Abstract:In the process of in-situ leaching of uranium, it is of great significance to accurately predict the amount of uranium metal extracted. Multiple machine learning methods such as multiple linear regression, Multi-Layer Perceptron (MLP) and Random forest (RF) were used to build prediction models. The results indicate that: 1) Compared with the traditional multiple linear regression algorithms, MLP and RF methods can obtain models with better prediction performance. 2) The MLP model has the best performance in predicting the change of uranium leaching metal content (R2=0.91). 3) With the same prediction accuracy, the RF model takes less time than that of the MLP model, and the hyperparameters setting is simpler. 4) The weight ratio of the total flow rate to the amount of uranium metal leached is 81.6% when the total flow rate and the amount of uranium leaching metal content per square meter are taken as the key factors.