Search

Wood is undeniably the most useful and readily available natural raw material. However, the susceptibility of wood products to fire is one of the crucial challenges faced in the wood industry. The fire behaviour of wood is a very complex phenomenon due to the different constituents and their independent reactions to fire. This article presents a thorough overview of the flammability stages of wood. It covers pyrolysis, thermal oxidative decomposition, ignition, combustion and heat release as well as flame extinction mechanisms. In the area of flame retardancy, conventional wood fire retardants, nanocomposites fire retardants and wood modification processes are investigated. Factors such as wood species, moisture content, density, experimental conditions such as external heat flux, heat exposure time, wood permeability and porosity are some of the deterministic parameters characterising the fire behaviour. This paper is a one-stop-shop for researchers analysing wood flammability due to the inclusion of all aspects pertaining to the burning of wood. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The use of 3D printing technology for manufacturing construction materials is gaining popularity, however, only a few studies have reported the fire behavior of such parts. In this research, the fire properties of 3D printed polylactide acid (PLA) parts with varying infill densities along with the tensile properties were analysed. The results from the fire tests showed that increasing the infill density increased the fuel load, which sustained combustion. Hence, the peak heat release rate and total heat release increased with an increment in infill density percentage. It was also observed that the increasing infill density had no effect on the flammability rating of the parts due to the constant shell thickness used for all the parts. In addition, the tensile strength and ductility of the parts increased with density as a porous part is more susceptible to failure than a solid homogeneous part. • Higher infill density increases the amount of material in the part. • Increasing infill density increases fuel load and flammability. • Flammability rating of 3D printed parts depend on shell thickness. • Ductility of 3D printed parts increases with infill density. • Higher infill density improves tensile strength. [ABSTRACT FROM AUTHOR]

To study the practicability of a micro combustion calorimeter to analyze the calorimetry kinetics of wood, a micro combustion calorimeter with 13 heating rates from 0.1 to 5.5 K/s was used to perform the analysis of 10 kinds of common hardwood and softwood samples. As a microscale combustion measurement method, MCC (microscale combustion calorimetry) can be used to judge the flammability of materials. However, there are two methods for measuring MCC: Method A and Method B. However, there is no uniform standard for the application of combustible MCC methods. In this study, the two MCC standard measurement Methods A and B were employed to check their practicability. With Method A, the maximum specific heat release rate, heat release temperature, and specific heat release of the samples were obtained at different heating rates, while for Method B, the maximum specific combustion rate, combustion temperature and net calorific values of the samples were obtained at different heating rates. The ignition capacity and heat release capacity were then derived and evaluated for all the common hardwood and softwood samples. The results obtained by the two methods have significant differences in the shape of the specific heat release rate curves and the amplitude of the characteristic parameters, which lead to the differences of the derived parameters. A comparison of the specific heat release and the net calorific heat of combustion with the gross caloric values and heating values obtained by bomb calorimetry was also made. The results show that Method B has the potentiality to evaluate the amount of combustion heat release of materials. [ABSTRACT FROM AUTHOR]

The effect of pyrolysis reactors on the properties of biochars (with a focus on flammability and mechanical characteristics) were investigated by keeping factors such as feedstock, carbonisation temperature, heating rate and residence time constant. The reactors employed were hydrothermal, fixed-bed batch vertical and fixed-bed batch horizontal-tube reactors. The vertical and tube reactors, at the same temperature, produced biochars having comparable elemental carbon content, surface functionalities, thermal degradation pattern and peak heat release rates. The hydrothermal reactor, although, a low-temperature process, produced biochar with high fire resistance because the formed tarry volatiles sealed water inside the pores, which hindered combustion. However, the biochar from hydrothermal reactor had the lowest nanoindentation properties whereas the tube reactor-produced biochar at 300 °C had the highest nanoindentation-hardness (290 Megapascal) and modulus (ca. 4 Gigapascal) amongst the other tested samples. Based on the inherent flammability and mechanical properties of biochars, polymeric composites' properties can be predicted that can include them as constituents. [Display omitted] • Birch wood was carbonised in three different reactors with analogous reaction conditions. • Hydrothermal, vertical and horizontal tube reactors were used to produce the biochars. • Vertical and horizontal tube biochars had most properties identical to each other. • Hydrothermal biochar resisted combustion the most but had low mechanical property. • Different reactors had varied effect on the nanoindentation properties of biochars. [ABSTRACT FROM AUTHOR]

