Comparative evaluation of the predictability of neural network methods on the flammability characteristics of extruded polystyrene from microscale combustion calorimetry

Predictions of both combustible material flammability and heat release parameters have been long goals in fire safety research, for its complex heat, mass transfer and chemical reaction process in gas phase. In this study, neural network method is employed to predict materials flammability consideri...

Full description

Saved in:
Bibliographic Details
Published in:Journal of thermal analysis and calorimetry 2019-12, Vol.138 (5), p.3055-3064
Main Authors: Mensah, Rhoda Afriyie, Jiang, Lin, Asante-Okyere, Solomon, Xu, Qiang, Jin, Cong
Format: Article
Language:English
Subjects:
Analytical Chemistry
Back propagation networks
Calorimetry
Chemical reactions
Chemistry
Chemistry and Materials Science
Combustion
Comparative analysis
Deviation
Extrusion
Fire prevention
Fire protection
Fire safety
Flammability
Group method of data handling
Heat measurement
Heat of combustion
Inorganic Chemistry
Mass transfer
Measurement Science and Instrumentation
Methods
Neural networks
Organic chemistry
Physical Chemistry
Polymer Sciences
Polystyrene resins
Prediction models
Vapor phases
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Predictions of both combustible material flammability and heat release parameters have been long goals in fire safety research, for its complex heat, mass transfer and chemical reaction process in gas phase. In this study, neural network method is employed to predict materials flammability considering its wide application in predicting key properties of engineering problems. The use of group method of data handling (GMDH) and feed forward back-propagation (FFBP) neural networks in predicting the heat of combustion and heat release capacity (HRC) from microscale combustion calorimetry has been examined. The study presented models with excellent predictability though GMDH out-performed FFBP. The deviation of the predicted and measured HRC data from this study was compared with the results of other predictive modelling techniques used in flammability studies. The GMDH neural network results presented the least mean deviation of 4.01 signifying accurate predictions. Hence, this study proposed the use of GMDH in predicting flammability characteristics of materials.
ISSN:1388-6150
1588-2926
DOI:10.1007/s10973-019-08335-0