Utilising genetic algorithm to optimise pyrolysis kinetics for fire modelling and characterisation of chitosan/graphene oxide polyurethane composites. (1st February 2020)

• Record Type: Journal Article
• 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.
• Is Part Of: Composites. Number 182(2020)
• Publication Date: 2020-02-01
• Subjects: Large eddy simulation -- Layer-by-layer -- Flame retardant -- Pyrolysis -- Combustion -- Cone calorimeter; Composite materials -- Periodicals; Materials science -- Periodicals; Composite materials; Periodicals; Electronic journals; 620.118
• DOI: 10.1016/j.compositesb.2019.107619
• Languages: English
• ISSNs: 1359-8368

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: 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.