A multiple objective optimization approach to the decommissioning and dismantling of a nuclear power plant.

The complexity, relevance and critical nature of the decommissioning of nuclear power plants (NPP) are of great significance in today\u27s society. Following the catastrophe in Fukushima a shift in the general public\u27s perception of NPP took place throughout the world and in Europe in particular. In this dissertation interdisciplinary methods will be discussed to identify solutions which take into account the technological complexity and organizational issues involved in the dismantling and decommissioning process of NPP. Operations research, lean management, simultaneous engineering, cost analysis, multiple-objective optimization, project management, software tools are powerful concepts and methodologies when undertaking the dismantling and decommissioning process of NPP. Besides the presentation of a wide range of terminological and methodological definitions and technical terms based on the Literature Review, in the dissertation a framework for model development of a Multiple objective optimization problem (MOOP) will discussed focusing on empirical data from a virtual NPP. The theoretical foundation of the framework is at the intersection of two successful approaches used to describe and accomplish similar complex challenges, and the integration of state-of-the-art process approaches such as lean management. The procedural conception of the model is mainly leant on the OMEGA model (International Atomic Energy Agency (IAEA) (2008)). Mathematically the model is derived from Jones et. al. (1998). Finally the application of the model using different software tools (AIMMS, MATLAB, R and SPSS) will be presented. In conclusion the work will be put into a position to venture a critical outlook and discussion for the future of the decommissioning and dismantling processes of NPP. The main goal of this dissertation is to define the requirements for the optimization of three objectives: Minimizing the total project cost, reducing the safety hazard (risk) and managing project duration. Also a description of how the programming language R and the AIMMS program interfaces with the OMEGA application and how R will be used to solve the MOOP will be given. The software Microsoft Project will be leveraged in order to model this objective

A methodology based on benchmarking to learn across megaprojects: the case of nuclear decommissioning

Purpose: The literature lacks a single and universally accepted definition of major and megaprojects: usually, these projects are described as projects with a budget above $1 billion and a high level of innovation, complexity & uniqueness both in terms of physical infrastructure and stakeholder network. Moreover, they often provide fewer benefits than what were originally expected and are affected by delays and cost overruns. Despite this techno-economic magnitude, it is still extremely hard to gather lessons learned from these projects in a systematic way. This paper presents an innovative methodology based on benchmarking to investigate good and bad practices and learn from a portfolio of unique megaprojects. Design/methodology/approach: The methodology combines quantitative & qualitative cross-comparison of case studies and statistical analysis into an iterative process. Findings: Indeed, benchmarking offers significant potential to identify good and bad practices and improve the performance of project selection, planning and delivery. Research limitations/implications: The methodology is exemplified in this paper using the case of Nuclear Decommissioning Projects and Programmes (NDPs). Originality/value: Indeed, due to their characteristics, NDPs can be addressed as megaprojects, and are a relevant example for the application of the methodology presented here that collects and investigates the characteristics that mostly impact the performance of (mega)projects, through a continuous learning process

Comparison and Screening of Nuclear Fuel Cycle Options in View of Sustainable Performance and Waste Management

Is it true that a nuclear technology approach to generate electric energy offers a clean, safe, reliable and affordable, i.e., sustainable option? In principle yes, however a technology impact on the environment strongly depends on the actual implementation bearing residual risks due to technical failures, human factors, or natural catastrophes. A full response is thus difficult and can be given first when the wicked multi-disciplinary issues get well formulated and “resolved”. These problems are lying at the interface between: the necessary R&D effort, the industrial deployment and the technology impact in view of the environmental sustainability including the management of produced hazardous waste. As such, this problem is clearly of multi-dimensional nature. This enormous complexity indicates that just a description of the problem might cause a dilemma. The paper proposes a novel holistic approach applying Multi-Criteria Decision Analysis to assess the potential of nuclear energy systems with respect to a sustainable performance. It shows how to establish a multi-level criteria structure tree and examines the trading-off techniques for scoring and ranking of options. The presented framework allows multi-criteria and multi-group treatment. The methodology can be applied to support any pre-decisional process launched in a country to find the best nuclear and/or non-nuclear option according to national preferences and priorities. The approach addresses major aspects of the environmental footprint of nuclear energy systems. As a case study, advanced nuclear fuel cycles are analyzed, which were previously investigated by the Nuclear Energy Agency (NEA/OECD) expert group WASTEMAN. Sustainability facets of waste management, resource utilization and economics are in focus

