Analysis on the Fire Growth Rate Index Considering of Scale Factor, Volume Fraction, and Ignition Heat Source for Polyethylene Foam Pipe Insulation

Recycled polyester filled with eggshells waste-based nano CaCO3: thermo-mechanical and flame-retardant features

Improved environmental-friendly fire-retardant nanocomposite.

Use of synthetic wollastonite nanofibers in enhancing mechanical, thermal, and flammability properties of polyoxymethylene nanocomposites

This study investigates the mechanical, thermal, and flammability properties of synthetic wollastonite nanofibers (SWN) reinforced polyoxymethylene (POM) nanocomposites. SWN has been added into the POM nanocomposites in the range of 0.5–3 phr via melt blending. The mechanical properties were investigated through tensile and impact tests with scanning electron microscopy and energy dispersive X‐ray analysis. The thermal characterization was performed by thermogravimetry analysis and differential scanning calorimetry. Flame retardancy of nanocomposites was studied through cone calorimetry analysis and limiting oxygen index test. The tensile strength of nanocomposites improved by 5.88% at 1 phr SWN content, whereas Young's modulus increased with increasing content. The thermal stability of nanocomposites was enhanced as indicated by the higher initial degradation temperature, which rose about 22°C at 1 phr SWN content. The POM/SWN nanocomposites exhibited better mechanical strength despite their lower crystallinity due to the substantial reinforcing effect of SWN. The flame retardancy of nanocomposites improved, as indicated by the reduction of peak heat release rate from the cone calorimetry test. This study shows that SWN has simultaneously enhanced the mechanical strength, thermal stability, and flame retardancy of POM nanocomposites and has the potential in automotive applications.

Experimental Assessment of the Thermal Conductivity of Basalt Fibres at High Temperatures

This paper investigates fibrous thermal insulation materials of various densities to assess the change in their thermophysical properties at high temperatures. The thermal conductivity of fibrous thermal insulation materials is discussed as a function of the temperature in the range from 50 °C to 500 °C. It is shown that the thermal insulating properties depend not only on the physical properties of the material (e.g., density or diameter of fibres), but also on the geometric parameters of the structure and on the orientation of the fibres. The influence of high temperatures on the mass change of fibrous materials associated with the burnout of synthetic binders is shown. These features should be taken into account during the design of thermal insulation operating at high temperatures to provide the optimal selection of the material and to guarantee the stability of their thermal properties.

Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment

Mountains are popular tourist destinations due to their climate, fresh atmosphere, breathtaking sceneries, and varied topography. However, they are at times exposed to accidents, such as fire caused due to natural hazards and human activities. Such unforeseen fire accidents have a social, economic, and environmental impact on mountain towns worldwide. Protecting mountains from such fire accidents is also very challenging in terms of the high cost of fire containment resources, tracking fire spread, and evacuating the people at risk. This paper aims to fill this gap and proposes a three-fold methodology for fire safety in the mountains. The first part of the methodology is an optimization model for effective fire containment resource utilization. The second part of the methodology is a novel ensemble model based on machine learning, the heuristic approach, and principal component regression for predictive analytics of fire spread data. The final part of the methodology consists of an Internet of Things-based task orchestration approach to notify fire safety information to safety authorities. The proposed three-fold fire safety approach provides in-time information to safety authorities for making on-time decisions to minimize the damage caused by mountain fire with minimum containment cost. The performance of optimization models is evaluated in terms of execution time and cost. The particle swarm optimization-based model performs better in terms of cost, whereas the bat algorithm performs better in terms of execution time. The prediction models’ performance is evaluated in terms of root mean square error, mean absolute error, and mean absolute percentage error. The proposed ensemble-based prediction model accuracy for fire spread and burned area prediction is higher than that of the state-of-the-art algorithms. It is evident from the results that the proposed fire safety mechanism is a step towards efficient mountain fire safety management.