Citation

Jiang L, Mensah RA, Asante-Okyere S, Försth M, Xu Q, Ziggah YY, Restás, Berto F, Das O. Fire Mater. 2022; 46(5): 830-842.

Copyright

(Copyright © 2022, John Wiley and Sons)

DOI

10.1002/fam.3030

PMID

unavailable

Abstract

While there have been various attempts in establishing a model that can predict the flammability characteristics of polymers based on their molecular structure, artificial intelligence (AI) presents a promising alternative in tackling this pressing issue. Therefore, a novel approach of adopting AI methods, extreme learning machines (ELMs) and group method of data handling (GMDH) in estimating heat release capacity, total heat release and char yield from thermophysical properties of polymers were addressed. GMDH showed a clear indication of overfitting whereby the models generated excellent training results but could not provide similar performance during testing. The superior generalisation performance of ELM during testing makes it the standout method. ELM produced HRC predictions having R and RRMSE of 0.86 and 0.405 for training, 0.94 and 0.356 for testing. For THR estimates from ELM, the R and RRMSE scores were 0.9 and 0.195 for training, 0.93 and 0.131 for testing. While char yield ELM model generated 0.88 and 0.795 for training, 0.93 and 0.383 for testing. The potential of ELM was demonstrated as it estimated the flammability parameters of 105 polymers having little or no empirical test results.

Language: en

Keywords

ELM; flammability; GMDH; microscale combustion calorimeter; thermophysical properties