Multi-layer perception based model predictive control for the thermal power of nuclear superheated-steam supply systems

Dong, Zhe; Zhang, Zuoyi; Huang, Xiaojin

doi:10.1016/j.energy.2018.03.046

Cited by 45 publications

(18 citation statements)

References 37 publications

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“…In order to assess performance from the perspective of quan-tication, two widely used evaluation metrics are utilized in our experiments to measure prediction accuracy: mean absolute error (MAE), 44 root mean square error (RMSE). 45 Their expressions are given as:…”

Section: Experimental Settingsmentioning

confidence: 99%

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Guo

Wang

et al. 2020

RSC Adv.

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Section: Experimental Settingsmentioning

confidence: 99%

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Guo

Wang

et al. 2020

RSC Adv.

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“…The thermal power plant boiler is a complex object with nonlinear coupling, large lag, multivariable, and multi-interference. At present, superheated steam temperature control systems of most domestic thermal power plant boiler still use PID controllers, which has many shortcomings in control quality [1][2][3][4][5][6][7][8][9][10][11][12][13], such as the lack of timely adjustment and excessive overshoot. Nowadays, many scholars have proposed many advanced control algorithm strategies for boiler superheated steam temperature control systems.…”

Section: Introductionmentioning

confidence: 99%

“…It is often impossible to obtain an effective filtering effect by a single controller, and it is difficult to obtain a better accuracy effect. In addition, the diversified generation method under the actual working conditions determines that the single control algorithm is not ideal for eliminating the disturbance effect [8,[14][15][16][17][18]. Then the researchers proposed a number of integrated control algorithms to improve the accuracy of the system.…”

Section: Introductionmentioning

confidence: 99%

Undisturbed Switching Control Method of Superheated Steam Temperature Systems

2019

Complexity

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In this paper, a new type of superheated steam temperature switching control system for thermal power plants is presented. A single neuron adaptive PSD (Proportional Sum Differential) predictive controller is designed. The DCS (Distributed Control System) control system platform is used for configuration design. At the same time, the feedforward compensation technology and anti-integration saturation technology are employed to improve the characteristics of large hysteresis and multi-interference in the superheated steam temperature system. Undisturbed switching performance can be well obtained between the new controller and its own PID controller. This proposed method has been well applied in a thermal power plant (600MW) and achieved better control quality.

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“…The power-level control of MHTGR-based NSSS, which regulates the neutron flux, temperatures of both helium and steam by changing the control rod speed, rotation-rate of feed water pump and helium blowers, is a critical technique for the efficient and safe operation of NPPs. Currently, in China, stimulated by the need in the carbon emission reduction, local power demand and heat sources for industrial complexes, control performance optimization and guaranteed inherent stability are two promising directions of MHTGR-based NSSS power control [3].…”

Section: Introductionmentioning

confidence: 99%

“…Energies 2018, 11, x FOR PEER REVIEW 2 of 14 demand and heat sources for industrial complexes, control performance optimization and guaranteed inherent stability are two promising directions of MHTGR-based NSSS power control [3]. During load-following operation of NPPs, the severe nonlinearity and model uncertainty of NSSS dynamics have resulted in stability issue to NSSS power control engineers.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Matrix Control for the Thermal Power of MHTGR-Based Nuclear Steam Supply System

et al. 2018

Self Cite

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The modular high temperature gas-cooled reactor (MHTGR) based nuclear steam supplying system (NSSS) is constituted by an MHTGR, a once-through steam generator (OTSG) and can generate superheated steam for industrial heat or electric power generation. The wide range closed-loop stability is achieved by the recently proposed coordinated control law, in which the neutron flux and the temperatures of both main steam and primary coolant are chosen as controlled variables, and the flowrates of both primary and secondary loop and the control rod speed are chosen as manipulated variables. However, the thermal power is only controlled in open loop manner and hence could be further optimized through feedback. Motivated by this, a dynamic matrix control (DMC) is proposed for optimizing the thermal power of MHTGR based NSSS. A simple step-response model with the thermal power response data is utilized in designing the DMC. The design objective of DMC is to optimize the deviation of the thermal power from its reference under its rate constraint. Then, by the virtue of strong stability of existing control law and optimization ability of DMC, a cascade control structure is implemented for the thermal power optimization, with the coordinated control law in the inner loop and DMC in the outer loop. Numerical simulation results show the satisfactory improvement of thermal power response. This cascade control structure inherits the advantages of both proportional-integral-differential (PID) control and DMC, by which the zeros offset and the short settling time of thermal power are realized.

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Multi-layer perception based model predictive control for the thermal power of nuclear superheated-steam supply systems

Cited by 45 publications

References 37 publications

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Undisturbed Switching Control Method of Superheated Steam Temperature Systems

Dynamic Matrix Control for the Thermal Power of MHTGR-Based Nuclear Steam Supply System

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