Data-driven modeling-based digital twin of supercritical coal-fired boiler for metal temperature anomaly detection

Zhipeng Cui, Jing Xu, Wenhao Liu, Guanjia Zhao and Suxia Ma

Energy, 2023, vol. 278, issue PA

Abstract: The flexible operation of coal-fired boilers deteriorates the heat transfer capacity of tubes, leading to localized overheating, tube cracking, and even boiler failure. This study presents a digital twin (DT) for detecting the metal temperature anomaly of a coal-fired boiler by predicting the metal temperature time series using data-driven methods. A data-based model adopting an extreme gradient boosting algorithm tuned by a particle swarm optimization algorithm is proposed to predict the metal temperature with an output sequence of 6 min. The predicted metal temperature is then transformed into an interval based on statistical distribution and confidence level to improve the robustness of the model. Moreover, a histogram is used to determine the benchmark of metal temperature anomaly with respect to varying operating conditions. The proposed DT is applied to a 600-MW on-duty supercritical coal-fired boiler. Results indicate that the proposed DT can accurately predict metal temperature and is useful for detecting metal temperature anomalies in advance.

Keywords: Coal-fired boiler; Metal temperatures prediction; Data driven; Flexible operation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223013531
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:278:y:2023:i:pa:s0360544223013531

DOI: 10.1016/j.energy.2023.127959

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 ().