Prediction of energy mass loss rate for biodiesel fire via machine learning and its physical modeling of flame radiation evolution

Lei Deng, Congling Shi, Haoran Li, Mei Wan, Fei Ren, Yanan Hou and Fei Tang

Energy, 2023, vol. 275, issue C

Abstract: Biodiesel is an emblematic energy of green power and developing vigorously, which is conducive to an important strategic significance covering the sustainable development of the global economy, the promotion of energy substitution, the reduction of environmental pressure, and the control of atmospheric contamination. In order to study the biodiesel energy burning characteristics, a series of experiments were carried out in an ISO9705 full-scale room, and the effects of pool size on the burning rate, flame height, flame oscillation frequency, and flame radiation fraction of biodiesel energy fire were systematically analyzed. This study uses genetic algorithm-back propagation neural network (GA-BPNN) algorithms for real-time prediction of transient fire mass loss rates. Three parameters (pool size, liquid depth, and burning time) are paired with the fuel mass loss rate and trained using a GA-BPNN algorithms model. The results show that the GA-BPNN algorithms predictions have a good correlation with the validated experimental values and the relative is less than 15%. Furthermore, the ratio of intermittent flame height and continuous flame height to their mean value is calculated respectively at about 1.72 and 0.58. The flame oscillation frequency decreases following the increase in oil pool size, the correlation can be calculated by f=0.3(D/g′)1/2. Finally, a new correlation χrad=0.2Q˙*−2/3 is proposed to predict the flame radiation fraction by increasing the flame viewing factor coefficient. The proposed correlation can be used to describe its evolution under different oil pool sizes, which can be essential to estimate the flame radiation impact on surroundings for such biodiesel energy fires.

Keywords: Biodiesel pool fire; Mass loss rate; Oscillation frequency; Radiation fraction; GA-BPNN algorithm; Machine learning (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422300782X
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:275:y:2023:i:c:s036054422300782x

DOI: 10.1016/j.energy.2023.127388

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 ().