Power probability density function control and performance assessment of a nuclear research reactor

Abharian, Amir Esmaeili; Fadaei, Amir Hosein

doi:10.1016/j.anucene.2013.09.018

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Bounded output distribution control theory has been proposed (Wang & Yue, 2003) to control the output probability density function (PDF) instead of the mean or variance of the system output. B-spline functions have been used to model the reactor PDF (Abhariana & Fadaei, 2014), and the power controller is designed based on an approximation model. The modelling, controller design, and performance assessment method have been used to control the power of the Tehran research reactor.…”

Section: Control For a Tehran Research Reactor With The Power Probabimentioning

confidence: 99%

System science and control techniques for harnessing nuclear energy

Badgujar

2016

Systems Science & Control Engineering

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Scientists and researchers are optimistic about safe usage of nuclear power even after several accidents. The control system is a crucial subsystem of the reactor, and accounts for its safe operation. In this review article, several types of control problems in nuclear reactors are presented and discussed, particularly the direct and indirect effects of temperature changes on the reactivity. The first part of the article describes the architecture for instrumentation and control, and outlines various reactivity feedback. Nuclear fuel handling control and turbine speed control are illustrated. The second part considers simulation techniques used in practice, including simulations of the effects of internal feedback and external reactivity perturbations, simulation techniques used to model the different types of problems, and the control schemes used to control reactor parameters. The paradigm of each control algorithm is explained. Techniques to simulate or to model a reactor coupled with different types of loads are explained.

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Section: Control For a Tehran Research Reactor With The Power Probabimentioning

confidence: 99%

System science and control techniques for harnessing nuclear energy

Badgujar

2016

Systems Science & Control Engineering

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“…Like MWD control in polymerization processes, many other industrial processes also have the problems that the product quality to be controlled is closely linked to output variables that need to follow certain distribution patterns, for example, particle size distribution (PSD) control in polymerization processes [13][14][15][16], pulp fibre length distribution control in paper industries [17], particulate process control in powder industries [18,19], crystal size distribution (CSD) control in crystallization processes [20][21][22], crystal size and shape distribution control of protein crystal aggregation in biopharmaceutical production [23], flame temperature distribution control in furnace systems [24,25], power PDF control in nuclear research reactors [26], and bubble size distribution control in flotation processes [27], to name a few. To tackle the control problems for such systems, the idea of output stochastic distribution control (SDC) or output PDF control has been proposed, in which the full shape of the output distribution is directly controlled [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Predictive PDF control in shaping of molecular weight distribution based on a new modeling algorithm

Zhang¹,

Yue²,

Zhou³

2015

Journal of Process Control

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a b s t r a c tThe aims of this work are to develop an efficient modeling method for establishing dynamic output probability density function (PDF) models using measurement data and to investigate predictive control strategies for controlling the full shape of output PDF rather than the key moments. Using the rational square-root (RSR) B-spline approximation, a new modeling algorithm is proposed in which the actual weights are used instead of the pseudo weights in the weights dynamic model. This replacement can reduce computational load effectively in data-based modeling of a high-dimensional output PDF model. The use of the actual weights in modeling and control has been verified by stability analysis. A predictive PDF model is then constructed, based on which predictive control algorithms are established with the purpose to drive the output PDF towards the desired target PDF over the control process. An analytical solution is obtained for the non-constrained predictive PDF control. For the constrained predictive control, the optimal solution is achieved via solving a constrained nonlinear optimization problem. The integrated method of data-based modeling and predictive PDF control is applied to closed-loop control of molecular weight distribution (MWD) in an exemplar styrene polymerization process, through which the modeling efficiency and the merits of predictive control over standard PDF control are demonstrated and discussed.Crown

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“…Remark 4: The original soft-bound output control problem 319 is stated in (10) with the probability level of P 0 set up for the 320 control objective. This control problem is then transformed 321 to the bounded PDF tracking problem as described in (17) 322 with two constraints on the performance index and the PDF 323 integration, respectively. The integration of the target PDF 324 over the soft-bound region is P 1 that can be calculated by 325 (12).…”

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confidence: 99%

“…Algorithm The following procedure is provided for implementation of this soft-bound control algorithm step by is introduced in (21) for the error dynamic model. It can be seen from the above procedures that with steps i) to vi), the soft-bound output control problem in (10) has been recast into a constrained output PDF tracking problem (17).…”

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Soft-Bound Interval Control System and Its Robust Fault-Tolerant Controller Design

Zhou

Yue

2021

IEEE Trans. Syst. Man Cybern, Syst.

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In this work, a soft-bound interval control problem 1 is proposed for general non-Gaussian systems with the aim 2 to control the output variable within a bounded region at a 3 specified probability level. To find a feasible solution to this 4 challenging task, the initial soft-bound interval control problem 5 has been transformed into an output probability density func-6 tion (PDF) tracking control problem with constrained tracking 7 errors, thereby the controller design can be handled under the 8 established framework of stochastic distribution control (SDC). 9 Fault tolerant control has been developed for soft-bound interval 10 control systems in presence of faults. Three fault detection 11 methods have been proposed based on criteria extracted from the 12 initial soft-bound control problem and the recast PDF tracking 13 problem. An integrated design for fault estimation and fault tol-14 erant control (FTC) is proposed based on a double proportional 15 integral (PI) structure. This integrated FTC is developed through 16 linear matrix inequality (LMI). Extensive simulation studies have 17 been conducted to examine key design factors, implementation 18 issues and effectiveness of the proposed approach. 19 Index Terms-Non-Gaussian systems, soft-bound control, 20 stochastic distribution control (SDC), probability density function 21 (PDF), fault detection, fault tolerant control (FTC). 22 I. INTRODUCTION 23 Stochastic control has been an active area in control engi-24 neering and applications since 1970's as most practical sys-25 tems have stochastic characteristics. Continuous efforts have 26 been made in development of minimum variance control [1]-27 [3], linear quadratic Gaussian (LQG) control [4], Markovian 28 stochastic control [5], stochastic adaptive control, stochastic 29 optimization and forecasting, sliding mode control [6]-[8], to 30 name a few. Most of these methods are focused on stochastic 31 features of system variables, mean and variance for exam-32 ple, under the assumption of Gaussian distribution. In real 33 applications, however, a large number of stochastic processes 34 are non-Gaussian, examples include molecular weight distri-35 bution control in polymerization [9], [10], pulp fiber length 36 distribution control in paper industries [11], particulate process 37 control in powder industries [12], crystal size distribution 38 control in crystallization [13], soil particle distribution control 39 [14], flame temperature distribution control in furnace systems 40 [15], [16] and power probability density function control in

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Power probability density function control and performance assessment of a nuclear research reactor

Cited by 9 publications

References 11 publications

System science and control techniques for harnessing nuclear energy

System science and control techniques for harnessing nuclear energy

Predictive PDF control in shaping of molecular weight distribution based on a new modeling algorithm

Soft-Bound Interval Control System and Its Robust Fault-Tolerant Controller Design

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