Pipeline hydraulic conveying is a common mode of transportation in industry, which has the advantages of long transportation distance, high safety, low operation and maintenance costs, environmental friendliness and flexible layout. At present, there are many researches on the flow characteristics of two-phase flow in horizontal, inclined and vertical pipelines in the pipeline system, while there are few researches on the bend in the system. It is urgent to clarify the two-phase flow mechanism of this section, identify the erosion mechanism of this section, and make an accurate prediction. In this study, firstly, the CFD-DEM coupling method under the Euler-Lagrange framework was used to investigate the influence of five factors including bending angle, bending radius, input velocity, particle diameter and particle concentration, on the pressure drop and erosion rate of bend. Based on the above five variables, the Pairwise method was used to carry out the combination of cases and numerical simulation calculation was done, and hundreds of valid data were obtained. Based on this data set, six machine learning models were developed for training, the accuracy of each model was compared, and the relative importance of each feature for the prediction results was obtained. The results show that the pressure drop increases with the increase of input velocity, particle concentration, particle diameter and bending angle, and has little relationship with the bending radius; The erosion rate increases with the increase of input velocity, particle concentration, particle diameter and bending radius. With the increase of bending angle, it decreases slightly before 90° and increases after 90°. The prediction accuracy evalution index R2
(the closer to 1, the more accurate) of the best machine learning model for pressure drop and erosion rate are about 0.96 and 0.99, respectively, which have good prediction ability, and can be used to predict the pressure drop and erosion rate of solid-liquid two-phase flow in bend under the influence of multiple parameters. It is found that the input velocity and particle concentration are the most influential factors on the pressure drop and erosion rate prediction, respectively.