Prediction of pressure drop in solid-liquid two-phase pipe flow for deep-sea mining based on machine learning

In deep -sea mining, the accurate and rapid prediction of the pressure drop in a solid-liquid two-phase pipe flow (SLPF) with different parameters including particles, pipes, and flow fields, remains an issue yet to be fully resolved. In this study, an extensive investigation of the pressure drop in a slpf is conducted using machine-learning techniques. By collecting 1290 sets of data from 13 experimental papers and performing analysis and processing, we obtain a machine-learning ensemble algorithm capable of accurately predicting the pipe -pressure drop based on random forest (RF), back propagation (BP), and polynomial regression (PR) algorithms. The performance of the ensemble algorithm surpasses that of the other three algorithms, whether applied to pure substance (PS) particles or mixed particles (MP) containing PS and equivalent particles. For PS particles, the particle concentration and particle diameter -to -pipe diameter (PTP) account for the second and third weights influencing the pressure drop. Using the computational fluid dynamics (CFD)-discrete element method (DEM), this can be attributed to the significant kinetic energy loss caused by the collisions and friction between the particles and pipe wall and the excessive gravity of the particles, which influences the pressure drop.