Phenomenological and residence time distribution models for unit operations in a continuous pharmaceutical manufacturing process
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Escotet Espinoza, Manuel.
Phenomenological and residence time distribution models for unit operations in a continuous pharmaceutical manufacturing process. Retrieved from
https://doi.org/doi:10.7282/T3Q81HHPExport
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TitlePhenomenological and residence time distribution models for unit operations in a continuous pharmaceutical manufacturing process
Date Created2018
Other Date2018-05 (degree)
Extent1 online resource (xvi, 295 p. : ill.)
DescriptionInterest in continuous pharmaceutical manufacturing (CPM) technology is rapidly growing, with all major pharmaceutical companies developing products in their pipelines using this technology. As it has been extensively reported, CPM can deliver enormous advantages including faster product development, less material use, reduced capital cost due to small equipment size, superior process control, optimized performance, and more reliable quality manufacturing. Nevertheless, given the novel and complex nature of the technology, CPM systems require further study compared to traditional batch processes. CPM studies must be carefully designed, optimized, validated, and controlled as holistic system in order to operate robustly, efficiently, and provide the aforementioned advantages. To achieve CPM’s advantages in full, it is necessary to develop and implement a framework wherein the processes can be evaluated and studied as integrated systems. In this work, tools established in the process systems engineering (PSE) methodology were implemented to develop models that can aid CPM process design, evaluation, control, and optimization. The focus of this work included the development and implementation of computationally efficient phenomenological and residence time distribution models for systems in a CPM system. In the first two chapters of this work, a thorough review of the current implementation of models in the pharmaceutical industry is presented. Within the review, the different types of models currently implemented in the industry are enumerated followed by the challenges of their implementation. Among some of the most difficult challenges for modeling CPM powder-based systems is the ability to determine relationships between critical process inputs and outputs, and the ability capture the impact of material properties on the process. To overcome these challenges a framework for developing predictive phenomenological (i.e., engineering) models that include the effect of material properties on the process was developed. The third and fourth chapters of this work are devoted to describing the model development framework and provide an example case study of the methodology when it was successfully applied to a tablet compaction process. The successful integration of material property effects into the modeling of the pharmaceutical unit operation led to the development of a material property library that collected a wide array of property measurements for a number of pharmaceutically relevant materials. The material property library, described in the fifth chapter of this work, was used as a tool to determine the impact of material properties on: (1) residence time distribution experiments and (2) the operation of continuous powder feeding units. Residence time distribution (RTD) methods and models were studied in this work, as their application to characterize CPM systems has become standard. The effect of material properties on RTD methods were evaluated in the sixth chapter to provide recommendations for using the RTD methodology to characterize CPM units. Ultimately, the unit operation characterization and modeling framework presented in this work along with the recommendations offered for RTD experimentation and modeling were applied to the development of a dynamic phenomenological and RTD model for a continuous powder feeding unit. The model, described in the seventh chapter of this work, was used to predict the behavior of the CPM-specific unit over a wide range of material property and process inputs.
NotePh.D.
NoteIncludes bibliographical references
Noteby Manuel Escotet Espinoza
Genretheses, ETD doctoral
Languageeng
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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