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Lookup NU author(s): Dr Eni OkoORCiD, Dr Jie ZhangORCiD
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There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of models of the plant. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and ideally require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not connect well with sound theoretical basis. As a result, a data-driven approach based on neural networks is opted for in this study. Models based on this approach incorporates all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level).
Author(s): Oko E, Wang M, Zhang J
Publication type: Article
Publication status: Published
Journal: Fuel
Year: 2015
Volume: 151
Pages: 139-145
Print publication date: 01/07/2015
Online publication date: 07/02/2015
Acceptance date: 27/01/2015
ISSN (print): 0016-2361
ISSN (electronic): 1873-7153
Publisher: Elsevier
URL: http://dx.doi.org/10.1016/j.fuel.2015.01.091
DOI: 10.1016/j.fuel.2015.01.091
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