Machine learning and numerical simulation research on specific energy consumption for gradated coarse particle two-phase flow in inclined pipes

In deep-sea mining engineering, accurately predicting the energy required per unit length of pipeline to transport a unit mass of solids (dimensionless specific energy consumption, DSEC) is crucial for ensuring energy conservation and efficiency in the project. Based on our previous work, we utilized the machine learning (ML) and the computational fluid dynamics (CFD)–discrete element method (DEM) method to study the transport characteristics and flow field variations of gradated coarse particles in inclined pipes (gradated particles refer to solid particles mixed in specific size and quantity ratios). First, we collect 1185 sets of data from 13 experimental literature, and after analyzing and processing them, an ensemble model based on four other ML models is developed. Both for pure substance particles (PS) and mixed particles (MP), the prediction accuracy of this ensemble model is relatively higher (PSs are spherical particles with uniform size and density, and MPs are particles with different shapes, sizes, and densities). Then, the CFD-DEM process and the operating conditions include low flow velocity with low volume concentration (2 m/s and 2.5%), low flow velocity with high volume concentration (2 m/s and 7.5%), and high flow velocity with low volume concentration (4 m/s and 2.5%). Under conditions of low flow velocity and low concentrations, as well as high flow velocity and low concentrations, the DSEC hardly changes with the variation of the pipe inclination angle. Under low flow velocity and high-concentration conditions, as the pipe gradually becomes vertical, the value of DSEC gradually increases.