In this paper some results of a further development of a technical cooperation project, initiated in 2004, between the CDTN/CNEN, The Brazilian National Nuclear Energy Commission, and the STUK, The Finnish Radiation and Nuclear Safety Authority, are presented. The objective of this project is to study applications of fuzzy logic, and artificial intelligence methods, on uncertainty analysis of high level waste disposal facilities safety assessment. Uncertainty analysis is an essential part of the study of the complex interactions of the features, events and processes, which will affect the performance of the HLW disposal system over the thousands of years in the future. Very often the development of conceptual and computational models requires simplifications and selection of over conservative parameters that can lead to unrealistic results. These results can mask the existing uncertainties which, consequently, can be an obstacle to a better understanding of the natural processes. A correct evaluation of uncertainties and their rule on data interpretation is an important step for the improvement of the confidence in the calculations and public acceptance. This study focuses on dissolution (source), solubility and sorption (sink) as key processes for determination of release and migration of radionuclides. These factors are affected by a number of parameters that characterize the near and far fields such as pH; temperature; redox conditions; and other groundwater properties. On the other hand, these parameters are also consequence of other processes and conditions such as water rock interaction; pH and redox buffering. Fuzzy logic tools have been proved to be suited for dealing with interpretation of complex, and some times conflicting, data. For example, although some parameters, such as pH and carbonate, are treated as independent, they have influence in each other and on the solubility. It is used the technique of fuzzy cognitive mapping is used for analysis of effects of variations on one parameter on the others in a system. This technique uses the concept of fuzzy sets to represent the “quality” of the relation between parameters rather then deterministic numbers.

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