ACKNOWLEDGEMENTS I would like to express my sincere gratitude to the company for the institutional support provided for the development of this research, as well as to my supervisors for their constant guidance, trust, and support. Without this backing, the completion of this work would not have been possible. REFERENCES Albarracin, L. (2021). Empresa Minera Chinalco Perú S.A. Gerencia de Procesos Planta y Gerencia de Mina: Integración de mina-planta on line (ODJITSM). Bendezú, A. (2007). Mineralización tipo pórfido de Cu-Mo asociadas a venas cordilleranas de metales base: Toromocho-Morococha, Distrito de Morococha, Perú. García, A. (2015). Algoritmos para la estimación de modelos de mezclas gaussianas. Universidad de Cantabria. MathWorks. (2026). Mathworks. Obtenido de Autoencoders (Autocodificadores): http://la.mathworks.com/discovery/autoencoder.html Muñoz, A. (2022). Operational Decisions Just-In-Time (ODJIT). Ramos, J., Recines, F., Alcala, E., & Muñasqui, K. (2023). Modelamiento de alteraciones en 3D del Yacimiento Toromocho usando ensambles mineralógicos a partir de información semicuantitativa XRD de blast holes y diamond drill holes. XXI Congreso Peruano de Geología. Saldana, M., Galvez, E., Sales-Cruz, M., Salinas-Rodríguez, E., Castillo, J., Navarra, A., . . . Cisternas, L. (2026). A Stochastic Model Approach for Modeling SAG Mill Production and Power Through Bayesian Networks: A Case Study of the Chilean Copper Mining Industry. Minerals. Theunissen, C., Bradshaw, S., Auret, L., & Muller, T. (2021). One-Dimensional Convolutional Auto-Encoder for Predicting Furnace Blowback Events from Multivariate Time Series Process Data—A Case Study. . Minerals. Yin, J., & Pei, Z. (2021). Neural network-based order parameter for phase transitions and its applications in high-entropy alloys. Nature Computational Science, 1-8. doi:10.1038/s43588-021-00139-3 Zhang, Z., Jiang, T., Zhan, C., & Yang, Y. (2019). Gaussian feature learning based on variational autoencoder for improving nonlinear process monitoring. Journal of Process Control, 136155.
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