Citas bibligráficas
Idrogo, Y., (2022). Machine Learning aplicado al control de la fragmentación de rocas en la voladura de minas a tajo abierto [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/22525
Idrogo, Y., Machine Learning aplicado al control de la fragmentación de rocas en la voladura de minas a tajo abierto [Tesis]. PE: Universidad Nacional de Ingeniería; 2022. http://hdl.handle.net/20.500.14076/22525
@misc{renati/1750895,
title = "Machine Learning aplicado al control de la fragmentación de rocas en la voladura de minas a tajo abierto",
author = "Idrogo Zamora, Yessica Pamela",
publisher = "Universidad Nacional de Ingeniería",
year = "2022"
}
This thesis develops the data analysis and predictive modeling in blasting for an open pit mine. The first stage consists of posing the problem to be solved, this includes identifying the objectives, and in the case of a machine learning modeling which is the target variable or variables to be predicted, this variable to be predicted will be vital to solve the problem in question. At this stage, the algorithm or algorithms that are going to be used to solve the problem in question are identified. The second stage consists of data analysis where the types of variables, the amount of data available, the categories that are presented, the correlations between variables, and the statistical distribution of these variables are analyzed. The third stage is the data treatment where transformations are carried out on the variables of the database in the present thesis, the standardization treatments were carried out to homogenize the range of each parameter of the model and the Yeo Johnson transformation to normalize the distributions of data that are highly based. The fourth stage consists of carrying out the machine learning model to obtain the variable of interest, in this case artificial neural networks were used, which had as input variables the parameters that were already analyzed and treated in the previous stages and the output was the variable of interest that allows solving the problem posed in the first stage. The fifth stage consisted of analysis of results where the impact of the program based on artificial intelligence was evaluated to meet the desired objective of generating the drilling location of the holes that meets the fragmentation target and this through a comparison of the results obtained through the traditional method versus the method using artificial neural networks. Mention that, the flexibility of the artificial neural network allows engineers to generate models that adapt to their design needs, cost and fragmentation objective such as leading to crushing and grinding.
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