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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Now showing 1 - 10 of 15
  • Tesis de maestría / master thesis
    Optimization of kinetic and operating parameters in bioreactors using evolutionary algorithms
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11) Barrera Hernández, Gonzalo Irving; Sosa Hernández, Víctor Adrián; emipsanchez; Alfaro Ponce, Mariel; Aranda Barradas, Juan Silvestre; Corrales Muñoz, David Camilo; School of Engineering and Sciences; Campus Estado de México; Gómez Acata, Rigel Valentín
    Bioreactors play a role in creating biological products such as medicines and biofuels by care fully controlling factors such as substrate levels and temperature within them to obtain optimal production results, bioreactor production process poses a challenge that poses a challenge to engineers due to the intricate setup involved. In the field of microbiology and biotechnology, conventional approaches such as the Monod model, logistic growth models, and fed-batch techniques have been employed to predict and improve the growth conditions of microor ganisms and the production of proteins of interest in fermenters. However, these approaches could face challenges when they encounter nonlinear systems and conflicting objectives. To address these challenges, our suggestion is to approach the configuration of factors in bioreactors as an optimization problem using an evolutionary algorithm that can improve the effectiveness and quality of the operating process. The objective of this study is to in vestigate and create a pipeline that integrates evolutionary algorithms to solve multi-objective and scalar optimization problems, aimed at identifying kinetic and critical parameters within a bioreactor system. The optimization process involves, in the first stage, a least squares ap proach that considers product, biomass, dissolved oxygen, and substrate concentrations as objectives, with the kinetic parameters (e.g., maximum specific growth rate and substrate affinity) serving as decision variables. The second stage focuses solely on maximizing the amount of produced product, specifically biomass, using critical operational variables, such as feed rate and aeration, as decision variables. The research employs Escherichia coli as a microorganism that has been genetically al tered to produce orange fluorescent protein (OFP) to test the validity of improvement frame works. Initially, in the simulation and process tuning phase, experimental information, from batch cultures, is used to accurately determine the factors. Later, in the fed-batch phase, the application of an algorithm is used to optimize biomass yield while considering operational constraints such as oxygen levels and maximum reactor volume. The findings show that this method accurately calculates factors during the fed-batch phase and efficiently increases biomass production in the continuous fed phase. The use of algorithms such as multiple NSGA-III and single-objective genetic algorithms provides valuable benefits when dealing with intricate bioreactor configurations that have conflicting objectives such as managing substrate consumption and improving production yield. This approach has promising prospects for improving the accuracy and efficiency of bioprocess optimization, while increasing its scalability, in the field of biotechnology in the future.
  • Tesis de maestría / master thesis
    View planning for three-dimensional environment reconstruction using the Next Best View method
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-03) Shain Ruvalcaba, Everardo; López Damian, Efraín; emipsanchez; Santana Díaz, Alfredo; López Damián, Efraín; School of Engineering and Sciences; Campus Ciudad de México; González Hernández, Hugo Gustavo
    This study was made with the purpose of understanding the impact of the objective functionand optimization methods on the Next Best View problem, which consists in finding the next position that the sensor or camera needs to take to scan an object or scenery in its totality. A simulated 5-Degree-of-Freedom mobile robot with a mounted simulated range sensor was used on a Virtual Reality Modeling Language environment, and the space discretization was made using a voxel map. For the objective function, two main factors were included: an area factor to make sure that the image taken by the sensor provides the best possible information, and a motion factor made up of distance and energy sub-factors to reduce the resources used by the robot, making multiple experiments on a laboratory scene to determine their best arrangement on the final objective function. Global optimization tasks such as a backstepping technique to escape local minima and a dynamic change in the objective function were implemented. The retrievement of the scene was made on an iterative process, with each iteration needing an optimization process for which three different methods were tested: Nelder-Mead, an Evolution Strategy, and Simulated Annealing. A set of experiments comparing the three methods in computational time and retrievement efficiency were made on three different environments with increasing difficulty to test their repeatability, with them being a laboratory model, a room with a cube and a pyramid inside it, and a study room with multiple furniture and windows.
