Performance of Receding horizon predictive controller for research reactors

Andraws, Mina S.; Mahmoud, Imbaby I.; El-Hamid, Asmaa A. Abd; Hammad, Sherif; Yousef, Ahmed H.

doi:10.1109/icces.2017.8275317

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…The detail conventional CRSA algorithm at RTP can be found in [26] and the simplified algorithm block diagram shown in Figure 5. Another type of conventional CRSA method is proposed in [7] for the Egyptian Second Testing Research Reactor (ETRR-2). Instead of taking the lowest rod position to be withdrawal, the control rod with the lowest worth value is selected to increase the reactor power or vice versa.…”

Section: Control Rod Selection Algorithm (Crsa)mentioning

confidence: 99%

“…A similar algorithm is used by conventional CRSA in [7] with a slightly different local variable name. For example, "CR(i)_POS_COUNTER" (rod position) is represented by "CR(i)_CrWorth" (rod worth) in [7]. The optimal solution with less complexity and time response for the selection rods is introduced in the nuclear reactor.…”

Section: 4 For(i=1 ; I <5 ;I++){ If( (Cr(i)_position_counter>=max_pomentioning

confidence: 99%

“…The movement of compensating rods is decided by the control rod selector logic [7] known as Control Rod Selection Algorithm (CRSA). To the best of our knowledge, conventional CRSA compensates multiple rods [7] (4 control rods for RTP) or using a single Regulating rod as referring to [8][9][10][11][12][13] for introducing the external reactivity in TRIGA reactors. The conventional CRSA applied either by getting the minimum position or rod worth value of four control rods and select the lowest value to withdraw the control rod to increase the reactor's power.…”

Section: Introductionmentioning

confidence: 99%

“…To ensure the rapid process of the rod selection, the conventional CRSA is established by the simplest form of logical sequence control rod movement based on the rod position regardless of the control rod worth value. For instance, simplifying a group of control rods to one control rod [21], taking rod worth curve fitting by using sine wave equation in [22][23][24], using differential equation based on rod position [25][26], using empirical static function [27], using two control rod model [28] and using control rod withdrawal percentage [7]. Unfortunately, the presented control rod position dynamic model did not consider the actual CDRM and difference between rod worth values for each individual control rods (non-linear function) due to fuel burnup for long time operation in the case of big margin which may lead to inaccuracy result.…”

Section: Introductionmentioning

confidence: 99%

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An improved control rod selection algorithm for core power control at TRIGA PUSPATI Reactor

Minhat

Subha

Hassan

et al. 2020

JMES

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The 1 MWth TRIGA PUSPATI Reactor known as RTP undergoes more than 37 years of operation in Malaysia. The current core power control utilized Feedback Control Algorithm (FCA) and a conventional Control Rod Selection Algorithm (CRSA). However, the current power tracking performance suffers and increase the workload on Control Rod Drive Mechanism (CRDM) if the range between minimum and maximum rod worth value for each control rod has a significant difference. Thus, it is requiring much time to keep the core power stable at the power demand value within the acceptable error bands for the safety requirement of the RTP. In conventional CRSA, regardless of the rod worth value, the lowest position of the control rod is selected for up-movement to regulate the reactor power with 2% chattering error. To improve this method, a new CRSA is introduced named Single Control Absorbing Rod (SCAR). In SCAR, only one rod with highest reactivity worth value will be selected for coast tuning during transient and the lowest reactivity worth value will be selected for fine-tuning rod movement during steady-state. The simulation model of the reactor core is represented based on point kinetics model, thermal-hydraulic models and reactivity model. The conventional CRSA model included with control rod position dynamic model and actual reactivity worth curve data from RTP. The FCA controller is designed based on Proportional-Integral (PI) controller using MATLAB Simulink simulation. The core power control system is represented by the integration of a reactor core model, CRSA model and FCA controller. To manifest the effectiveness of the proposed SCAR algorithm, the results are compared to the conventional CRSA in both simulation and experimentation. Overall, the results shows that the SCAR algorithm offers generally better results than the conventional CRSA with the reduction in rising time up to 44%, workload up to 35%, settling time up to 26% and chattering error up to 18% of the nominal value.

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Section: Control Rod Selection Algorithm (Crsa)mentioning

confidence: 99%

Section: 4 For(i=1 ; I <5 ;I++){ If( (Cr(i)_position_counter>=max_pomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An improved control rod selection algorithm for core power control at TRIGA PUSPATI Reactor

Minhat

Subha

Hassan

et al. 2020

JMES

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“…Most studies using analytical for designing the MPC core power control as the current practices [11], [12], and other possibilities methods such as System Identification (ID) help to simplify the process and convert the non-linear model based on zero dimension approach in ordinary differential equation (ODE) to state-space form. In this study, to evaluate the effectiveness of the developed MPC and Fuzzy PCRC strategies, a set of performance criteria based on a descriptive approach which is most commonly used in this core power control application area such as settling time, rise time, per cent overshoot, chattering error, offset error, workload, and energy released.…”

Section: Introductionmentioning

confidence: 99%

Model predictive and fuzzy logic controllers for reactor power control at Reaktor TRIGA PUSPATI

Minhat

Subha

Hassan

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The Reaktor TRIGA PUSPATI (RTP) rely extensively on a core power control system for control reactivity to fulfilment the fundamental safety function for a nuclear reactor. It is technically challenging to operate within tight multiple parameter constraints and keep the core power output stable. At present, the power tracking performance of the system could be considered unsatisfactory which produces a relatively long settling time and high control effort. Hence, a study of a model predictive control (MPC) strategy by integrating the current Power Change Rate Constraint (PCRC) using fuzzy logic which is part of the core power control design is conducted. In this paper, the MPC design based on mathematical models of the reactor core included point kinetics model, thermal-hydraulic model, reactivity model and dynamic rod position model help to enhance core power control. The power tracking performance of the proposed control method and previous Feedback Control Algorithm-Fuzzy PCRC is compared via computer simulation with different power range operations. Overall, the results show the MPC-Fuzzy PCRC strategy provides a greater level of operational safety and optimum operation of the TRIGA reactor.

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Performance Evaluation of Research Reactors Under Different Predictive Controllers

Andraws

El-Hamid

Yousef

et al. 2019

Advances in Intelligent Systems and Computing

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Performance of Receding horizon predictive controller for research reactors

Cited by 4 publications

References 23 publications

An improved control rod selection algorithm for core power control at TRIGA PUSPATI Reactor

An improved control rod selection algorithm for core power control at TRIGA PUSPATI Reactor

Model predictive and fuzzy logic controllers for reactor power control at Reaktor TRIGA PUSPATI

Performance Evaluation of Research Reactors Under Different Predictive Controllers

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