Asphalt foaming quality control model using neural network and parameters optimization

Industrial asphalt foaming is a process of mixing, transferring heat and phase transition of multi-phase multi-component flow in the expansion chamber, including a series of complex problems such as difficult analysis and observation. In the context of this research, asphalt foaming quality control model using neural network was constructed to map the pertinent parameters (asphalt temperature, water content and air pressure) into the foaming properties (maximum expansion ratio and half-life time). In addition, Particle swarm optimization was adopted to avoid the local infinitesimal defect and slow constringency in the traditional BP algorithm. The prediction error of Shell 60/70 quality control model indicates that the asphalt foaming quality control model using PSO-BP neural network is effective, and this provides a novel idea for studying of the foam asphalt, which has practical significance for the design of asphalt foam equipment. Finally, a parameter optimization model with the maximization of the foaming index was proposed based on the quality control model to improve the foaming performance of the asphalt foaming equipment. With the analysis of a parameter optimization model of Shell 60/70 asphalt, the better foaming properties are the maximum expansion ratio of 12.28 times and half-life time of 11.02 s at the foaming condition of asphalt temperature of about 168 °C, the water content of about 1.5 wt% and the air pressure of about 1 bar. The method proposed in this paper is of important reference significance for the engineering with difficult analysis and observation.