  • Tesis doctorado / doctoral thesis
    Designing sustainable agri-food supply chain networks
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12-02) Gholiyanjouybari, Fatemeh; Hajiaghaeikeshteli, Mostafa; emipsanchez; Mejia Argueta, Christopher; Bonakdari, Hossein; School of Engineering and Sciences; Campus Monterrey; Smith Cornejo, Neale Ricardo; Rodríguez Calvo, Ericka Zulema
    Agri-food products are critical to sustaining human life, as they provide essential nutrients for maintaining bodily functions. Agri-food production must match potential demand to ensure efficient supply to industries and markets. In terms of national and international concerns, this is one of the most important, even a top priority. In recent years, the agri-food industry has prioritized the development of efficient supply chain systems based on current trends and principles, such as sustainability and circular economy. The principles of sustainability can be employed to effectively utilize or reintroduce agri-food waste back into the network. This PhD dissertation deals with designing new agri-food supply chain networks for the first time in the literature. It not only considers the most important products to study, but also focuses on the recent trends and challenging issues like circular economy, water consumption, CO2 emission, and sustainability. We considered Saffron, Coconut, Soybean, and Pistachio, respectively, in four chapters of this thesis. In this work, we formulate some novel mixed-integer linear programming models to design agri-food supply chain networks in different agriculture industries, considering the above new challenges. The multi-objective networks struggle to manage the total net profit while monitoring CO2 emissions and the satisfaction of customers within the network. Given the NP-hard nature of the networks, the solution approach embraces a set of conventional, new, modified, and hybrid metaheuristics to surmount its complexity effectively. The effectiveness of the proposed mathematical models is certified by case studies and general problems evolved from real-world practices. In Saffron's work, we consider marketing practices and develop a stochastic multiobjective programming model to improve sustainability in three main areas. A convex robust optimization approach addresses farm production capacity uncertainty and saffron demand uncertainty. The LP-metric method is used to validate the mathematical model for the saffron business. We adopt a modified Keshtel algorithm to deal with the problem of NP-hardness. Two strategies are used to evaluate the performance of the proposed solution methods: a statistical comparison and a supportive tool that is based on multicriteria decision-making (MCDM). According to the MCDM method, MOKASEO outperformed other algorithms in small, medium, and large-sized problems compared to the other algorithms tested. The secodn supply chain network that we consider to design its colsed-loop network is for the coconut industry. We propose a new mixed-integer linear programming model to design an agri-food supply chain network under sustainable terms. With the goal of resolving a multi-objective closed-loop supply chain, both forward and reverse movements of products are taken into account. During the planning process, the model monitors environmental pollution within the network as well as job opportunities. Given the NP-hardness of the model, we use six multi-objective optimizers and three hybrid algorithms, among which the multi-objective artificial rabbit optimizer is first developed and applied in this study. Therefore, fifteen practical tests are conducted to determine whether the model is compatible with real conditions. The Friedman statistical test and interval plots demonstrate that optimizers are capable of solving problems of all sizes. In both statistical tests and the hybrid Multi-Criteria Decision Making (MCDM) framework, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) outperformed solving practical tests. In the third work, we study circular economy in a closed-loop supply chain. To do this, we consider one of the most famous and valuable agricultural products, soybean. We formulate a novel mixed-integer linear programming model to design a closed-loop agri-food supply chain network under sustainability and circular economy terms. The multi-objective network strives to reduce CO2 emissions while monitoring customer satisfaction and overall net profit. Since the network is NP-hard, a combination of conventional and hybrid metaheuristics is used to overcome its complexity. Four multi-objective optimization algorithms and three hybrid algorithms are utilized to investigate the model's suitability for real-world conditions. A combination of interval plots and hybrid multi-criteria decision-making techniques demonstrates that optimizers can handle any size problem. For large and mediumsized problems, however, MOHHSA is more effective than MOGWO. Finally, in the fourth paper, we develop a new mixed linear mathematical model for the pistachio supply chain network to minimize the total fixed and variable costs of the closed-loop supply chain. This model is addressed with efficient and well-known meta-heuristic algorithms. A hybrid meta-heuristic algorithm is also developed to enhance the intensification and diversification phases. Finally, we compare and evaluate the quality of both meta-heuristic algorithms and hybrid algorithms.
