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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- 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 ZulemaAgri-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.
- 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 MonterreyThe 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.
- 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 MiguelIn 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.
- An integral approach for the synthesis of optimum operating procedures of thermal power plants towards better operational flexibility(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06-11) Rosado Tamariz, Erik; GANEM CORVERA, RICARDO; 60681; Batres Prieto, Rafael; emipsanchez; Ganem Corvera, Ricardo; Genco, Filippo; School of engineering and sciences; Campus Ciudad de MéxicoTo deal with the challenge of a balance between the large-scale introduction of variable renewable energies and intermittent energy demand scenarios in the current electrical systems, operational flexibility plays a key role. The electrical system operational flexibility can be addressed from different areas such as power generation, transmission and distribution systems, energy storage (both electrical and thermal), demand management, and coupling sectors. Regarding power generation, specifically at the power plant level, operational flexibility can be managed through the cyclic operation of conventional power plants which involve load fluctuations, modifications in ramp rates, and frequents startup and shutdowns. Since conventional power plants were not designed to operate under cyclic operating schemes with involve fast response times, must develop these capabilities through the design of operating procedures that minimize the time needed to take the power plant from an initial state to the goal state without compromising the structural integrity of critical plant components. This thesis proposes a dynamic optimization methodology to the synthesis of optimum operating procedures of thermal power plants which determine the optimal control valves sequences that minimize its operating times based on techniques of dynamic simulation, metaheuristic optimization, and surrogate modeling. Based on such an approach, the power plants must be increasing its operational flexibility to address a large-scale introduction of variable renewable energies and intermittent energy demand scenarios. This thesis proposes a dynamic optimization framework based on the implementation of a metaheuristic optimization algorithm coupled with a dynamic simulation model, using the modeling and simulation environment OpenModelica and a surrogate model to estimate in a computationally efficient way the structural integrity constraint of the dynamic optimization problem. Two case studies are used to evaluate the proposed framework by comparing their results against information published in the literature. The first case study focuses on managing the thermal power plant's flexible operation based on the synthesis of the startup operating procedure of a drum boiler. The second case study addresses the synthesis of an optimum operating strategy of a combined heat and power system to improve the electric power system’s operational flexibility.
- 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
- Principles to Automate Inventive Problem Solving Based on Dialectical Negation, Assisted by Evolutionary Algorithms and TRIZ(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2011-01-05) Durán Novoa, Roberto Alejandro; DURAN NOVOA, ROBERTO ALEJANDRO; 237436; Dr. Noel León Rovira; Dr. Humberto Aguallo Téllez; Dr. David Guemes Castorena; Dr. Víctor Hiram Vázquez Lasso; Dr. Eduardo Uresti CharreAn inventive problem (IP) can be defined as a human perception of a situation that has to be changed, but with at least one obstacle which impedes achievement of the desired goal. In practice, they are solved generally using random trial and error, despit
