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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- Advanced Optimization of the Mexican National Interconnected Transmission Grid: A Comprehensive Analysis of Power Losses, Greenhouse Gas Emissions, and Renewable Energy Curtailment(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-12-26) Vega Gómez, Oscar Alejandro; Flores Tlacuahuac, Antonio; emimmayorquin; Hernández Romero, Ilse María; Faculty of Engineering Sciences; Campus Monterrey; Probst Oleszewski, Oliver ProbstThis study presents a thorough investigation into the Mexican National Interconnected System’s (NIS) Transmission grid, employing a mathematical optimization model implemented in the Julia programming language. The principal focus is to optimize the dynamic behavior of the system, considering both renewable and conventional energy sources. The dispatch study spans from 2017 to 2022, with hourly annual generation resolution; it focuses on achieving optimal cost efficiency while simultaneously meeting the system’s de mand and analyzing the transmission power losses for different scenarios. The formulation systematically integrates factors such as bidirectional transmission flux, current flow, generation costs, greenhouse gas emissions including various pollutants, optimal operating policies, power losses, and transmission capacity. The study highlights the mismatch between generation and transmission capacity growth, providing detailed assessments of the impact of minimum generation levels and grid upgrades. The findings contribute valuable insights into addressing congestion issues, optimizing the grid, and promoting sustainable energy practices in the Mexican NIS. The study also addresses power flow and losses within the Mexican transmission grid, as well as the impacts of generating with higher generator minimum capacity levels. It covers environmental consequences, renewable energy curtailment, and congestion patterns arising from congestion and power losses.
- Data-Driven Recurrent Neural Networks Modeling of Cyanobacteria Growth in Bubble Columns Reactors under Sparging with CO2-enriched Air(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-09-28) Avilez Cuahquentzi, Karen Joselyne; Flores Tlacuahuac, Antonio; emimmayorquin; Hernández Romero, Ilse María; School of Engineering and Sciences; Campus Monterrey; Parra Saldivar, RobertoThe Institute of Advanced Materials for Sustainable Manufacturing at ITESM conducted a 10-month experiment on biomass production using cyanobacteria in bioreactors. This study highlights the robust predictive capabilities of LSTM models, which surpass traditional approaches. The research unfolds through key stages, emphasizing the importance of dataset preprocessing, including outlier identification, data scaling, and feature selection using statistical methods. Recognizing the dataset’s sequential nature, we transform the time series into supervised learning, enabling effective training and network design. The LSTM model architecture, detailed in Appendices A and B, efficiently captures temporal dependencies, offering an effective alternative to dynamic models. Eval uation metrics, such as mean absolute error and mean absolute percentage error, underscore the accuracy of the model, which is crucial for precision-demanding applications. This work significantly contributes to reaction system analysis by presenting a streamlined approach for forecasting biomass production. We introduce an innovative monitoring approach using CO2 concentration [%], yielding valuable insights for sustainable industrial practices. Looking forward, our LSTM models hold promise for optimizing algae biomass production systems. Future research will explore Bayesian optimization techniques, hyperparameter tuning, and real-time sensor data integration. This holistic approach aligns with ongoing efforts in industrial biotechnology and sustainability, bridging the oretical research with practical applications.
- Prediction of viscoelastic and printability properties on binder-free TiO2-based ceramic pastes by DIW through a Machine Learning approach(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-08-15) Pulido Victoria, Luis Antonio; Flores Tlacuahuac, Antonio; emimmayorquin; Bonilla Cruz, José; Hernández Romero, Ilse María; Escuela de Ingeniería y Ciencias; Campus MonterreyPrintability, a key factor in the success of Additive Manufacturing (AM) processes, relies on various aspects, including material properties and process parameters. This study aims to explore the relationship between the rheological properties of binder-free TiO2-based ceramic pastes and their printability using a machine learning approach, specifically utilizing a feedforward Deep Neural Network (DNN) model. The main objective of this thesis is to develop a predictive model that can accurately estimate the printability of binder-free TiO2-based ceramic pastes based on key rheological properties. To achieve this, a comprehensive dataset consisting of 25 samples of rheological properties and corresponding printability assessments was collected and used for model training and evaluation. The methodology involved the collection of rheological and viscoelastic data for a range of TiO2-based ceramic pastes including shear viscosity (η) values as a function of shear rate (γ_ ), and storage (G0) and loss (G00) modulus values as a function of oscillatory stress (σ). Rheological properties (γ_ and η) were then utilized as input features for the feed-forward DNN model, while the viscoelastic data (G0 and G00) was set as the target properties. The model was trained using a portion of the dataset, and the remaining set was utilized for testing. No validation set was defined due to the small size of the dataset. The results obtained from the developed model not only demonstrated a correlation between the rheological properties and printability of the studied ceramic pastes but also revealed a relationship between the rheological and viscoelastic behavior of the samples, providing deeper insights into this ceramic system. The predictive capabilities of the model were evaluated based on performance metrics such as Mean Squared Error (MSE) and Relative Error ( ). The discussion section provides a detailed analysis of the findings and explores the limitations of the study, such as the sample size and potential influence of the experimental work on the model’s performance. This thesis contributes to the field of AM by presenting a machine learning-based approach for predicting the printability of binder-free TiO2-based ceramic pastes. The DNN model proves to be a valuable tool for assessing the printability of the pastes. Moreover, it provides further insights into the ceramic system by correlating rheological and viscoleastic properties. This research opens up possibilities for optimizing paste formulations and process parameters to enhance the additive manufacturing of ceramics. Furthermore, the model here proposed may serve as starting point for exploring alternate machine learning algorithms, such as transfer learning, generative networks and even Bayesian optimization.