Garigliano nuclear power plant: seismic evaluation of the turbine building

The Italian Garigliano Nuclear Power Plant (NPP) started its energy production in 1963. At present it is in the decommissioning stage. In order to get a proper management of the radioactive waste that will be produced during the dismantling operations it has been considered convenient to convert the turbine building of the plant into a temporary waste repository. This decision posed a remarkable seismic safety assessment issue. As a matter of fact, the challenge was to extend, in satisfactory safety conditions, the use of an important facility that has reached the end of its designed lifetime and to have this extended use approved by nuclear safety agencies. In this context many tasks have been accomplished, of which the most important are: (a) a new appraisal of site seismic hazard; (b) the execution of many investigations and testing on the construction materials; (c) the set up of a detailed 3D finite element model including the explicit representation of foundation piles and soil; (d) consideration of soil structure kinematic and dynamic nteraction effects. This paper describes the adopted seismic safety assessment criteria which are based on a performance objectives design approach. While performance based design is the approach currently recommended by European Regulations to manage seismic risk and it is fully incorporated in the Italian code for conventional buildings, bridges and plants, NPP are not explicitly considered. Therefore it was necessary to delineate a consistent interpretation of prescribed rules in order to properly select the maximum and operating design earthquakes on one side and corresponding acceptable limit states on the other side. The paper further provides an outline of the numerical analyses carried out, of the main results obtained and of the principal retrofitting actions that will be realized

Decision-support for decommissioning offshore platforms.

An estimated 2,500 offshore decommissioning projects are expected to be completed between 2018 and 2040 with significant accompanying challenges. In this research, a decision model for decommissioning offshore platforms is developed. The decommissioning decision model (DDM) aids logical determination of the optimal option for decommissioning a platform through a multicriteria decision analysis of the considered options with respect to safety, cost, environmental impact, technical feasibility, and public perception. It synthesizes information about a platform's features with expert opinion to identify the best option for decommissioning the platform from a list of available options. It also facilitates the progressive integration of historical data to replace subjective human opinion and improve the quality of decision-making as this becomes available. A case-study approach was used to demonstrate the DDM's applicability with information from an industry survey of decommissioning practitioners. Five decommissioning options were considered for the case study platform, and these were evaluated with a hybrid of Likert scale and Analytic Hierarchy Process (AHP). Using this technique, the optimal option for decommissioning the case study was determined with a 60% efficiency savings in time taken to complete the analysis as compared to the traditional AHP process. Results showed that partial removal is the preferred option for the case study, and the platform features with high relevance to options selection are substructure weight, water depth and age. Moreso, respondents from the North Sea were observed to be more averse to leaving platform materials in place as compared to people from Offshore USA, Africa, and Asian Seas. These findings were seen to agree with literature and industry practice through a comprehensive validation process. Thus, evidencing the DDM's flexibility and robustness and making a case for its industry adoption. After its validation, the DDM's capability to support integration of historical data was investigated with the aid of a prediction model for estimating the costs of using different options for decommissioning offshore platforms. This costing model was developed by applying machine learning regression to historical decommissioning cost data. The model predicts decommissioning options costs for five different scenarios with reasonable accuracy as indicated by an r-squared value of 0.935, implying that it is reliable for predicting decommissioning costs. It was used to predict decommissioning options costs for the case study. These costs were then integrated into the DDM to replace the input data for cost criterion as obtained from the survey. The models developed in this research improve upon the existing works in decommissioning optimisation. Industry adoption of the decision model will result to significant reduction of time, resources and efforts spent in decision-making during decommissioning. By acting as an unbiased basis for justifying the choice of a decommissioning option for an offshore asset, the DDM mitigates the traditional conflict between stakeholders of decommissioning projects. The costing model aids early estimation of decommissioning costs for budgeting, asset trading and other preliminary cost evaluation purposes prior to detailed engineering cost estimation. Therefore, both models represent a significant contribution towards the advancement of the current offshore decommissioning practice

