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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- Data-driven modeling and bayesian optimization of cooling towers for the reduction of water consumption(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025) Alatorre Cuéllar, Karla Valeria; Montesinos Castellanos, Alejandro; emipsanchez; Hernández Romero, Ilse María; López Guajardo, Enrique Alfonso; School of Engineering and Sciences; Campus MonterreyThis study presents a data-driven framework that integrates Machine Learning and Bayesian Optimization to minimize water consumption in industrial cooling towers while preserving cooling efficiency. Using historical operational and environmental data from a power generation facility, several regression models (Linear Regression, Random Forest, XGBoost, and Neural Networks) were developed to predict makeup water flow. Random Forest and XGBoost achieved the highest accuracy, with R2 scores of 0.982 and 0.972, respectively. Bayesian Optimization was employed to efficiently tune hyperparameters, yielding substantial improvements in predictive performance such as reducing RMSE by up to 18.6%. The methodology also incorporated feature importance analysis, which identified critical operational drivers such as blowdown flow and inlet water temperature. Overall, Random Forest was preferred due to its superior predictive accuracy, ease of interpretation, and practical integration into operational dashboards. By combining predictive modeling, optimization, and interpretability, the study offers a powerful methodology for a data-driven tool to support decision-making and identify opportunities for minimizing makeup water use in cooling tower operation.
- Greenhouse irrigation control based on reinforcement learning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Padilla Nates, Juan Pablo; Lozoya, Rafael Camilo; emimmayorquin; Orona, Luis Miguel; Medina, Sergio Armando; School of Engineering and Sciences; Campus MonterreyAccording to the United Nations, the worldwide population will grow to a vast number of 9 billion people by 2050. As the population keeps increasing, meeting the demand for food has become a tough challenge. Therefore, it is necessary to research and develop strategies on agriculture in order to keep up with demand while maintaining sustainability. Precision Irrigation, a sub-branch of precision agriculture, has gained momentum in modern times. This is an area of study about saving water while maintaining and not impacting the growth of the plant. By manipulating the irrigation schedule, one can keep the soil moisture level at the optimum level without stressing the plant. The objective of this thesis is to explore the implementation and performance of advance closed-loop control systems using artificial intelligence, such as the actor-critic from reinforcement learning, in a controlled environment to optimize the water schedule. The results will be compared against another closed-loop controller, the On-Off control, and an open-loop controller, the Time-Based Control. Water consumption analysis revealed that closed-loop controllers achieved a 40\% reduction in water use, compared to the open-loop controller. Additionally, the actor-critic controller showed a better response at maintaining the soil moisture level closer to the MAD limit compared to the On-Off and Time-based controls.