LAUSR

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. In order 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 is remarkable, enabling strategies for gathering reliable information about the chemical process in the primary loop, towards possible operational improvements.