Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Charging EV station forecasting and location model for Mexico’s private sector(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-13) Hernández Salazar, Aldo; Ríos Solís, Yasmín Águeda; emimmayorquin; Jacobo Romero, Yulitza Yazmin; Shcool of Engineering and Sciences; Campus Monterrey; Probst, OliverThe decarbonization of the transport sector is critical for addressing climate change, with electric vehicles (EV) representing a pivotal solution. This thesis focuses on forecasting EV adoption and optimizing charging stations’ location in Mexico’s private sector. The study examines relevant national and international regulations and existing EV adoption models through a comprehensive literature review. Data collection incorporates national statistics, energy consumption records, and market reports on EV sales and adoption rates. Using statistical methods, the research develops multiple scenarios for EV adoption up to 2030. A mixed integer programming model is then constructed to maximize the profitability of charging station placements, considering constraints such as budget, parking availability, and electrical capacity. A detailed case study with anonymized data from Iberdrola’s clients is conducted, simulating the model to determine optimal charging station locations and configurations. The results provide valuable insights into the infrastructure needed to support the transition to EVs in Mexico, offering strategic recommendations for stakeholders. The study concludes with suggestions for future research, emphasizing the importance of real-time data and expanding the analysis to public charging infrastructure. This work aims to contribute significantly to Mexico’s sustainable energy transition and develop an efficient, widespread EV charging network.
- Energy consumption and greenhouse gas emissions modeling for the cement production industry in Mexico(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Murrieta Melchor, Mariana; Santoyo Castelazo, Edgar; emimmayorquin; Ledezma Martínez, Minerva; School of Engineering and Sciences; Campus Ciudad de México; García Kerdan, IvánThe world is working on strategies to face climate change, of which mitigating emissions is crucial. In the long term, one of the most significant challenges is to meet the needs of society while incorporating sustainable processes that consider such mitigation. Currently, cement industry contributes to emissions generation within its core processes, accounting for over 8% of global greenhouse gas (GHG) emissions. Mexico is an emerging economy with a great need for infrastructure development, which leads to an increasing cement demand. Population and economic growth tendencies are vital for predicting this industry’s demand. Different emissions mitigation strategies can be assessed with this macroeconomic perspective to determine their adaptation and implementation in the Mexican context. Aiming to collaborate in the global efforts to address climate change, this study proposed a research framework that provided a perspective on implementing mitigation strategies in the Mexican cement production industry by 2050. This framework provided an overview of the national cement industry, followed by a Business-As-Usual scenario construction with macroeconomic indicators, from which five alternative scenarios were derived. Energy and emissions modeling was carried out for each scenario using the Low Emissions Analysis Platform (LEAP). The alternative scenarios were based on national, international, and private sector emissions reduction targets for the cement production process in Mexico. These scenarios were discussed through a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, finding that carbon capture technologies are essential for significantly reducing GHG emissions. However, these technologies are not fully developed nor accessible for the industry to adopt with the required urgency. Additionally, clinker-to-cement ratio reduction represents an essential measure to reduce process-related emissions. Finally, reaching the emissions reduction targets of the scenarios requires concerted efforts between the private and public sectors. The novelty of this work resides in: (a) concentrating cement production industry performance in a single information and data platform; (b) adopting the modeled emissions reduction targets and their contextualization to Mexico; and (c) adjusting an appropriate methodology framework to evaluate a critical industrial sector. Moreover, this framework could be used for future research on emissions mitigation pathways in other national industrial sectors.
- Financial Habits of Mexican Women using Machine Learning Algorithms(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-04) Lozano Medina, Jessica Ivonne; Hernández Gress, Neil; RR/tolmquevedo; Ceballos Cancino, Héctor Gibrán; Flores Segovia, Miguel Alejandro; School of Engineering and Sciences; Campus Monterrey; Hervert Escobar, LauraThis research was conducted under the Master in Computational Science program at Tecnológico de Monterrey. The proposal is a model to assess a profile risk for Mexican women, who require the service of a financial portfolio offered by a financial institution. Typically, women are scored with a lower financial risk than men. However, the understanding of variables and indicators that lead to such results, are not fully understood. Furthermore, the stochastic nature of the data makes it difficult to generate a suitable profile to offer an adequate financial portfolio to the women segment. Therefore, there is a great interest for developing methods that correctly model the behavior, and aid the decision-making process in financial services. Several models in the State-of-art for this type of analysis is done with linear programming and statistical techniques. Therefore, this study will use a benchmark of Machine Learning algorithms, such as Unsupervised and Supervised Learning algorithms, to extract information on four different datasets relevant to the population of interest. The first phase involves applying state-of-the-art techniques on public datasets of the Mexican population, whereas the second phase involves a future research involving a financial institution to create the model for the Women segment. It was found that financial habits of the population are heavily dependent on the region. There also an important group in the population characterized for not possessing an account in a financial institution and also not having emergency funds. In the case of the profiles of women, the most important attributes were their civil status and their participation in the workforce. The largest group of women are housewives, though the second largest group consists of married women who also participate in the workforce.