The changes in the operating conditions of the main control room of nuclear power plants due to new digitalized human-system interfaces (HSIs) pose challenges to traditional human reliability analysis (HRA) methods. This paper reviews current knowledge about the effect of digital HSI on human behaviour and reliability, relevant performance shaping factor (PSF) taxonomies as well as research on the effect of PSFs on human error probability estimates in advanced control rooms (ACRs). It cannot be generally concluded that either analogue or digitalized control rooms would be always better than the other. ACRs have the potential to offer error reducing and supportive features to the user, but may also introduce increased complexity and interface management tasks as well as potentially error-prone team working practices. Some effects seem universal, but most depend on the specific design. The main PSF categories presented in HRA methods and international guidelines seem still relevant in ACRs. Quantitative empirical studies show that especially training and experience, availability and quality of procedures as well as task type or complexity are important factors. However, digitalization changes the way in which the PSFs should be defined and measured, and the effects of PSFs on the error estimates may be different.
Nonisothermal multiphase modeling with the T2VOC numerical simulator was used as an integration tool in an in situ remediation project in which trichloroethylene (TCE)‐contaminated soil was treated with thermally (steam) enhanced soil vapor extraction. Numerical simulation studies were employed in the iterative conceptualization of the migration of the contaminants and the effects of different treatment operations, in the planning of optimal system design and control of the remediation processes, as well as for integration of information obtained from field investigations during different phases of the project. Modeling proved to be a valuable tool in planning and decision making. It was, however, difficult to forecast the costs or the duration of the project, since the actual TCE mass and distribution was poorly known. The power of modeling lies in its ability to compare the effect of different strategies or measures on the cleanup efficiency, and in studying the associated effects during the treatments.
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