Boiler dynamic control with optimized nitric oxides and efficiency

Ahmed, Rehan; Elshafei, Moustafa; Habib, Mohamed A.; Al-Zaharnah, I.

doi:10.1177/0959651817729079

Cited by 1 publication

(1 citation statement)

References 29 publications

(50 reference statements)

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“…54 On the contrary, the turbine system has small inertia and fast response. 55 If the boiler system and turbine system fail to work in coordination, the main stream pressure will fluctuate greatly, which is very disadvantageous for the stable and safe operation of the power plant. 56 Therefore, the main purpose of the coordinated control of power plants is to respond to the target load as quickly as possible, while ensuring the smooth running of the main stream pressure.…”

Section: Modeling and Problem Descriptionmentioning

confidence: 99%

Digital twin-based subspace model predictive control for thermal power plant

Zhao

Cai

Jiang

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a digital twin-based subspace model predictive control scheme is proposed for solving model mismatch caused by parameter perturbations of thermal power plants. First, the rational range of essential parameters in the nonlinear model is identified based on the nominal data of an actual 600 MW power plant, which provides necessary foundation for subsequent studies. Second, to improve the load-changing capacity under parameter disturbances, a novel digital twin-based subspace model predictive control algorithm is established by regarding the crucial parameter as extended input of predictive model. Finally, the small fluctuations during steady-state operation are effectively restrained through transforming weights and constraints slowly and smoothly at the end of load change. Simulation results illustrate that compared with the conventional subspace-based model predictive control, the proposed digital twin-based subspace model predictive control scheme can obtain a better performance in terms of both dynamic regulation rate and steady-state performance under parameter perturbations.

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Section: Modeling and Problem Descriptionmentioning

confidence: 99%