Utilising genetic algorithm to optimise pyrolysis kinetics for fire modelling and characterisation of chitosan/graphene oxide polyurethane composites

• Published in: Composites. Part B, Engineering Vol. 182; p. 107619
• Main Authors: Yuen, A.C.Y., Chen, T.B.Y., Wang, C., Wei, W., Kabir, I., Vargas, J.B., Chan, Q.N., Kook, S., Yeoh, G.H.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2020
• Subjects: Combustion; Cone calorimeter; Flame retardant; Large eddy simulation; Layer-by-layer; Pyrolysis; Flame retardant; Cone calorimeter; Pyrolysis; Combustion; Large eddy simulation; Layer-by-layer
• ISSN: 1359-8368; 1879-1069
• DOI: 10.1016/j.compositesb.2019.107619

A fire assessment model has been developed to provide a better understanding of the flame propagation, toxic gases and smoke generations of polymer composites. In this study, the effectiveness of the Chitosan/Graphene Oxide layer-by-layer fire retardant coating on flexible polyurethane foam was investigated experimentally and numerically via Cone Calorimetry. To generate quality pyrolysis kinetics to enhance the accuracy of the model, a systematic framework to extract TGA data is proposed involving the Kissinger–Akahira–Sunose method followed by Genetic Algorithm, with less than 5% of RMS error against experimental data. The proposed fire model is capable of predicting and visualising fire development and emitting gas volatiles. [Display omitted] •Porous media surface regression pyrolysis model incorporating detailed chemistry.•Advanced numerical genetic searching algorithm for pyrolysis kinetics extraction.•Layer-by-layer assembly approach for fabrication of FPU/GO/CHT composites.•Visualisation and prediction of flammability, toxicity and smoke reductions.