  • Tesis de maestría
    Tailoring metaheuristics for designing thermodynamic-optimal water based cooling devices for microelectronic thermal management applications
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06) Pérez Espinosa, Guillermo; TERASHIMA MARIN, HUGO; 65879; Terashima Marín, Hugo; emipsanchez; Ortiz Bayliss, José Carlos; Aviña Cervantes, Juan Gabriel; Escuela de Ingeniería y Ciencias; Campus Monterrey; Cruz Duarte, Jorge Mario
    Heat sinks provide a common and straightforward alternative to dealing with the Microelectronic Thermal Management (MTM) problem due to their simplicity of fabrication, low cost, and reliability of heat dissipation. The MTM problem is highly relevant in today's electronics industry, as new electronic devices' miniaturization and enhanced performance have increased their heat power generation. So, regarding the second law of thermodynamics, an optimal heat sink design can guarantee that the microelectronic components operate without jeopardizing their life span and performance. To solve this challenging problem, Metaheuristics~(MHs) have shown to be excellent alternatives due to their reliability, flexibility, and simplicity. Nevertheless, no single MH guarantees an overall outstanding performance. Thus, the motivation for this work is to open ample room for practitioners to find the proper solver to deal with a given problem without requiring extensive knowledge of heuristic-based optimization. This work studies the feasibility of implementing a strategy for Automatic Metaheuristic Design powered by a hyper-heuristic search to minimize the entropy generation rate of microchannel heat sinks and tailor population-based and metaphor-less MHs for solving the MTM. A mathematical model based on thermodynamic modeling via the Entropy Generation Minimization (EGM) criterion was used to obtain the value of the entropy generation rate of a rectangular microchannel heat sink according to their design. Four different scenarios were considered, varying the design specifications for the heat sinks and comparing our generated MH against seven well-known heuristic-based algorithms from the literature. The one-sided Wilcoxon signed ranked test was used to perform these comparisons. Statistical evidence was found to claim that our tailored MHs manage to outperform them, in most cases, at least in the tested scenarios. Additionally, we followed a methodology to infer which operators should be considered in a curated heuristic space to design the proper MH easily. We found that using this curated search space benefits the overall process, as the HH algorithm managed to tailor high-performing MHs faster and more consistently than its counterpart. Furthermore, insights were obtained on which HH parameters are more suitable for our search, as some can enhance the tailoring process when tuned correctly. Finally, we tested some of our best designs found to see how they perform when minor fluctuations appear on some variables, just as they occur in real-life implementations. All the experimentation processes also found that the search operators of evolutionary algorithms are well suited to solve this problem, as they compose several of our tailored MHs, and that the combination of High Thermal Conductive Graphite and water achieved the lower entropy generation rate values from the four combinations tested.
  • Tesis de maestría
    Impact of pulsed electric fields on fermentation process during yogurt production
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06) Miranda Mejía, Graciela A.; MORALES DE LA PEÑA, MARIANA; 223211; Morales de la Peña, Mariana; puemcuervo; Arredondo Ochoa, Teresita; Gomez, Lorena; School of Engineering and Sciences; Campus Monterrey; Tejada Ortigoza, Viridiana
    A study on the effect of pulsed electric fields (PEF) application to the inoculum for natural drinkable yogurt production is presented in this dissertation. This research involves the fermentation time optimization of yogurt production through the application of PEF, as well as the evaluation of the proximal composition, physicochemical characterization, and a discriminatory sensory perception test immediately after processing and during storage of the obtained yogurt treated with PEF, having a control yogurt as a reference. Chapter 1 includes the motivation, problem statement, and context of this study. Chapter 2 is related to the hypothesis and objectives. Chapter 3 comprises the theoretical framework regarding yogurt production, Lacto-fermentation, and PEF principles and applications. Chapter 4 details the materials and methods to conduct the experimental work. Chapter 5 focuses on the results analysis and a discussion. Chapter 6 includes conclusions and recommendations. Finally, it is included a disclosure regarding scientific material and an appendix section containing complementary information collected during data organization and analysis. Overall, this master’s dissertation demonstrated that PEF technology is a potential alternative to optimize yogurt production processes through the reduction of fermentation time without significantly altering its proximal content and physicochemical characteristics and sensory perception, resulting in a final pro are included duct similar to that one obtained by the conventional process.
