Tesis

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Colección de Tesis y Trabajos de grado (informe final del proyecto de investigación, tesina, u otro trabajo académico diferente a Tesis, sujeto a la revisión y aceptación de una comisión dictaminadora) presentados por alumnos para obtener un grado académico del Tecnológico de Monterrey.

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  • Tesis doctorado / doctoral thesis
    Environmental assessment of urban rivers through a dual lens approach: machine learning based water quality analysis and metagenomic characterization of contamination effects
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Fernández del Castillo Barrón, Alberto; Gradilla Hernández, Misael Sebastián; emipsanchez; García González, Alejandro; Pacheco Moscoa, Adriana; Brown, Lee; Oscar Alejandro Aguilar Jiménez; School of Engineering and Sciences; Campus Monterrey; Senés Guerrero, Carolina
    Urban rivers are critical ecosystems increasingly threatened by pollution. Effective water quality monitoring and contamination assessment are essential for informed management decisions. The Santiago River, a key hydrologic system in Mexico, has become one of the country’s most polluted rivers, posing significant ecological risks and public health concerns for nearby communities. This study underscores the urgent need for comprehensive environmental evaluation and enhanced monitoring approaches. Chapter one introduces the motivation behind monitoring water quality in highly polluted rivers, presenting the problem statement and contextual background of the Santiago River basin. It outlines the research question and provides an overview of the proposed dual-lens approach: combining water quality analysis via machine learning algorithms with metagenomic characterization of contamination effects. Key contributions of this work to the field are also highlighted. Chapter two reviews global monitoring strategies from highly polluted rivers, focusing on nine rivers across developed and developing countries to offer a comparative perspective on water quality management needs. In Chapter three, regression and classification machine learning models are developed to predict the Santiago River Water Quality Index (SR-WQI), designed as complementary tools to strengthen the current monitoring program. Chapter four analyzes the historical water quality patterns of the Santiago River to identify the most variable and representative data for training machine learning models. This chapter also reveals that redundant data can hinder model performance by leading to overfitting. Chapter five investigates spatial variations in the microbial composition of Santiago River sediments and examines correlations with water quality. Using high-throughput sequencing, potential microbial biomarkers were identified and impacts of physicochemical parameters and heavy metals on microbial communities were assessed. Finally, chapter five highlight the main findings of this thesis and covers some limitations, perspectives for future research and final remarks.
  • Tesis de maestría
    Mathematical Modelling of a Domestic Wastewater Treatment System Combining a Septic Tank, an Up flow Anaerobic Filter, and a Constructed Wetland
    (Instituto Tecnológico y de Estudios Superiores de Monterrey) Fernández del Castillo Barrón, Alberto; SENES GUERRERO, CAROLINA; 253929; Senés Guerrero, Carolina; puelquio, emipsanchez; De Anda Sánchez, José; Díaz Torres, Osiris; Escuela de Ingeniería y Ciencias; Campus Monterrey; Gradilla Hernández, Misael Sebastián
    Wastewater treatment is essential for environmental protection, public health, and to ensure water supply for future generations. Conventional treatment technologies, such as activated sludge, are energy-intensive and require constant maintenance as they were designed for large cities. These technologies are not suitable for wastewater treatment in small communities and rural areas. Decentralized treatment approaches combining Anaerobic Reactors (AR) and Constructed Wetlands (CW) have proven more appropriate in these cases. These passive treatment methods require low investment and maintenance. Additionally, they allow for onsite water reuse and energy generation. The combined configuration of an AR sequentially coupled with a CW have proven to be an efficient system by which the limitations of the individual units (AR and CW) are overcome. Chapter one provides the motivation related to global needs of wastewater treatment, problem statement and context, which is the deficit of treatment and current technologies deficiencies. The research question and solution overview provided by the mathematical modeling applied to decentralized treatment technologies. Finally, the main contributions of this work to the state of the art are detailed. In Chapter 2, several systems combining three types of AR (Up-flow Anaerobic Sludge Blanket, Anaerobic Baffled Reactor, and Up-flow Anaerobic Filter) with CW are reviewed as well as their capabilities and drawbacks. An emphasis was made to analyze their performances, characteristics, and the processes involved in pollutant removal (solids, organic matter, nutrients, and sulfate). In Chapter 3, the experimental work of this thesis is presented. Mathematical modeling of the pollutant removal processes occurring in wastewater treatment plants can provide detailed and valuable information. More profound knowledge provided by these models is useful for predicting the future behavior of the treatment systems and can be applied to optimize the operation and facilitate monitoring. Optimal operation procedures and feasible monitoring are essential to ensure the successful implementation of these technologies in rural areas and small communities where investment budgets are limited. The system's efficiency and robustness can also be increased by applying mathematical modeling in design and control. The experimental system evaluated consisted of a Septic Tank (ST), an Up-flow Anaerobic Filter, and a Horizontal Flow Constructed Wetland (HFCW). Sampling was done biweekly for three months. BOD5, COD, TSS, TKN, ON, NH4+, NO2- and NO3- levels were measured. The experimental data was used to develop three different mathematical models: First order kinetics models, Linear Multiple Regression models, and Mass balance models. The first-order kinetic models developed were efficient to predict pollutant removal with significant precision. Multiple linear regression models were found to help reduce the cost and time of monitoring procedures. These models also reflected physical, chemical, and biological processes involved in pollutant removal in a logical manner. Finally, mass balance models indicated that the system is highly tolerant to influent wastewater variations.
En caso de no señalar algo distinto de manera particular, los materiales son compartidos bajo los siguientes términos: Atribución-No comercial-No derivadas CC BY-NC-ND http://creativecommons.org/licenses/by-nc-nd/4.0
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