A BIM-driven framework for integrating rules and regulations in the decommissioning of nuclear power plants

Purpose: The relative low capital cost and contributions to mitigating global warming have favoured the continuous construction and operation of nuclear power plants across the world. One critical phase in the operation of nuclear plants for ensuring safety and security of radioactive products and by-products is decommissioning. With the advent of digital twinning in the building information modelling (BIM) methodology, efficiency and safety can be improved from context-focus access to regulations pertaining demolition of structures, and cleaning-up of radioactivity inherent in nuclear stations. A BIM-driven framework to achieve a more regulation-aware and safer decommissioning of nuclear plants is proposed. Design: The framework considers task requirements, and landscape and environmental factors in modelling demolition scenarios that characterise decommissioning processes. The framework integrates decommissioning rules/regulations in a BIM linked non-structured query system to model items and decommissioning tasks, which are implemented based on context-focused retrieval of decommissioning rules and regulations. The concept’s efficacy is demonstrated using example cases of digitalised nuclear power plants. Findings: This approach contributes to enhancing improvements in nuclear plant decommissioning with potential for appropriate activity sequencing, risk reduction, and ensuring safety. Originality: A BIM-driven framework hinged on querying non-structured databases to provide context-focused access to nuclear rules and regulations, and to aiding decommissioning, is new

Loodussäästliku tuumkütuse tsükli modelleerimine ja analüüs optimaalseks tooraine kasutuseks ja radioloogilise mõju vähendamiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioonePaljud riigid püüavad piirata kasvuhoonegaaside heitkoguseid ja püüavad keskenduda süsinikuvabadele energiaallikatele, mistõttu on tuumaenergia jätkuva arutelu teema, et laiendada kogu maailma energiatootmise segmenti ja katta baaskoormuse nõudeid. On vaja hinnangut turul pakutavatele uraanitarnetele ja vastavat ressursside optimeerimise konteksti, tegemaks otsuseid, mis mõjutavad tuumaenergia pikaajalist arengut, ning hoiatab poliitikakujundajaid võimalikest uraaniturul toimuvatest muutustest, et aidata teha tehnoloogilisi valikuid. Käesoleva töö eesmärgiks on demonstreerida ja katsetada arenenud simulatsioonimeetodeid ja kontseptsioone, et luua realistlikumaid kütusetsüklite simulaatoreid ja seeläbi luua otsusetegijatele vajalik abivahend. Lisaks lahendab töö optimeerimisparadigma, leidmaks soodsaima tuumakütusetsükli tehnoloogia, mis aitab säästa loodusvarasid, suudab minimiseerida kõrgetasemelist radioaktiivsete jäätmete mõju loodusele ja vähendades tuumamaterjalide levikust tulenevatMany countries are struggling to limit greenhouse gas emissions and focus on carbon-free energy sources, and therefore nuclear energy is a subject of continuing debate to enhance the worldwide energy production mix and cover the base load requirements. Evaluation of uranium supply in a market and resource optimization context is needed to inform decisions impacting the long-term development of nuclear power and warn policy makers about possible uranium market supply-side volatilities and to help choose the technology mix. The aim of this work is to demonstrate and test advanced simulation methods and conceptual ideas to enable realistic fuel cycle simulators and develop a supporting tool for decision makers. Additionally, this work facilitates the selection of the most favorable nuclear fuel cycle, using optimization with respect to multiple criteria save natural resources, minimize the impact of high level radioactive waste on nature, to and decrease nuclear material proliferation risks