Examinando por Autor "Restrepo Lopera, Juan Pablo"

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    Comparison of PBM and ANPM models for predicting grinding product size distributions
    (Universidad EAFIT, 2020) Luján González, Juan Camilo; Restrepo Lopera, Juan Pablo; Builes Toro, Santiago
    Grinding is a very important industrial operation that draws up to 4% of the global electricity consumption. It is imperative to predict accurately the appropriate retention times necessary for a given size reduction to minimize the wasted energy invested in overgrinding. However, the most common models for scaling, such as Bond, could lead to a design risk on the order of ± 20% due to their assumption that a single particle size can describe the entire particle size distribution. Thus, different approaches (both phenomenological and non- phenomenological) need to be explored. In the present work, a population balance model is compared with an algebraic statistical model, to predict the evolution of particle size distribution over time, assessing them in terms of accuracy, robustness, and computational complexity. Even though the population balance model had a lower accuracy and higher mathematical complexity its predictions were physically coherent, which made it a more robust model for extrapolating to different initial conditions and milling times. It is important to note that due to the 2020 COVID-19 pandemic, experimental information was limited, which inhibited an independent validation of the models, and an overfitting analysis for the ANPM.
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    Nonparametric Generation of Synthetic Data Using Copulas
    (Universidad EAFIT, 2023) Restrepo Lopera, Juan Pablo; Laniado Rodas, Henry; Rivera Agudelo, Juan Carlos
    This article presents a novel nonparametric approach to generate synthetic data using copulas, which are functions that explain the dependency structure of the real data. The proposed method addresses several challenges faced by existing synthetic data generation techniques, such as the preservation of complex multivariate structures presented in real data. By using all the information from real data and verifying that the generated synthetic data follows the same behavior as the real data under homogeneity tests, our method is a significant improvement over existing techniques. Our method is easy to implement and interpret, making it a valuable tool for solving class imbalance problems in machine learning models, improving the generalization capabilities of deep learning models, and anonymizing information in finance and healthcare domains, among other applications.
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    Proceso de ASC - INTEGRACION DE PLANTA A ESCALA DE LABORATORIO PARA LA CAPTURA DE CO2 POR ABSORCION-DESORCION CON AMINAS ACUOSAS
    (Universidad EAFIT, 2019) Román Restrepo, Valeria; Restrepo Lopera, Juan Pablo; Betancur Osorio, Camilo; Sepulveda García, Yessenia; Arboleda Otero, Mariana; Builes, Santiago; Universidad EAFIT
    The increasing use of fossil fuels for energy generation and the high emission of greenhouse gases associated with these processes have made it necessary to develop technologies that can mitigate their impact. The chemical absorption process of CO2 using aqueous amines has been implemented in the industry for several decades and is considered a viable strategy for medium-term CO2 emissions mitigation. Pumping highly viscous fluids such as amines requires large amounts of energy, and they are commonly mixed with water to improve their fluidity. This mixture negatively impacts reaction rates, resulting in the need for larger equipment to capture the same amounts of CO2. Additionally, the regeneration process of aqueous amines requires the supply of large amounts of energy to increase their temperature and release the CO2. These kinetic and energetic characteristics of the process are the main obstacles to the widespread implementation and extensive use of this technology in the industry. This work continues the construction process of a plant for chemical absorption using aqueous amines. It builds upon the construction of a gas capture plant that took place in 2018, aiming to operate in a steady state and monitor the performance of different types of amines, amine mixtures, and operational conditions of the system in order to find alternatives that can reduce the energy cost of the process.