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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- 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 GustavoThis 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.
- 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 ValentínBioreactors 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.
- 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 MarioHeat 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.
- 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, ViridianaA 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.
- 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, PaulThe 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.
- 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, RamónThis 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.
- Optimal design of water allocation networks in highly altered basins: the guandu river case, Brazil.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12-05) Saucedo Ramírez, Oswaldo Adolfo; MAHLKNECHT, JURGEN; 120939; Mahlknecht, Jürgen; emipsanchez; Ramírez Orozco, Aldo Iván; Loge, Frank J.; School of Engineering and Sciences; Campus Monterrey; González Bravo, RamónWater scarcity is present in many regions around the world. Factors affecting water availability include, but are not limited to, population growth, resource depletion, alteration of natural ecosystems, and climate change. Said this, the study of water allocation networks is taking an important role worldwide due to the importance of the liquid for the development of human activities. The application of optimization models represents an opportunity to create new approaches to water resources management and to guarantee the sustainability of natural resources. Many previous optimization models focused on studying the hydraulic elements of the water allocation networks (e.g., pipes, pumps, and storage) to maximize economic profit or minimize distribution costs. These past approaches often neglected the hydrological aspects which describe the behavior of natural ecosystems. The aim of this research is to develop a multiobjective optimization model that incorporates parameters and equations for hydrological processes for the design of water allocation networks in highly altered basins. The model is applied to the Guandu basin in Brazil, one of the most altered watersheds worldwide (receiving 96% of its volume from surrounding basins). This basin supplies 9 million inhabitants of the Rio de Janeiro metropolitan area. Simultaneously, this basin is characterized by a strong relationship with the energy sector, i.e., around 25 % of the city's energy is produced in the basin through a hydropower complex. The results show that water transfer can be optimized by integrating water storage and reuse/recycling elements to satisfy water demands throughout the year. Also, through optimal allocation networks, it is possible to avoid saline intrusion downstream of the Guandu river, even if there is a reduction in the volume transferred from nearby basins. The developed tool is a highly feasible option for decision-making in water resources planning and management.
- 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, LeonardoOptimization 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.
- Discrete event simulation with integration of optimizations and the internet of things(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-12) García Martínez, Rubén Febronio; URBINA CORONADO, PEDRO DANIEL; 298324; Urbina Coronado, Pedro Daniel; RR; Ahuett Garza, Horacio; Orta Castañón, Pedro Antonio; Escuela de Ingeniería y Ciencias; Campus MonterreyThere is a lack of development in the discrete event simulation area. Representing a real process through a digital model has proven to be a useful tool because modifications can be carried out at no cost to corporations within a simulated environment. Even so, within the creation of DES for specific processes, it has different limitations (data collection, visual representation of the model, platform integration for the final simulation). That is why the present work proposes the integration of discrete event simulation (DES) with the environment of industrial internet of things and an industrial process. A simulated process was developed using Simpy, a Python tool, and Siemens Plant Simulation. Variables obtained from Python simulation were saved on a remote server using Google Scripts. The results of the Plant simulation were sent to an Excel data sheet. Comparisons of both simulations were performed, with variables such as cycle time, number of parts per day and other indicators. After comparing the simulations, other scenarios were tested to illustrate how the Python model can work with different data. Then, optimizations were carried out to maximize the number of items produced in different scenarios. Future work includes implementation in real factory environment and the interconnection with other technologies as augmented reality.
- A hybrid metaheuristic optimization approach for the synthesis of operating procedures for optimal drum-boiler startups(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020) Garduño Hernández, Emilio; BATRES PRIETO, RAFAEL; 589386; NOGUEZ MONROY, JUANA JULIETA; 202512; PONCE CRUZ, PEDRO; 31857; Batres Prieto, Rafael; RR; Noguez Monroy, Juana Julieta; Ponce Cruz, Pedro; Escuela de Ingeniería y Ciencias; Campus Ciudad de MéxicoA steam generator serves as a power generation equipment that uses the expansive power of the steam to generate electricity. The startup process of a steam generator plays an important role in the ability of a power plant to adjust its electricity generation to changes in demand. As renewable generation plants increase, the levels of variability in electricity production increase. Fast startups become instrumental as they enable traditional power generation plants to provide the quantity of electricity missing when variable renewable energies cannot satisfy demand. A main equipment involved in the startup process of the steam generator is the drum boiler. However, if the startup process is carried out too fast, excessive thermal stresses can occur and provoke damage to the components of the drum boiler. This thesis proposes a dynamic optimization methodology to synthesize operating valve profiles that minimize the startup time of the drum boiler while avoiding the excessive formation of thermal stresses. Since valve operations influence the time-varying behavior of the steam, dynamic simulation is needed in order to evaluate the operating procedure. This thesis proposes a dynamic optimization approach with a hybrid-metaheuristic algorithm that generates the optimal startup procedure of a drum boiler. The proposed algorithm is based on two important elements of two metaheuristic algorithms. Namely, the search zone in the cooling element from the simulated annealing algorithm and the efficient computational performance provided from the tabu search algorithm memory structures. A case study evaluates the proposed approach by comparing it against results previously published in the literature.
