Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551014
Pertenecen a esta colección Tesis y Trabajos de grado de los Doctorados correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- A methodology to optimize water networks in buildings using digital technologies(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-04) Cortez Lara, Pedro Maximiliano; Barrios Piña, Héctor Alfonso; emimmayorquin, emipsanchez; Mora Polanco, Abrahan Rafael; Rangel Ramírez, José Guadalupe; Bustamante Bello, Martín Rogelio; Escuela de Ingeniería y Ciencias; Campus Puebla; Sánchez Andrade, BenjamínRecent advances in construction Digital Technologies (DT) have renewed interest in Building Information Modeling (BIM). At the same time, concerns about the environmental impact of the building sector continue to grow. Yet, studies that link BIM with Water Efficiency Analysis (WEA) remain limited. Most available work offers only simplified assumptions, partial simulations, or narrow approaches focused on specific modeling tasks. As a result, many buildings still rely on oversized Water Networks (WN). These systems increase carbon and water footprints and pose sanitary risks due to long periods of water stagnation. This research seeks to address these issues by developing a method to improve WN through DT while reducing their environmental impact. The study adopts a mixed approach that integrates BIM, Metaheuristics, and Input–Output (IO) analysis. The first part of the study analyzed the influence of Peak Water Demand (PWD) in WN and introduced a procedure to estimate it using standardized information. The method was evaluated through a residential case study. The results showed that the proposed approach provides consistent PWD estimates and performs better than the methods currently used in practice. The predicted demand was significantly lower, with values that were about 2.6 times smaller than those obtained through traditional procedures. The method also produced results that were close to the measurements collected on site. Even so, its purpose is not to replicate the exact observed values. A perfect match could reduce the safety margin and lead to undersized systems that fail during unusual or high-demand conditions. The second stage evaluated a methodology to integrate WEA within a BIM environment. Autodesk Revit was chosen as the primary platform because it is widely adopted and can connect different digital tools through a single model. The three proposed domains showed consistent improvements in water savings and reductions in electrical power. Their structure and customization increase the modularity of the methodology. This allows the process to adapt to projects of different scales while keeping a clear and practical workflow. These features help designers and professionals identify relevant elements and parameters early in the design phase. This leads to better water use outcomes and improves the performance of the WN throughout the following stages of the project. The third stage focused on creating a BIM-Metaheuristics algorithm to optimize WN. This part of the research stands out because it requires low technological resources while maintaining high precision in the selection of optimal pipe diameters. The method incorporates environmental factors and hydraulic constraints in a single optimization process. The results indicate that the model can reduce pipe sizes by one nominal diameter in most cases. In more demanding scenarios, the reduction can reach two diameters. These adjustments are obtained while decreasing environmental impact, lowering costs, and minimizing computational demands. The approach is flexible and can be applied to a wide range of building contexts. It consistently produces optimal configurations for WN. This contributes to a shift in how environmental performance is evaluated in plumbing design. This stage also explores the blue and carbon footprint assessment-based BIM-Input Output (BIM-IO) using Multifunctional Analysis of Regions Through Input-Output (MARIO) tool. The proposed framework demonstrated simplicity and ease of use for assessing Blue and Green water footprints in buildings using MARIO and BIM. BIM’s applicability across various building environments enables extensive data extraction. This includes detailed information from systems such as structure, architecture, HVAC, and plumbing. The level of detail depends on the Level of Detail (LOD) used in the model. The outcomes from the third stage were analyzed to determine the environmental impact and development of new policies.
- Designing a disaster relief supply chain network for pandemics(2025-06-17) Mosalla Nezhad, Behzad; Hajiaghaei-Keshteli, Mostafa; emimmayorquin; Campus MonterreyPandemics, such as the COVID-19 pandemic (2020-2022), have exposed critical vulnerabilities in supply chains and logistics systems, emphasizing the need for resilient and sustainable solutions. Efficient and fair distribution of essential resources, effective waste management, and prioritization of vulnerable populations are critical to pandemic responses. Existing supply chains often struggle with challenges such as spikes in demand for essential items and inequitable resource distribution. This dissertation addresses these gaps by developing deterministic and stochastic mathematical models, advanced optimization techniques, and integrating new technologies, such as Internet of Things (IoT) platforms, to improve supply chain resilience, sustainability, and adaptability during crises, with a specific focus on managing critical resources and waste. Building on this foundation, this dissertation is organized into four studies, each addressing methods and solutions to tackle challenges in pandemic logistics. In the first study, presented in Chapter 2, a bi-objective mathematical model was developed to optimize the distribution of personal protective equipment (PPE) during the COVID-19 pandemic. The model optimizes both cost minimization and demand fulfillment, employing advanced metaheuristics such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a hybrid metaheuristic approach. Results demonstrate enhanced efficiency and equity in PPE allocation compared to traditional distribution methods. Chapter 3 presents the second study, which develops an Internet of Things (IoT)-enabled reverse supply chain (RSC) for managing COVID-19-related medical waste during the COVID-19 pandemic. The network aims to enhance safety, minimize costs, and promote sustainability in the collection, transportation, treatment, and recycling of waste. IoT devices provide real-time data to estimate waste generation and optimize network operations. A metaheuristic framework addresses the computational complexity of the model, delivering efficient solutions. The model is validated using real-world data from Puebla, Mexico. The third study, presented in Chapter 4, explores the challenges pandemics pose to supply chains, highlighting the need for robust relief networks to distribute supplies effectively and manage disruptions. A multi-objective sustainable network model is proposed to streamline the flow of relief supplies and manage waste. This model aligns with the Sustainable Development Goals (SDGs) by emphasizing energy-efficient production and transportation. The model incorporates real-time data through the Internet of Medical Things (IoMT), which connects medical devices to enhance data collection and sharing for improved network coordination. Due to the NP-hard nature of the problem, tuned metaheuristics are employed to validate the model across five test scenarios, ensuring its adaptability to diverse conditions. Finally, the fourth study, presented in Chapter 5, introduces a system dynamics model to simulate demand varying by age groups during pandemics. A stochastic mathematical model is also developed to prioritize vulnerable populations, such as the elderly, within sustainable humanitarian supply chain networks. By integrating system dynamics with Improved Epsilon-Constraint Methods, these models address uncertainty to ensure equitable resource distribution while managing costs and emissions to align with sustainability principles. Together, these studies offer insights and practical tools to enhance the resilience, agility, and sustainability of supply chains during pandemics, delivering actionable strategies for policymakers and practitioners.
- Novel bioengineering strategies for the recovery and purification of PEGylated lysozyme conjugates: in situ ATPS and affinity chromatography(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2017-07-13) Mejía Manzano, Luis Alberto; Mejía Manzano, Luis Alberto; 375359; Rito Palomares, Marco Antonio; Mayolo Deloisa, Karla Patricia; González Valdez, José Guillermo; Asenjo de Leuze, Juan A.; Parra Saldívar, RobertoPEGylation is the modification of therapeutic proteins with polyethylene glycol (PEG) with the goal of improving their bioavailability and effectivity in the organism. During the PEGylation process, proteins with different degrees of PEGylation and positi