Villanueva López, Vladimir

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    Experimental and numerical study of the continuous fluidized bed dryer
    (2016) Villanueva López, Vladimir; Velázquez Figueroa, Carlos; College of Engineering; Bogere, Moses N.; Estévez De Vidts, Luis A.; Department of Chemical Engineering; Castillo, Paul
    Nowadays, batch fluid-bed drying is one of the most efficient drying methods for particulate material; but at the same time, it is the most energy consuming operation in the manufacturing of solid dosage forms. To minimize cost and speed up the production of pharmaceutical products, the continuous manufacturing emerges as an important alternative. The advantages of this technology in comparison to the batch fluid bed dryer, which is the most predominant in the industry is to reduce cycle times, optimize for faster production, guarantee real-time quality assurance. This work was oriented to understand the continuous fluidized bed drying phenomena by using numerical simulations and experimentation. I designed a prototype of a continuous fluid bed dryer by using computer aided design CAD coupled with computational fluid dynamic simulations CFD. The equipment promotes back-mixing and the transportation of the particles through the units, by the momentum exerted by the inlet airflow without requiring a mechanical assistant to exit. The effect of the inlet air velocity and inlet air temperature were evaluated at different initial moisture contents of the lactose granules. A two-phase model proposed by Burgschweiger and Tsotsas[1] included in the processing system engineering tool gSOLIDS for PSE Enterprise was used to understand the interaction of the process parameters with the performance of the novel continuous fluid bed dryer. The two mass transfer correlations were evaluated to describe the drying kinetics of the particles in the emulsion phase. It was found that the inclusion of the mass transfer correlation proposed by Rhode, result on better predictions of the moisture content of the granules at the outlet of the continuous fluid bed dryer. Finally, the coupling of computation fluid dynamics with discrete elements methods simulations was used to visualize the fluidization patterns inside the equipment. In this way was possible to visualize the residence time of the particles.
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    Development of sensitive analytical methods based on quantum cascade laser spectroscopy
    (2021-07-09) Villanueva López, Vladimir; Hernández Rivera, Samuel P.; College of Arts and Sciences - Sciences; Guzmán Martínez, Aikomari; Mina Camilde, Nairmen; Santana Vargas, Alberto; Department of Chemistry; Rodríguez Román, Daniel
    Quantum cascade laser (QCL) technology has enabled the development of more sensitive analytical methods based on mid-infrared spectroscopy due to high brightness and spectral resolution. The advantages of this technology have been proved in material characterization. Still, it is necessary to gather a more profound knowledge of its capability through practical applications to exploit at the maximum level its numerous advantages. This dissertation provides a framework on how to develop analytical methods based on QCL from the development of software based on LabVIEW to acquire spectroscopic data, process the signals, and finally the analysis of the spectroscopic data using multivariate analysis (MVA) methods and machine learning (ML) algorithms. The angle of incidence is an important factor that significantly affects reflectance measurements. The effect of the changes in this critical parameter was evaluated. First, experiments performed at a near-grazing angle of incidence enable reflection-absorption infrared spectroscopy (RAIRS). Under that configuration, detecting analytes at trace levels, such as high explosives (HEs), including PETN, RDX, and Tetryl, was successful. The quantification of C-4 was achieved using partial least square regression (PLS) analysis. Then the experiments changing the angle of incidence and the substrate where the HEs were deposited were conducted to evaluate the capabilities of advanced machine learning algorithms to classify them. The Naive Bayes classifier showed the highest discriminating capabilities with a 96.9% probability of detection and 93.3% precision. The detection of HEM was successful. Finally, diffuse reflectance measurements in back-reflection mode, with laser beam incidence parallel to surface normal, were conducted to develop an analytical method for quantifying active pharmaceutical ingredients (APIs) in tablets and powder mixes with excipients. This optical configuration allows deeper penetration in compressed tablets. Therefore, larger volumes of the analyte can be scrutinized. The experimental studies were focused on the analysis of condensed phases, but they can be extended to gas-phase/vapors sensing due to the high spectral resolution of QCL.