiguous: the model enables identification of major flows and tendencies, and provides support for primary discrimination between fuel cycle scenarios39 while demonstrating the importance of integrating material flow methodology with assessment criteria. Our results are in agreement with models which study in more detail specific aspects of the nuclear fuel cycle or specific criteria. As such, our integrative assessment provides a decision support tool for analyzing the major trade-offs regarding different scenarios. After the selection of the desired scenario, models such as COSI 15 could be applied to plan the transition phase in more detail. Our results suggest that nuclear energy involves several tradeoffs. The technological and investment choices depend on the priorities set at the national or even European level. From a geopolitical perspective, the import of raw material (Europe requires about 24 000 t/y natural uranium, of which 95% is imported), with a high dependency on countries such as Russia (providing 25% of uranium and 31% of enrichment services) would imply that if Europe continues with nuclear energy (which currently produces 30% of European electricity,2 SI Figure S1), it should focus on further development of new technologies (e.g., fast reactor systems), which require less raw materials and are able to recycle waste materials. However, increasing material efficiency is not correlated with the reduction of other impacts. Indeed if such a measure is taken, proliferation risks for example are highly uncertain and expected to increase. Furthermore, the European nuclear fuel cycle causes significant externalities which cannot be ignored: more than half of

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depleted uranium produced is disposed of outside the system; slightly less than half of the natural uranium is processed outside the system, causing accumulation of wastes, and emissions outside the European borders. In Europe, no permanent high-level waste repositories exist; only one is under construction in Finland (planned to be operational by 2020). A site and design have been selected in Sweden, and studies are in development in France.2 In other countries such as Switzerland, the search for repository sites is delayed or has been altogether stopped due to concerns with public acceptance.8 The example of Yucca Mountain underlines the difficulty of maintaining planned deadlines and budgets for geological repositories.40 From a security and social acceptance perspective, a key issue is minimization of proliferation risks. There is an implicit trade-off between waste management optimization and proliferation resistance when using fast reactors, as the decrease of radioactivity and heat in waste for the first also facilitates proliferation.21 However, from a material perspective, conventional technologies achieve at best a 17.9% reduction of uranium demand through a combination of reduced tails assay and reduced enrichment grade, whereas fast reactors could achieve a 99.4% reduction of uranium demand. A further issue of importance is social acceptance: recent surveys9,41 have revealed that the European population, in majority against nuclear energy, considers itself misinformed on nuclear issues, and generally desires a higher participation and discussion on nuclear issues. Security against terrorist attacks, proliferation, and disposal of nuclear wastes are seen as major issues. Those who consider themselves better informed see nuclear wastes as less of a risk. It has been shown that in a democracy, a combination of technical safety with procedural fairness and participation could enhance acceptance of geological repositories.42 These elements suggest that an optimization of the nuclear fuel cycle according to all criteria may not be possible; therefore a key element in determining the most suitable solution is the weighting of the assessment criteria which is expected to differ across different stakeholder groups.43 A solution can only be obtained by a thorough analysis of the interests of involved stakeholders,44,45 and through making their values (normative perspective) explicit. This should be followed by consensus building process 46 including broad stakeholder involvement, transparent process definition, and a stepwise procedure to implement the solution.8

’ ASSOCIATED CONTENT

bS

Supporting Information. Glossary of common nuclear terms; details concerning nuclear context and electricity shares in Europe; conceptual framework; data sources; error estimation; model description; energy requirements; fast reactor fuel cycle model; uncertainty of flows; proliferation resistance measurement. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Phone: 41-44-377-75-79; fax: þ41-44-377-72-01; E-mail: [email protected].

’ ACKNOWLEDGMENT We thank the agencies who provided data. We thank three anonymous reviewers for their helpful comments and references. Furthermore we thank Thomas Graedel, Michael Stauffacher, 2447

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Environmental Science & Technology and Christof Kn€ori for their remarks, and Suren Erkman for advice during research.

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