This paper presents a reactor-monitoring algorithm using the group method of data handling (GMDH) that creates nonlinear algebraic models for system characterization. The monitoring system was applied to the IEA-R1 experimental reactor at the Instituto de Pesquisas Energéticas e Nucleares (IPEN). The IEA-R1 is a 5-MW pool-type research reactor that uses light water as coolant and moderator and graphite as reflector. The GMDH provides a general framework for characterizing the relationships among a set of state variables of a process system and is used for generating estimates of critical variables in an optimal data-driven model form. The monitoring system developed in this work was used to predict the IEA-R1 reactor environment, using nuclear power, rod position, and coolant temperatures, by combining two variables at a time. The results obtained using the GMDH models agreed very well with the dose rate measurements, with prediction errors of less than 5%. The error was minimal when the dose rate prediction was made using reactor power and coolant temperature.
