Modeling and Intelligent Controller Design for Reactor Regulating System of Advanced CANDU Reactor (ACR-700) in LabVIEW

ACR-700 Reactor Dynamics and Intelligent Control


  • Arshad H. Malik Pakistan Atomic Energy Commission, Chashma, Pakistan
  • Feroza Arshad Pakistan Atomic Energy Commission, Karachi, Pakistan
  • Aftab A. Memon Mehran University of Engineering and Technology, Jamshoro, Pakistan



Spatial Reactor Dynamics, Intelligent Controller, GA Optimization, ACR-700, LabVIEW


In this research work, an Advanced CANDU Reactor of 700 MWe rating (ACR-700) is attempted for Reactor Regulating System (RRS) modeling and intelligent controller design. ACR-700 is a state-of-the-art advanced generation-III reactor. The reactor regulating system is modeled with special emphasis on internal and external reactivity devices. ACR-700 is designed with Liquid Zone Control (LZC) Model, Adjuster Banks Model, Absorber Banks Model and Shutdown Banks Model. The RRS model is a highly sophisticated model developed based on the principle of spatial nuclear reactor dynamics. The RRS model is a multivariable model. The spatial reactor dynamics is modeled based on five internal feedbacks and three feedbacks. The original controller design of RRS is comprised of a PID control algorithm. The control design is reattempted with an advanced intelligent algorithm in which the ANFIS controller is used as a modern control design tool. The new ANFIS controller is basically a multivariable controller. All the modeling of RRS is implemented in Visual Basic (VB) Software while the controller is configured in LabVIEW. A special toolkit is designed for the interfacing of Visual Basic and LabVIEW known as VBLAB. The optimization of intelligent controller parameters is carried out by Genetic Algorithm (GA). The GA-optimized intelligent controller is configured with the VB RRS model. All the variable trends are visualized in the VB environment. The proposed closed-loop ACR-700 RRS control system is tested for small perturbation Analysis and power ramp-down transient and found with excellent behavior well within the design limits. The performance of the suggested ACR-700 RRS controller is compared with the existing conventional controller. The performance of the suggested control scheme is marked with reduced oscillations and faster as compared to the existing control scheme.


M. Johnson, S. Lucas, and P. Tsvetkov. Modeling of reactor kinetics and dynamics. Idaho National Laboratory, Report Idaho Falls, Idaho 83415, U.S. Department of Energy, Canada (2010).

W. K. Lam. Advanced pressurized water reactor simulator. IAEA Workshop on NPP Simulators for Education, Bucharest, Romania (2006).

A. S. Mollah. Education tool for simulation of safety and transient analysis of a pressurized water reactor. International Journal of Integrated Sciences & Technology 03: 01-10 (2018).

S. U. E. Hakim, A. Abimanyu, and Sutanto. Simulator design of Kartini reactor based on LabVIEW. Journal forum Nukir 12: 29-41 (2018).

A. H. Malik, A. A. Memon, and F. Arshad. Advanced multi-modeling of PWR dynamics and deep learning based computational tool in SIMULINK and LabVIEW. Proceedings of Pakistan Academy of Sciences-A: Physical and Computational Sciences 59: 71-81 (2022).

S. M. H. Mousakazemi. Control of a pressurized light-water nuclear reactor two-point kinetics model with performance index-oriented PSO. Nuclear Engineering and Technology 53: 2556-2563 (2021).

H. S. Shim, and J. C. Jung. Improved RRS logical architecture using genetic algorithm. Nuclear Engineering and Technology 49: 1696-1710 (2017).

C. H. Cho, J. H. Choi, J. Y. Hoi, and S. Y. Lee. CANDU NPP reactor regulating system modeling using CATHENA. Annulus of Nuclear Energy 23: 909-918 (1996).

G. Boroni, and A. Clausse. Ladwig: A training simulator of the safety operation of a CANDU. Science and Technology of Nuclear Installations: 1-8 (2011).

C. S. Subudhi, T. U. Bhatt, and A. P. Tiwari. A mathematical model for total power control loop of large PHWRs. IEEE Transactions on Nuclear Science 46: 1901-1911 (2016).

V. S. Volodin, and A. O. Tolokonskii. Parameters settings of NPP automatic regulators via analytical simulators. XIII International Youth Scientific and Practical Conference 317-326 (2017).

A. P. Tiwari, T. U. Bhatt, and P. V. Surjagade. Modelling and spatial control of 540 MWe pressurized heavy water reactor. Transactions on Indian National Academy of Engineering 06: 731-753 (2021).

S. Banerjee, D. Bose, A. Hazra, S. Chattopadhyay, and K. Ghosh. Controller design for operation of a 700 MWe PHWR with limited voiding. Nuclear Engineering and Design 357: 1-12 (2020).

M. C. Darling, G. F. Luger, T. B. Jonesy, M. R. Denman, and K. M. Groth. Intelligent modeling for nuclear power plant accident management. International Journal on Artificial Intelligence Tools 27: 1-25 (2018).

A. H. Malik, A. A. Memon, and F. Arshad. Fractional order modelling and robust multi-model intelligent controllers’ synthesis for ACP1000 nuclear power plant. Mehran University Research Journal of Engineering and Technology 41: 43-53 (2022).

M. F. Shidik, and R. S. N. Mahmudah. Safety analysis of advanced CANDU reactor-700 (ACR-700) during transient and emergency condition using ACR simulator. Journal of Sains Dasar 10: 30-35 (2021).




How to Cite

Arshad H. Malik, Feroza Arshad, & Aftab A. Memon. (2022). Modeling and Intelligent Controller Design for Reactor Regulating System of Advanced CANDU Reactor (ACR-700) in LabVIEW: ACR-700 Reactor Dynamics and Intelligent Control. Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences, 59(4), 25–36.



Research Articles