Copper Ore Grade Prediction using Machine Learning Techniques in a Copper Deposit
- سال انتشار: 1403
- محل انتشار: مجله معدن و محیط زیست، دوره: 15، شماره: 3
- کد COI اختصاصی: JR_JMAE-15-3_012
- زبان مقاله: انگلیسی
- تعداد مشاهده: 163
نویسندگان
Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru
Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru
Department of Chemical Engineering, Faculty of Engineering, National University of the Altiplano of Puno, Puno, Perú
Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru
Departamento de Ingeniería Metalurgica, Universidad Nacional de Trujillo, Trujillo, Perú
Department of Metallurgical Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Perú
Department of Metallurgical Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Perú
چکیده
The objective of this research work to employ machine learning techniques including Multilayer Perceptron Artificial Neural Networks (ANN-MLP), Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR) to predict copper ore grades in a copper deposit located in Peru. The models were developed using ۵۶۵۴ composites containing available geological information (rock type), as well as the locations of the samples (east, north, and altitude) and secondary ore grade (Mo) obtained from drilling wells. The data was divided into ۱۰% (۵۶۵ composites) for testing, ۱۰% (۵۶۵ composites) for validation, and ۸۰% (۴۵۲۳ composites) for training. The evaluation metrics included SSE (Sum of Squared Errors), RMSE (Root Mean Squared Error), NMSE (Normalized Mean Squared Error), and R&sup۲; (Coefficient of Determination). The XGBoost model could predict the ore grade with an SSE of ۱۵.۶۷, RMSE = ۰.۱۷, NMSE = ۰.۳۴, and R&sup۲; = ۰.۶۶, the RFs model with an SSE of ۱۶.۴۰, RMSE = ۰.۱۷, NMSE = ۰.۳۶, and R&sup۲; = ۰.۶۵, the SVR model with an SSE of ۱۹.۹۴, RMSE = ۰.۱۹, NMSE = ۰.۴۳, and R&sup۲; = ۰.۵۷, and the ANN-MLP model with an SSE = ۲۱.۰۰, RMSE = ۰.۱۹, NMSE = ۰.۴۶, and R&sup۲; = ۰.۵۵. In conclusion, the XGBoost model was the most effective in predicting copper ore grades.کلیدواژه ها
Multilayer Perceptron Artificial Neural Network, Random Forests, Extreme Gradient Boosting, Support Vector Regressionاطلاعات بیشتر در مورد COI
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