Research on the saturated-unsaturated seepage PINN model for in-situ leaching of ionic rare earth ores AITranslate

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Chinese Library Classification Number:TD865[矿业工程—金属矿开采]

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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 saturatedunsaturated 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.