Abstract

In this study, water consumption predictions are made based on data obtained from two copper mineral concentration plants located in the northern region of Chile, Antofagasta Region, measured during a year of mining operation. The area to which this operation belongs is characterized by a desert climate with average annual rainfall of less than 50 mm and maximum and minimum temperatures of 29 degrees C and 4 degrees C for summer, and 26 degrees C and -5 degrees C for winter, measured during 2022. To perform the predictions, a database corresponding to two concentration plants with daily measurements for a year was used, which were analyzed using four regression models using Machine Learning (ML) in Python: Support Vector Regressor (SVR), Extreme Gradient Boost (XGBoost), Artificial Neural Network (ANN), and Random Forest Regressor (RF). The predictions obtained by each of the ML models were studied using cross-validation hyperparameter tuning and identifying the variable with the greatest impact. The model with the best prediction results was ANN, as it yielded the lowest relative error in the predictions.