Abstract
Fuel Temperature reactivity coefficient is one of the important operational and safety parameters in nuclear reactors to monitor for normal operation and transient safety analysis in research as well as in power reactors. The aim of this work is to estimate the temperature reactivity coefficient by using adaptive neuro-fuzzy inference system (ANFIS) as a preliminary assessment for developing an online monitoring system in research reactors such as the Reactor TRIGA PUSPATI (RTP). The input for the learning mechanism is the fuel temperature recorded at Ch A of the instrumented fuel element (IFE) and control rod positions with the fuel temperature reactivity coefficient as the targeted output. The ANFIS model was validated using testing data that consisted of fuel temperature measured at Ch B and results on -0.00571 $°C−1 (-3.997 pcm°C−1) of actual output and -0.00592 $°C−1 (4.144 pcm°C−1) of predicted output. Through the developed model, it showed that the ANFIS also can be used as a tool to estimate the fuel temperature reactivity coefficient with good prediction and accuracy shown from the calculation of mean absolute error (MAE) of 0.00349.
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