Combustion parameter prediction for mining conveyor belts by using convolutional neural network–long short-term memory

Abstract

The combustion characteristic parameters of mining conveyor belts represent a crucial index for measuring the fire performance and hazard posed by combustible materials. An accurate prediction of its value provides important guidance on preventing conveyor belt fires. The critical parameters of a flame–retardant polyvinyl chloride gum elastic conveyor belt were measured under different radiative heat fluxes, including mass loss rate, heat release rate, effective heat of combustion and gas production rates for CO and CO₂. The prediction method for the combustion characteristics of conveyor belts was proposed by combining a convolutional neural network with long short-term memory. Results indicated that the peak values of the mass loss, heat release, smoke production and gas production rates of CO and CO₂ were positively correlated with radiative heat flux, whilst the time required to reach the peak value was negatively correlated with it. The peak time of the effective heat of combustion occurred earlier. Through deep learning modelling, mean absolute error, root mean square error and coefficient of determination were determined as 2.09, 3.45 and 9.93 × 10⁻¹, respectively. Compared with convolutional neural network, long short-term memory and multilayer perceptron, mean absolute error decreased by 26.92%, 24.82% and 25.09%, root mean square error declined by 27.82%, 29.59% and 29.59% and coefficient of determination increased by 0.05 × 10⁻¹, 0.06 × 10⁻¹ and 0.06 × 10⁻¹, respectively. The findings provide a quantitative reference benchmark for the development of conveyor belt fires and offer new technical support for the construction of early warning systems for conveyor belt fires in coal mines.