  • Tesis de doctorado
    Studies on optimal design, operation and control of process and energy systems
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-07-07) Andrés Martínez, Oswaldo; Flores Tlacuahuac, Antonio; tolmquevedo; Escobar Valderrama, Gerardo; Mayo Maldonado, Jonathan C.; Rico Ramírez, Vicente; Puebla Núñez, Héctor; School of Engineering and Sciences; Campus Monterrey
    The quest for more sustainable process and energy systems requires optimal design, operation and control solutions at different temporal and spatial scales. Greenhouse gas emission and waste reduction, and renewable energy sources integration are some ongoing concerns in chemical and power generation plants. These goals should be achieved while maximizing profits and meeting technical and environmental restrictions. Process systems engineering provides reliable computational and mathematical tools to address many of these emerging issues. In this regard, control and optimization techniques have the potential to handle sustainability related challenges in chemical engineering and other fields. Optimization can be applied at molecular, process and enterprise levels, while control policies can be deployed along time intervals of milliseconds, minutes, hours and days. In this work, studies on optimal design, operation and control in the context of chemical process and energy systems are presented. Subjects such as recovery of waste energy, national power flow systems, reactor operating policies, and integration of renewable energy are discussed. Case studies of different nature and complexity are tackled by means of mathematical programming and optimal control methods. Particularly, optimal molecular design under uncertainty, singular optimal control of chemical processes, optimal power flow of the Mexican electricity system, and model predictive control of dc-dc boost converters. Results show the capability of the utilized methods to produce optimal solutions that can be implemented in real settings or in further theoretical and practical developments.
  • Tesis de doctorado
    Passive decentralized island mode detection and optimization-based design of passive filters for disconnection events in microgrid systems
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-06) López Gutiérrez, Juan Roberto; Ponce Cruz, Pedro; puemcuervo; Balderas Silva, David Christopher; Reyes Rosario, Alfredo; Soriano Avedaño, Luis Arturo; School of Engineering and Sciences; Campus Ciudad de México; Ibarra Moyers, Luis Miguel
    In recent years, the electrical network has been evolving towards becoming a more sustainable system, the present environmental concerns regarding the greenhouse gas emission by the energy sector have pushed forward the integration of alternative generation units that promote the decarbonization of the energy production sector. Over the past decades the integration of these ``cleaner'' energy generation systems has been done in an On-grid and an Off-grid fashion, however, this integration strategy encountered some problems regarding key areas such as control and management, just to mention a few. The microgrid concept is then created to overcome these issues, allowing a seamless integration to the electrical network of the growing alternative generation assets, improving how these ``cleaner'' energy production alternatives are managed into more sustainable systems. In Microgrids with a high penetration of renewable energy sources, power converters are used to regulate the produced energy to a single voltage and frequency reference value across the microgrid. Adequate incorporation of an LC filter at the output of power electronic devices allows the attenuation of harmful harmonics that can be introduced to the microgrid's energy bus. By traditional methods, LC filter values can be calculated by means of the power rating, switching frequency, cutoff frequency, and using the bode frequency domain. It is important to consider that, a microgrid including distributed generators can operate connected to the main electrical network or in an isolated manner, referred to as island operation. The transition between both states can occur voluntarily, but a disconnection can also happen unexpectedly. The associated transients can be harmful to the grid, and compensating actions must be triggered to avoid service interruption, preserve power quality, and minimize the possibility of faults. It is important to consider that in transition from a connected to an autonomous microgrid operation, the calculated LC filter can lead to high harmonic injection. As a result, a tuning methodology capable of obtaining the right set of parameters for the LC filter for such transition events can improve the performance of the microgrid. Alternately, such transition events must be detected to enable compensating action; island