Effect of Loop Configuration on Steam Drum Level Control for a Multiple Drum Interconnected Loops Pressure Tube Type Boiling Water Reactor

Gaikwad, Avinash J.; Vijayan, P.K.; Iyer, Kannan N.; Bhartiya, Sharad; Kumar, Raj; Lele, H.G.; Ghosh, Aniruddha; Kushwaha, H. S.; Sinha, R.K.

doi:10.1109/tns.2009.2033682

Cited by 11 publications

(2 citation statements)

References 51 publications

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“…Xiaocen Xue [26] utilized a radial basis function neural network (RBFNN) for the FTS slurry reactor control and modeling, successfully predicting the reactor temperature. Favorable control equality was reached by the introduction of a neural network and different air-water loop configurations as well [27][28][29]. In addition, the neural network is an integral part of predictive control, inverse-model-based control, and adaptive control methods [24].…”

Section: Introductionmentioning

confidence: 99%

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

Liu,

Zhang,

Zhang

et al. 2024

Applied Sciences

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The control of steam drums, used to remove heat from Fischer–Tropsch synthesis or diethyl oxalate hydrogenation, is confronted with a challenge on controlling quality. The traditional proportional–integral–differential (PID) controllers with fixed parameters are dissatisfying upon deployment. The backward-propagation neural network (BPNN) self-tuning PID control algorithm was thus developed and implemented via a Python and KINGVIEW software combination. Application experiments showed that, in both setpoint control and step change control of the steam drum pressure, static deviation and the maximum error were less with the BPNN self-tuning PID controller, in comparison to the conventional PID controller. Moreover, it seemed that certain adaptations occurred to the nonlinear change in the reaction system, revealing that it was superior to the traditional PID controller. It is shown that the backward-propagation neural network will improve the control quality in boiling water drum systems for exothermic reactions. It can be predicted that the backward-propagation neural network is qualified for process condition control in the chemical industry.

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Section: Introductionmentioning

confidence: 99%

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

Liu,

Zhang,

Zhang

et al. 2024

Applied Sciences

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“…Steam drum level control at multiple loop configurations has been proposed to enhance the safety margin [9,10]. For proper control of drum level single parameter control is not sufficient, and three element Steam Drum Level Controller has been conventionally used for most of the boilers where controlling parameters are Drum level, steam flow and feed water flow.…”

Section: Introductionmentioning

confidence: 99%

Importance of Three-Elements Boiler Drum Level Control and Its Installation in Power Plant

Chakraborty¹,

Manna²,

Dey³

2014

IJICS

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Adaptive control and signal processing literature survey (No. 17)

2010

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please email Erfu Yang at erfu.yang@eee.strath.ac.uk. ADAPTIVE SYSTEMSAhmed-Ali T, Postoyan R, Lamnabhi-Lagarrigue F. Continuous-discrete adaptive observers for state affine systems. Automatica 2009; 45(12):2986-2990. Chang Y. Adaptive sliding mode control of multi-input nonlinear systems with perturbations to achieve asymptotical stability. IEEE Transactions on Automatic Control 2009; 54(12):2863-2869. Fisher J, Bhattacharya R. Linear quadratic regulation of systems with stochastic parameter uncertainties. Automatica 2009; 45(12):2831-2841. Geromel J, Colaneri P. H ∞ and dwell time specifications of continuous-time switched linear systems. IEEE Transactions on Automatic Control 2010; 55(1):207-212. Girard A, Pola G, Tabuada P. Approximately bisimilar symbolic models for incrementally stable switched systems. IEEE Transactions on Automatic Control 2010; 55(1): 116-126. Hu G, Gans N, Fitz-Coy N, Dixon W. Adaptive homography-based visual servo tracking control via a quaternion formulation. IEEE Transactions on Control Systems Technology 2010; 18(1):128-135. Karagiannis D, Sassano M, Astolfi A. Dynamic scaling and observer design with application to adaptive control. Automatica 2009; 45(12):2883-2889. Leva A, Piroddi L, Felice M, Boer A, Paganini A. Adaptive relay-based control of household freezers with on-off actuators. Control Engineering Practice 2010; 18(1):94-102. Lin Z, Lin Y, Zhang W. A unified design for state and output feedback H ∞ control of nonlinear stochastic Markovian jump systems with state and disturbance-dependent noise. robust control of linear motors with dynamic friction compensation using modified LuGre model. Automatica 2009; 45(12):2890-2896. Mojica-Nava E, Quijano N, Rakoto-Ravalontsalama N, Gauthier A. A polynomial approach for stability analysis of switched systems. Systems and Control Letters 2010; 59(1): 98-104. Nuño E, Ortega R, Basañez L. An adaptive controller for nonlinear teleoperators. Automatica 2010; 46(1):155-159. Patre P, MacKunis W, Johnson M, Dixon W. Composite adaptive control for Euler-Lagrange systems with additive disturbances. Automatica 2010; 46(1):140-147. Sang Q, Tao G. Gain margins of adaptive control systems. IEEE Transactions on Automatic Control 2010; 55(1): 104-115. Tanelli M, Picasso B, Bolzern P, Colaneri P. Almost sure stabilization of uncertain continuous-time Markov jump linear systems. IEEE Transactions on Automatic Control 2010; 55(1):195-201. Wang J, Hovakimyan N, Cao C. Verifiable adaptive flight control: unmanned combat aerial vehicle and a...

show abstract

Effect of Loop Configuration on Steam Drum Level Control for a Multiple Drum Interconnected Loops Pressure Tube Type Boiling Water Reactor

Cited by 11 publications

References 51 publications

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

Importance of Three-Elements Boiler Drum Level Control and Its Installation in Power Plant

Adaptive control and signal processing literature survey (No. 17)

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