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dc.contributor.advisorVelázquez Figueroa, Carlos
dc.contributor.authorVargas Colón, Ariel Omar
dc.date.accessioned2018-05-16T15:46:26Z
dc.date.available2018-05-16T15:46:26Z
dc.date.issued2010
dc.identifier.urihttps://hdl.handle.net/20.500.11801/543
dc.description.abstractThe Fluidized Bed Dryer (FBD) is a common unit used in the pharmaceutical industry for its unique advantages when working with powders. With recent development in technology and environmental awareness, a desire to optimize units to improve quality and reduce energy consumption has emerged. Many researchers have been studying different sensors and control techniques that could help achieve this desire. Near Infrared Spectroscopy (NIRS) has provided an advantage over other chemical sensors for its non destructive capacity and its speed. Model Predictive Control (MPC) has been proven as an advanced control strategy capable of online continuous optimization of the process. This research uses these two techniques to optimize a Fluidized Bed Dryer process. The research revolves around a laboratory scale FBD, with the purpose of generating a NIR mathematical treatment to improve online moisture content prediction and using the NIR reading as an input to an MPC for continuous online optimization of the process. System development was needed to communicate the NIR prediction to the Distributed Control System (DCS) that governs the automation of the FBD. The algorithm used for the communication allowed the use of Matlab’s mathematical base for programming the MPC. It also served as a bridge between the NIR software and the DCS. The mathematical treatment developed through the research consisted in the implementation of 3 partial least squares (PLS) models that uses raw spectrum where each model focused on different factors inside the spectrum to predict the sample’s moisture content and the error in the prediction. This technique shows sufficient improvement on the NIR prediction to allow a good performance from the controller.
dc.description.abstractEl Secador de Lecho Fluido (FBD) es una unidad usada comúnmente por la industria farmacéutica debido a sus ventajas al manejar sistemas de particulado. El desarrollo tecnológico y la conciencia ambiental han creado el deseo de optimizar procesos que mejoren la calidad de los productos y reduzca el consumo de energía. Muchos investigadores han estudiado el uso de diferentes sensores y distintas estrategias de control con el fin de lograr este objetivo. Entre los sensores, se destaca la espectroscopia en infrarrojo cercano (NIR) debido a sus ventajas tales como rapidez, su habilidad de cuantificar y su capacidad no invasiva y no destructiva. De similar modo, el Modelo de Control Predictivo (MPC) es una estrategia avanzada de control que permite la optimización de procesos de manera continua y en línea. Esta investigación utiliza estas dos áreas como base para la optimización de un FBD. Los experimentos realizados en esta investigación fueron realizados en un FBD en una escala de laboratorio. El propósito general consistió en la generación de un tratamiento matemático para mejorar la predicción de la humedad dentro del sistema utilizando los espectros infrarrojos y utilizando estos valores para alimentar un MPC que realizaría la optimización en tiempo real. El tratamiento matemático consiste en la utilización de 3 modelos por mínimo cuadrados parciales (PLS) que utilizando los espectros, determinaran una predicción de humedad y el error en esta predicción. Este tratamiento mejoró significativamente las predicciones logrando el desempeño deseado del controlador.
dc.language.isoenen_US
dc.subjectFluidized bed dryeren_US
dc.subjectModel predictive controlen_US
dc.subjectMulti-variant near infrared spectraen_US
dc.subject.lcshFluidizationen_US
dc.subject.lcshPredictive controlen_US
dc.subject.lcshChemometricsen_US
dc.titleAutomation of a fluid bed dryer using multi-variant near infrared spectra and model predictive controlen_US
dc.typeThesisen_US
dc.rights.licenseAll rights reserveden_US
dc.rights.holder(c) 2010 Ariel Omar Vargas Colónen_US
dc.contributor.committeeCardona Martínez, Nelson
dc.contributor.committeeAcevedo Rullan, Aldo
dc.contributor.committeeOrtiz, Eduardo
dc.contributor.representativeHernández, Samuel
thesis.degree.levelM.S.en_US
thesis.degree.disciplineChemical Engineeringen_US
dc.contributor.collegeCollege of Engineeringen_US
dc.contributor.departmentDepartment of Chemical Engineeringen_US
dc.description.graduationYear2010en_US


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    Items included under this collection are theses, dissertations, and project reports submitted as a requirement for completing a degree at UPR-Mayagüez.

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