A combined NOx emission prediction model based on semi-empirical model and black box models

Shicheng Li, Suxia Ma and Fang Wang

Energy, 2023, vol. 264, issue C

Abstract: Coal fired power plants account for a large part of China's NOx emission. A precise prediction of the NOx emission can effectively improve the pollutant control level and operation ability. In this study, a novel combined model based on 1D semi-empirical model and three black box models is proposed to predicted the dynamics of NOx emission of a 350 MW circulating fluidized bed (CFB) boiler. In addition, an improved differential evolution algorithm based on non-negative constraint theory is used to determine the optimal weight coefficient of the combined model. Three different working condition datasets of the CFB boiler are acquired to evaluate the performance of combined model. The results of the experiments and discussions show that the combined model overcomes the limitation of the single model and achieves better prediction results.

Keywords: Combined model; NOx emission; 1D model; Artificial intelligence (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422203016X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:264:y:2023:i:c:s036054422203016x

DOI: 10.1016/j.energy.2022.126130

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().