Bibliographic citations
Anticona, J., (2024). Evaluación de modelos predictivos para la optimización del costo de las operaciones unitarias de la UM Frances, Pataz – La libertad. [Universidad Nacional de Trujillo]. https://hdl.handle.net/20.500.14414/22690
Anticona, J., Evaluación de modelos predictivos para la optimización del costo de las operaciones unitarias de la UM Frances, Pataz – La libertad. []. PE: Universidad Nacional de Trujillo; 2024. https://hdl.handle.net/20.500.14414/22690
@misc{renati/1043584,
title = "Evaluación de modelos predictivos para la optimización del costo de las operaciones unitarias de la UM Frances, Pataz – La libertad.",
author = "Anticona Cueva, Jaime Yoni",
publisher = "Universidad Nacional de Trujillo",
year = "2024"
}
The main objective of this research was to evaluate predictive models for the optimization of the cost of unit operations at UM. Frances, Pataz La Libertad. A quantitative experimental research approach was used, consisting of training 4 predictive machine learning models, and subsequently the best performance was evaluated; the models trained were Multiple Linear Regression, Random Forest, Decision Tree and Artificial Neural Networks. The tools used were data collection guides. The population was constituted by the exploitation pits and the sample was constituted by the Hquina, Luz, Maurita N, Maurita S and Jugadora pits, in total there were 287 records that were taken from January to March 2024 in the UM. France s The results obtained when evaluating the profitability in January, February and March were 8.58 kUS$, 7.42 kUS$ and 7.40 kUS$ respectively. When training the different predictive models of machine learning in random forests an R2 of 89% was achieved, in mu ltiple linear regression an R2 of 91% was achieved, in decision tree an R2 of 85% was achieved, and using artificial neural networks an R2 of 0.92% was achieved In conclusion, the Artificial Neural Networks showed a higher R2 and therefore the optimum parameters of the unit operations costs (US$/tn) were found; it was obtained in drilling and blasting of 41.86, shovel cleaning at 19.78, hauling 11.36, loading 15.8 9, ventilation 7.81, and support 2.07 where exceeding these costs generates an over cost in the unit operations as a consequence affects the profitability of the company.
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