Research on performance of passive heat supply tower based on the back propagation neural network

Yanli Song, Xin Chen, Jialong Zhou, Tao Du, Feng Xie and Haifeng Guo

Energy, 2022, vol. 250, issue C

Abstract: In this study, the performance parameters of passive heat supply tower (PHST) were analyzed. The optimal daily water-air ratio of PHST was obtained using non dominated sorting genetic algorithm (NSGA-II), BP neural network algorithm and decision score (DMS). The energy saving potential and energy efficiency ratio range of 48.8–50.8 were obtained for PHST operation with the air parameters corresponding to the optimal water-to-air ratio. The study shows that PHST has superior energy-saving characteristics when used to supplement heat for underground soil. The BP neural network model can accurately calculate and predict the variation of PHST performance parameters. Higher energy efficiency of PHST system can be obtained by effectively controlling the water-air ratio. This study provides a new scheme for the operation of the supplementary heat system and a new idea for the efficient operation of the heat exchange equipment.

Keywords: Passive heat supply tower; Back propagation neural network; Performance parameters; NSGA-Ⅱ; Multi-objective optimization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422200665X
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:250:y:2022:i:c:s036054422200665x

DOI: 10.1016/j.energy.2022.123762

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 ().