Machine learning applications in minerals processing: A review
JT McCoy, L Auret - Minerals Engineering, 2019 - Elsevier
Abstract Machine learning and artificial intelligence techniques have an ever-increasing
presence and impact on a wide-variety of research and commercial fields. Disappointed by …
presence and impact on a wide-variety of research and commercial fields. Disappointed by …
[HTML][HTML] Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by XGBoost and SHAP-A “conscious-lab” development
Surprisingly, no investigation has been explored relationships between operating variables
and metallurgical responses of coal column flotation (CF) circuits based on industrial …
and metallurgical responses of coal column flotation (CF) circuits based on industrial …
A comparison of random forest and support vector machine approaches to predict coal spontaneous combustion in gob
C Lei, J Deng, K Cao, Y ** fuel resources is strategically crucial for Armenia. Far more than any other fossil
fuel resource, coal roughly generates half the nation's electricity. Although coal could play a …
fuel resource, coal roughly generates half the nation's electricity. Although coal could play a …
Prediction of froth flotation responses based on various conditioning parameters by Random Forest method
Flotation procedure is a combination of many sub-processes which make its modeling quite
complicated. Therefore, it is essential to use a method that can identify the most explanatory …
complicated. Therefore, it is essential to use a method that can identify the most explanatory …