有色金属科学与工程2026，Vol.17Issue(2)：165-176,12.DOI:10.13264/j.cnki.ysjskx.2026.02.001
离子型稀土浸矿饱和-非饱和渗流PINN模型研究
Research on the saturated-unsaturated seepage PINN model for in-situ leaching of ionic rare earth ores
摘要
Abstract
The prediction of saturated-unsaturated seepage processes is crucial for the in-situ leaching of ionic rare earth ores.Currently,large-scale seepage prediction studies based on Physics-Informed Neural Networks(PINN)are relatively limited.This study constructed a long-term,large-scale neural network prediction model based on PINN for simulating in-situ leaching engineering.The model was applied to investigate the predictions of saturated-unsaturated seepage under different training sample sizes,soil types,and spatiotemporal scales.The results showed that when predicting column leaching experiments,the model achieved an R2 of 0.959 when validated with experimental data and an R2 of 0.98 across different data volumes and soil types in the training set.For a 10 m deep soil column,the model achieved an R2 of 0.90 using only 0.4%of the simulated data for training and increased to 0.98 when the training data volume was raised to 1%,while the maximum error in the wetting front time curve was reduced to 0.36%.This model offers a novel approach and theoretical framework for predicting seepage processes in the in-situ leaching of ionic rare earth ores.关键词
饱和-非饱和渗流/神经网络/PINN/数值模拟/原地浸矿Key words
saturated-unsaturated seepage/neural network/PINN/numerical simulation/in-situ leaching分类
数理科学引用本文复制引用
张永康,曾伟,王观石,罗煜君,胡世丽,陈国梁..离子型稀土浸矿饱和-非饱和渗流PINN模型研究[J].有色金属科学与工程,2026,17(2):165-176,12.基金项目
国家自然科学基金资助项目(52364015) （52364015）
福建省科技重大专项(2023YZ037002) （2023YZ037002）
江西理工大学河流源头水生态保护江西省重点实验室(2024HLYTSZ04) （2024HLYTSZ04）
