🇪🇸Dynamic Bayesian Networks for Prediction of Chemical Radioisotope Levels in Nuclear Reactors

Presented at the 10th Annual BayesiaLab Conference on Thursday, October 27, 2022.

Abstract

Radiation dose in nuclear power plant reactors is known to be dominated by the presence of radioisotopes in the primary loop of the reactor. To strictly control it in normal operation (e.g., cleaning and reloading of nuclear fuel), established chemical theories exist to explain the amount of radioisotopes present in the reactor water circuits with respect to known control variables in the plant (e.g., thermal power on the reactor, pH, hydrogen, etc.). However, the high complexity and the uncertainty of the process make difficult an accurate estimation of the measured values of radioisotopes. In order to address this problem, this article introduces a dynamic Bayesian network (DBN) probabilistic model that allows to experimentally demonstrate the capabilities of the control variables to give information about the value of the radioisotope concentrations and to predict their values in a data-driven way. Our results in 5 different nuclear power plants show that the accuracy and reliability of these predictions are remarkable, enabling strategies for gathering reliable information about the chemical process in the primary loop towards possible operational improvements.

Presentation Video

Presentation Slides

About the Presenter

Dr. Daniel Ramos finished his Ph.D. in 2007 at Universidad Autonoma de Madrid (UAM), Spain. Since 2011, he has been an Associate Professor at the UAM. He is a staff member of AUDIAS Group. During his career, he has visited several research laboratories and institutions around the world, including the Institute of Scientific Police at the University of Lausanne (Switzerland), the School of Mathematics at the University of Edinburgh (Scotland), the Electrical Engineering School at the University of Stellenbosch (South Africa), and more recently the Netherlands Forensic Institute and the Computational and Biological Learning Lab of the University of Cambridge. He has been visiting professor at the Universidad de Buenos Aires in 2019. His research interests focus on the forensic evaluation of the evidence using Bayesian techniques, probabilistic calibration, validation of forensic evaluation methods, speaker and language recognition, and, more generally, signal processing and pattern recognition. Dr. Ramos is actively involved in the research of development of different aspects of forensic science, including the statistical evaluation of speech and chemical evidence (mainly glass). He has been invited by the NIST to several workshops, including the OSAC standardization initiative. He is the author of multiple publications in national and international journals and conferences, some of them awarded. He has also participated in several international competitive evaluations of speaker and language recognition technology since 2003. Recently, he has been working on signal processing and machine learning for industrial applications in the energy sector. Dr. Ramos is regularly a member of scientific committees at different international conferences, and he is often invited to give talks at conferences and institutions.