Sistema de información de alertas de estabilidad física en diques de relave

• Autor: Llanos Sanabria, Daniel Estuardo
• Formato: tesis de grado
• Fecha de Publicación: 2019
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/12352
• Enlace del recurso: https://hdl.handle.net/20.500.12724/12352
• Nivel de acceso: acceso abierto
• Materia: Sistemas de información; Computación en la nube; Diques; Relaves mineros; Information systems; Cloud computing; Dikes (Engineering); Tailings (Metallurgy); https://purl.org/pe-repo/ocde/ford#2.02.04

Due to the current problems where natural resources are directly affected by contamination of large areas of soil and rivers, impacting the environment, said contamination is caused by failures in the physical stability of the tailings dikes. For this, the development of an alert system for the physical stability of the levees is proposed, in order to minimize the risks and prevent these tailings spills from occurring, therefore the case will be addressed based on the interviews and reports from the Ministry of Energy and Mines (MEM). After the analysis of the interviews and the reports provided by the Ministry of Energy and Mines, the need to automate the recording of the measurements of the variables of humidity and inclination of the tailings dikes was identified, so the analysis will be carried out mainly on these two variables that are essential to maintain the stability of the dams, since if these variables are not controlled there would be a potential risk of landslides in the tailings dam. The objective of this project is to automate the data collection through the digital humidity and inclination sensors installed in the tailings dam and then send this data to the computing services in the cloud (Computing Cloud Services). Through an Internet connection where you can access the computational resources where the web servers and databases will be deployed, for the implementation of the service it focuses on the infrastructure as a service of “Infrastructure as a Service” (IaaS), later With the use of machine learning (Machine Learning) as a means of innovation, it will be possible to interpret this data and generate possible predictability scenarios, to avoid physical stability failures. This proposal seeks to provide a solution as a measure to prevent the collapse of tailings dams, thereby mitigating environmental disasters and human losses in the communities where mining activities take place.