detection methods are essential to this end. Such techniques typically depend on communication networks or on the introduction of minor electrical disturbances to identify and broadcast unexpected islanding events. However, local energy resources are distributed, variable, and are expected to be integrated in a plug-and-play manner; then, conventional island detection strategies can be ineffective as they rely on specific infrastructure. To overcome those problems, this work proposes to improve the islanding phenomenon in two main contributions. To tackle the issues in regards to the introduction of harmful transients by traditional LC filters, this work optimizes the LC output parameters with respect to the size of the filter components, the IEEE Std 519-2014, and bandwidth of the filter, within a bounded region of values subjected to performance conditions such as voltage output, and the produced total harmonic distortion measurements during the transition from a connected to an autonomous operation. In a case study, genetic algorithm optimization is used to obtain the LC filter parameters and compared to a conventional arithmetic methodology to obtain the values of the filter. The optimization results in a set of values that lead to a higher harmonic attenuation after the transition rather than a conventional method using the switching frequency as the main design factor. In the other end of the islanding phenomenon, where islanding events must be detected while avoiding traditional infrastructure setbacks, a straightforward, distributed island detection technique is proposed, this technique relies only on local electrical measurements, available at the output of each generating unit. The proposed method is based on the estimated power-frequency ratio, associated with the stiffness of the grid. A ``stiffness change'' effectively reveals island operating conditions, discards heavy load variations, and enables independent (distributed) operation. The proposal was validated through digital simulations and an experimental test-bed. Such test-bed consists of a Real-Time HIL implementation, the proposed island detection algorithm is programmed to run in an embedded format while connected to a Real-Time simulator running a microgrid equivalent model in the form of a three-phase parallel RLC load as recommended by the IEEE Std. 929 and IEEE Std. 1547 for islanding detection. Results showed that the proposed technique can effectively detect island operation at each generating unit interacting in the microgrid. Moreover, it was about three times faster than other reported techniques.
  • Tesis de maestría
    Location optimization of drug take-back boxes in the state of Pennsylvania
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-07) López Vázquez, Alejandro; López Soto, Diana; 366362; López Soto, Diana; puemcuervo, emipsanchez; Smith Cornejo, Neale Ricardo; School of Engineering and Sciences; Campus Monterrey; Griffin, Paul
    The Opioid Use Disorder (OUD) crisis in the USA poses one of the greatest public health problems(Hodder et al., 2021), just in 2019 72.9% out of the 70,630 overdose deaths in the US were caused by opioids (CDC, 2021). One of the most important strategies to tackle OUD is the creation of community-based programs with only a few of them being objectively evaluated (Leece et al., 2019). PROSPER being is an evidence-based model which delivers scientifically proven, high-quality programs for communities has delivered several projects, one of them being the creation of new drug take-back boxes in collaboration with Penn State University Engineering students (R. Spoth & Greenberg, 2011; Wagner, 2020). The problem posed in this thesis is the need to locate a limited number of boxes in a way that the availability and coverage is maximized, taking into consideration that the box should be located in a secure and public place and in some cases considering other factors as Population rate, Dispensation rate, OD death rate, DUD rate, ED visit rate, number of boxes already in the county and the percentage to reach the goal, for which 4 optimization models were created. Two of the models had a county level scope then selecting the zip codes to be covered and the other two working directly with zip code, the other difference is that two of the models only focus on maximizing the population covered while the other two focus both in the population and all the other factor previously mentioned. The results rendered by the models place model 2 as the one that better increases the coverage of the population, both by the county average and total population, nevertheless model 4 has also a great coverage increase, but also considering all the additional factors that make it a mor relevant and adequate model for the purpose of combating the OUD crisis in the state of Pennsylvania.
