Estimation of fuel temperature reactivity coefficient of a research reactor using ANFIS

• Published in: IOP conference series. Materials Science and Engineering 2019-06, Vol.555 (1), p.12002
• Main Authors: Mohd Ali, Nur Syazwani, Hamzah, Khaidzir, Idris, Faridah Mohamad, Alang Md Rashid, Nahrul Khair, Minhat, Mohamad Sabri, Rabir, Mohamad Hairie
• Format: Article
• Language: English
• Subjects: Adaptive systems; Artificial neural networks; Coefficients; Control rods; Fuels; Fuzzy logic; Nuclear fuel elements; Nuclear fuels; Nuclear reactors; Nuclear research and test reactors; Nuclear safety; Power reactors; Reactivity; Reactors
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/555/1/012002

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.