A coal-to-nuclear (C2N) transition means siting a nuclear reactor at the site of a recently retir... more A coal-to-nuclear (C2N) transition means siting a nuclear reactor at the site of a recently retired coal power plant. Three overarching questions from the C2N transition guide this research: where in the United States are retired coal facilities located and what factors make a site feasible for transition; what factors of technology, cost, and project timeline drive investor economics over such a decision; and how will C2N impact local communities? The study team evaluated the siting characteristics of recently retired plants and those operating coal-fired power plant sites run by a utility or an independent power producer utilizing publicly available data to screen U.S. coal power plant sites to nuclear-feasible locations. After screening all retired coal sites to a set of 157 potential candidates and screening operating sites to a set of 237 candidates, the study team estimates that 80% of retired and operating coal power plant sites that were evaluated have the basic characteristics needed to be considered amenable to host an advanced nuclear reactor. For the recently retired plant sites evaluated, this represents a capacity potential of 64.8 GWe to be backfit at 125 sites. For the operating plant sites evaluated, this represents a capacity Investigating Benefits and Challenges of Converting Retiring Coal Plants into Nuclear Plants iv
Background and Purpose Process mining for conformance analysis consists of comparing a reference ... more Background and Purpose Process mining for conformance analysis consists of comparing a reference process model against a datadriven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. Methods We present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. Results The comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman's correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. Conclusions The foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.
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Papers by Femi Omitaomu