Enhanced manufacturing storage management using data mining prediction techniques

Performing an efficient storage management is a key issue for reducing costs in the manufacturing process. And the first step to accomplish this task is to have good estimations of the consumption of every storage component. For making accurate consumption estimations two main approaches are possible: using past utilization values (time series); and/or considering other external factors affecting the spending rates. Time series forecasting is the most common approach due to the fact that not always is clear the causes affecting consumption. Several classical methods have extensively been used, mainly ARIMA models. As an alternative, in this paper it is proposed to use prediction techniques based on the data mining realm. The use of consumption prediction algorithms clearly increases the storage management efficiency. The predictors based on data mining can offer enhanced solutions in many cases.Telefónica, through the “Cátedra de Telefónica Inteligencia en la Red”Paloma Luna Garrid

Empirical Modeling of Used Nuclear Fuel Radiation Emissions for Safeguards Purposes

For nuclear nonproliferation safeguards, the ability to characterize used nuclear fuel (UNF) is a vital process. Fuel characterization allows for independent verification by inspectors of operator declarations of the special nuclear material flow and nuclear related activities within a facility, and an estimation of fissile material remaining in a fuel assembly. Current methods to verify this information rely heavily on non-destructive assay techniques, such as gamma spectroscopy and neutron detection measurements. While these measurements are effective tools for estimating a specific characteristic of the fuel, such as burnup or cooling time, they often require an accurate estimation of a select few isotopes in the fuel. This requirement means that the characterization is based on a very small amount of information that is contained in radiation emissions. To help overcome this limitation, this work investigates the use of empirical modeling to predict the burnup, initial enrichment, and cooling time of a Westinghouse 17x17 UNF assembly. This technique utilizes the entire spectrum of gamma emissions and gross neutron counts to predict each output to explore the full suite of information contained in these signatures. Three primary parametric modeling techniques are investigated for their performance in modeling this system: Ordinary Least Squares Regression (OLS), Principal Component Regression (PCR), and Partial Least Squares Regression (PLS). The models created are evaluated based on their root mean square percent error and condition number. The uncertainty of the best performing model is then quantified to understand the prediction interval of the predicted characterization and how this compares to the uncertainty of current measurement and characterization techniques. The PLS models are able to provide the best predictions while being stable. The PCR models have a consistent trade-off between accurate prediction results and stability. The OLS model provides fairly accurate results but is highly unstable due to correlations in the input data. The best model is the PLS model based on cross validation because it is stable, yields the lowest RMSPE values for burnup and enrichment predictions, and yields the second lowest percent of cooling time predictions that are more than 1 year away from the actual value. When used with the validation data set, this model yields RMSPE values of 0.42%, 1.39% and 4.61% for the burnup, enrichment, and cooling time, respectively. The total uncertainty of the predictions of this model are calculated to be 0.220 GWd/MTU, 0.051% U-235, and 0.694 years, for the burnup, enrichment, and cooling time, respectively