Polyethylene (PE) is one the most used plastics worldwide for a wide range of applications due to its good mechanical and chemical resistance, low density, cost efficiency, ease of processability, non-reactivity, low toxicity, good electric insulation, and good functionality. However, its high flammability and rapid flame spread pose dangers for certain applications. Therefore, different flame-retardant (FR) additives are incorporated into PE to increase its flame retardancy. In this review article, research papers from the past 10 years on the flame retardancy of PE systems are comprehensively reviewed and classified based on the additive sources. The FR additives are classified in well-known FR families, including phosphorous, melamine, nitrogen, inorganic hydroxides, boron, and silicon. The mechanism of fire retardance in each family is pinpointed. In addition to the efficiency of each FR in increasing the flame retardancy, its impact on the mechanical properties of the PE system is also discussed. Most of the FRs can decrease the heat release rate (HRR) of the PE products and simultaneously maintains the mechanical properties in appropriate ratios. Based on the literature, inorganic hydroxide seems to be used more in PE systems compared to other families. Finally, the role of nanotechnology for more efficient FR-PE systems is discussed and recommendations are given on implementing strategies that could help incorporate flame retardancy in the circular economy model. [ABSTRACT FROM AUTHOR]

Abstract The capability of artificial neural networks in predicting microscale combustion calorimeter (MCC) parameters of polymethyl methacrylate (PMMA) was carried out in this study. Using values of sample mass and corresponding heating rate, feed forward back propagation (FFBP) and generalized regression neural network (GRNN) models were developed to predict MCC parameters. On the whole, GRNN outperformed FFBP in predicting HRC data while FFBP model saw an improvement over GRNN when estimating pTime. It was also discovered that GRNN obtained better THR, pTemp and pHRR predictions during training but generated a relatively poor correlation when estimating the testing data. Sensitivity analysis on the ANN models revealed that heating rate had a more significant effect on the models’ outcome. Also, the ANN models observed the least error deviation when compared with HRC results for PMMA from structure-property models. Hence, ANN presents a reliable method for predicting flammability characteristics of PMMA from MCC test. [ABSTRACT FROM AUTHOR]

Although flame retardants are very effective in reducing the fire hazard of polymeric materials, their presence may be detrimental to mechanical strength. Hence, in order to have a holistic improvement of performance properties, a new approach has been developed wherein biochar is used to host a naturally-occurring flame retardant (lanosol). The issue of loss in mechanical strength of a polymer host is alleviated by the use of biochar. Three different doping procedures were investigated, namely, dry mixing, and chemical and thermal-based doping, to integrate lanosol into the biochar pores. The doped biochar was used to develop wheat gluten-based blends. The mechanical and flammability properties of the blends were assessed. It was found that thermal doping was the most effective in introducing significant amounts of lanosol particles inside the biochar pores. The bioplastic containing chemically, and thermally doped biochar had equal tensile strength (5.2 MPa), which was comparable to that of the unmodified material (5.4 MPa). The thermally doped biochar displayed the lowest cone calorimeter peak heat release rate (636 kW m−2) for combustion and the highest apparent activation energy (32.4 kJ mol−1) for decomposition. Thus, for flame retarding protein-based matrices, the use of additives thermally doped into biochar is recommended to both simultaneously improve fire-resistance and conserve mechanical strength. • Lanosol, a naturally occurring fire retardant, was doped into biochar pores using three methods. • The methods applied were dry mixing, chemical, and thermal doping. • The doped biochars were successfully used to fabricate wheat gluten-based biocomposites. • The most effective impregnation method was thermal doping. • Thermal doping was the best at preserving strength and lowering flammability. [ABSTRACT FROM AUTHOR]