  • Tesis de maestría
    Power grid optimization at macroscopic level: a WEF Nexus perspective for Monterrey metropolitan area
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12) Cantú Hernández, Rodrigo Alejandro; MAHLKNECHT, JURGEN; 120939; Mahlknecht, Jürgen; emipsanchez; Chuck Hernández, Cristina Elizabeth; Escuela de Ingeniería y Ciencias; Campus Monterrey; González Bravo, Ramón
    This project aims to develop an optimization approach to evaluate the associated systems’ power demand, including the residential, commercial, industrial, public, and agricultural users, based on the Water-Energy-Food Nexus (WEF nexus) perspective. The context for this project is the landscape of the WEF nexus involving the production and distribution of their corresponding goods under which the Monterrey Metropolitan Area (MMA) operates. This is an important case study due to the area’s contribution to the country’s development, it being Mexico’s main industrial hub. Previous efforts have only focused on the water sector concerning the other two and the food sector with the energy sector. The proposed approach seeks to identify the interrelationships among the three sectors to evaluate priorities in the management of natural resources and create a pathway to understand the dynamics of the interlinks within the conflicting resources with the energy sector as the focal point. With that said, the focus of this project is to propose an off-grid power optimization model emphasizing the integration of the water, energy, and food sectors to promote a diverse, reliable, sustainable, and sufficient grid. The proposed mathematical model accounts for economic and environmental objectives by including fixed and variable costs, optimal energy generation, and greenhouse gas emissions reduction. The most important contribution of the said model compared to other research efforts on the topic is the joint consideration of both fossil and renewable power generation technologies. All while implementing a granularity that covers: the different macroscopic tariffs and consumer segments in which both the energy and water are organized at a regional level for purposes of demand quantification and pricing calculation, the various possible cooling technologies that would be used in the appropriate power generation technologies to calculate water consumption, additional water consumption coefficients that describe the different food products that are produced in the region, additional wind and solar radiation averages per municipality, per month for the calculation of the eolic, photovoltaic and thermosolar technologies. Lastly, although possible, the off-grid power generation system has the consequence of more water and environmental accountability in the region. Nonetheless, it increases energy, water, and food security within the MMA. This would also mean a total disconnection to the CFE and the national power grid, in contrast to the hybrid system scenario, which includes almost all CFE-generated energy.
  • Tesis de maestría
    A work on optimizers for binarized neural networks: a second order approach
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-11) Suárez Ramírez, Cuauhtémoc Daniel; Gonzalez Mendoza, Miguel; tolmquevedo; Ochoa Ruiz, Gilberto; Morales González Quevedo, Annette; Sanchez Castellanos, Hector Manuel; School of Engineering and Sciences; Campus Monterrey; Chang Fernández, Leonardo
    Optimization of Binarized Neural Networks (BNNs) relies on approximating the real-valued weights with their binarized representations. Current techniques for weight-updating uses the same optimizers as traditional Neural Networks (NNs). There has only been one effort to directly train the BNNs with bit-flips by using a raw first moment estimate of the gradients and comparing it against a threshold for deciding when to flip a weight (Bop). In this thesis, we iteratively improve this approach by drawing parallels to the Adam optimizer with the inclusion of a second raw moment estimate to normalize the average of the gradients before doing the comparison with a threshold (Bop2ndOrder). Additionally, we tested the effect of using a scheduler on the threshold value as an equivalent to a regularizer, along with bias-corrected and not corrected versions of the optimizer. The proposed optimizer was tested using three different architectures with CIFAR-10 and Imagenet2012; in both datasets this proved to converge faster, being more robust to changes of the hyper-parameters, and achieving better accuracies. Moreover, we also proposed a proof of concept Probabilistic Binary Optimizer (PBop) which treats each weight as loaded coins (Bernoulli distribution) proving that, even though the results are not on par with state-of-the-art, the concept is feasible for Image Classification although it requires a deep exploration of the effect of the scaler.
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