Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366390768> ?p ?o ?g. }
- W4366390768 endingPage "3220" @default.
- W4366390768 startingPage "3220" @default.
- W4366390768 abstract "Considering the continuous increase in production costs and resource optimization, more than a strategic objective has become imperative in the copper mining industry. In the search to improve the efficiency in the use of resources, the present work develops models of a semi-autogenous grinding (SAG) mill using statistical analysis and machine learning (ML) techniques (regression, decision trees, and artificial neural networks). The hypotheses studied aim to improve the process's productive indicators, such as production and energy consumption. The simulation of the digital model captures an increase in production of 4.42% as a function of mineral fragmentation, while there is potential to increase production by decreasing the mill rotational speed, which has a decrease in energy consumption of 7.62% for all linear age configurations. Considering the performance of machine learning in the adjustment of complex models such as SAG grinding, the application of these tools in the mineral processing industry has the potential to increase the efficiency of these processes, either by improving production indicators or by saving energy consumption. Finally, the incorporation of these techniques in the aggregate management of processes such as the Mine to Mill paradigm, or the development of models that consider the uncertainty of the explanatory variables, could further increase the performance of productive indicators at the industrial scale." @default.
- W4366390768 created "2023-04-21" @default.
- W4366390768 creator A5011300543 @default.
- W4366390768 creator A5013931980 @default.
- W4366390768 creator A5065826856 @default.
- W4366390768 creator A5071261950 @default.
- W4366390768 creator A5086606901 @default.
- W4366390768 date "2023-04-19" @default.
- W4366390768 modified "2023-09-30" @default.
- W4366390768 title "Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry" @default.
- W4366390768 cites W1678356000 @default.
- W4366390768 cites W1702637483 @default.
- W4366390768 cites W1966649904 @default.
- W4366390768 cites W1967728428 @default.
- W4366390768 cites W1970361289 @default.
- W4366390768 cites W1971382571 @default.
- W4366390768 cites W1975047082 @default.
- W4366390768 cites W1983865151 @default.
- W4366390768 cites W1986321290 @default.
- W4366390768 cites W1987812160 @default.
- W4366390768 cites W1990361609 @default.
- W4366390768 cites W2004814624 @default.
- W4366390768 cites W2013306424 @default.
- W4366390768 cites W2017587044 @default.
- W4366390768 cites W2027192976 @default.
- W4366390768 cites W2031346917 @default.
- W4366390768 cites W2037621026 @default.
- W4366390768 cites W2049019723 @default.
- W4366390768 cites W2050029530 @default.
- W4366390768 cites W2066815144 @default.
- W4366390768 cites W2070230130 @default.
- W4366390768 cites W2079477317 @default.
- W4366390768 cites W2084070973 @default.
- W4366390768 cites W2088794999 @default.
- W4366390768 cites W2126553322 @default.
- W4366390768 cites W2149591630 @default.
- W4366390768 cites W2182975999 @default.
- W4366390768 cites W2327035729 @default.
- W4366390768 cites W2341531390 @default.
- W4366390768 cites W2581853708 @default.
- W4366390768 cites W2590751138 @default.
- W4366390768 cites W2604572079 @default.
- W4366390768 cites W2757109865 @default.
- W4366390768 cites W2759373267 @default.
- W4366390768 cites W2764120290 @default.
- W4366390768 cites W2780265514 @default.
- W4366390768 cites W2789980901 @default.
- W4366390768 cites W2806128998 @default.
- W4366390768 cites W2808919076 @default.
- W4366390768 cites W2886003457 @default.
- W4366390768 cites W2891262926 @default.
- W4366390768 cites W2892104002 @default.
- W4366390768 cites W2892722275 @default.
- W4366390768 cites W2900503808 @default.
- W4366390768 cites W2903880955 @default.
- W4366390768 cites W2911964244 @default.
- W4366390768 cites W2912718868 @default.
- W4366390768 cites W2912773338 @default.
- W4366390768 cites W2913018992 @default.
- W4366390768 cites W2913417634 @default.
- W4366390768 cites W2956699646 @default.
- W4366390768 cites W2983005228 @default.
- W4366390768 cites W2995872689 @default.
- W4366390768 cites W2995885765 @default.
- W4366390768 cites W3000401156 @default.
- W4366390768 cites W3038333071 @default.
- W4366390768 cites W3040327251 @default.
- W4366390768 cites W3047163641 @default.
- W4366390768 cites W3055409566 @default.
- W4366390768 cites W3102476541 @default.
- W4366390768 cites W3104146391 @default.
- W4366390768 cites W3119831359 @default.
- W4366390768 cites W3120644609 @default.
- W4366390768 cites W3133743901 @default.
- W4366390768 cites W3157283362 @default.
- W4366390768 cites W3165924257 @default.
- W4366390768 cites W3175506625 @default.
- W4366390768 cites W3177098662 @default.
- W4366390768 cites W3178516358 @default.
- W4366390768 cites W3182416595 @default.
- W4366390768 cites W3196143620 @default.
- W4366390768 cites W3198211135 @default.
- W4366390768 cites W3199561606 @default.
- W4366390768 cites W3201482179 @default.
- W4366390768 cites W3204791550 @default.
- W4366390768 cites W3205415239 @default.
- W4366390768 cites W3206854390 @default.
- W4366390768 cites W3207586670 @default.
- W4366390768 cites W34105695 @default.
- W4366390768 cites W4212883601 @default.
- W4366390768 cites W4220738705 @default.
- W4366390768 cites W4225930207 @default.
- W4366390768 cites W4230542890 @default.
- W4366390768 cites W4239354461 @default.
- W4366390768 cites W4242607850 @default.
- W4366390768 cites W4281746745 @default.
- W4366390768 cites W4282945662 @default.
- W4366390768 cites W4284988848 @default.