Experimental and simulation study on the suppression of polyethylene dust explosion characteristics and flame propagation behavior by ABC, MCA, and MPP powder

MQ Su and SN Chen and LJ Wei and Y Duo and BY Jiang and IJ Li and DW Ding and XR Liang, ADVANCED POWDER TECHNOLOGY, 36, 105019 (2025).

DOI: 10.1016/j.apt.2025.105019

Preferred explosion suppression techniques are critical for explosion prevention and control of polyethylene (PE) dust. This study systematically investigated the suppression characteristics and mechanisms of three powder suppressants on the explosion of PE dust at the micro and macro levels: composite dry powder material composed of ammonium phosphate salt as the active base material, supplemented with hydrophobic agents and inert fillers (ABC), melamine cyanurate (MCA), and melamine polyphosphate (MPP). Through the 20 L spherical explosion system, the suppression law of the explosion suppressant on the PE dust explosion pressure and flame development was analyzed, found that with the increase of the mass fraction of the explosion suppressant, the explosion pressure and flame brightness showed a trend of increasing and then decreasing. The increase in explosion intensity is attributed to the participation of NH3 from the suppressants in the explosion reaction, ABC is the best suppression effect of three powder suppressants. Combined with SEM and XPS techniques, it was observed that the explosion suppressant not only hindered the decomposition and oxidation reaction of the polyethylene particles, but also generated a phosphate gangue and other solids wrapped in the surface of the product, which isolated the active groups and heat in the environment, weakening the explosive chain reaction. Finally, reaction tendency and migration of main products were clarified by reaction force field simulation, constructing a microscopic explosion suppression mechanism. (c) 2025 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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