Understanding the Influence of Gypsum upon a Hybrid Flame Retardant Coating on Expanded Polystyrene Beads

Abstract

A low-cost and effective flame retarding expanded polystyrene (EPS) foam was prepared herein by using a hybrid flame retardant (HFR) system, and the influence of gypsum was studied. The surface morphology and flame retardant properties of the synthesized flame retardant EPS were characterized using scanning electron microscopy (SEM) and cone calorimetry testing (CCT). The SEM micrographs revealed the uniform coating of the gypsum-based HFR on the EPS microspheres. The CCT and thermal conductivity study demonstrated that the incorporation of gypsum greatly decreases the peak heat release rate (PHRR) and total heat release (THR) of the flame retarding EPS samples with acceptable thermal insulation performance. The EPS/HFR with a uniform coating and the optimum amount of gypsum provides excellent flame retardant performance, with a THR of 8 MJ/m2, a PHRR of 53.1 kW/m2, and a fire growth rate (FIGRA) of 1682.95 W/m2s. However, an excessive amount of gypsum weakens the flame retardant performance. The CCT results demonstrate that a moderate gypsum content in the EPS/HFR sample provides appropriate flame retarding properties to meet the fire safety standards.

Improvement of Heat Release Rate Measurement from Woods Based on Their Combustion Products Temperature Rise

“…Subsequently, the 2-tuples of combustion products' molar heat capacity (in kJ mol −1 K −1 ) and combustion products' chemical amount flow rate (mol s −1 ) were predicted by the trained neural network. Predicted data were used together with combustion products' temperature rise to HRR estimation by Equation (6). Value calculated by the Equation ( 6) was subsequently multiplied by 113.122 constant.…”

“…In the second method, the HRR was calculated from average values of combustion products' chemical amount flow rate and molar heat capacity and combustion products' temperature rise according to Equation (6). The results obtained by the second method are further denoted as nct_Model.…”

“…A detailed procedure for calculating FIGRA by one widely accepted method is given, e.g., EN 13823:2020 + A1:2022 [5]. A more detailed overview of MARHE and FIGRA parameters and their practical applications can be found, e.g., in [6][7][8][9]. The method of estimating other fire characteristics from the HRR is described in the following scientific works [10] (prediction of reaction to fire class), [11,12] (flashover category prediction), [13,14] (flame spread rate prediction) and [15,16] (prediction of combustion products, mainly carbon monoxide yield from released heat).…”

“…This study aims to design and train the neural network for high-accuracy prediction of the chemical amount flow rate and molar heat capacity of wood combustion products in the cone calorimeter exhaust duct from combustion products temperature rise and time after ignition. Data obtained by the prediction allow HRR calculation according to Equation (6). The second aim is to compute the average values of the chemical amount flow rate and molar heat capacity of the combustion product and subsequently compare the accuracy of HRR computed by Equation (6) from data predicted by the trained neural network and from these average values.…”