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http://hdl.handle.net/11375/32365
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DC Field | Value | Language |
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dc.contributor.advisor | Mhaskar, Prashant | - |
dc.contributor.advisor | Latulippe, David | - |
dc.contributor.author | Gough, Ian | - |
dc.date.accessioned | 2025-09-23T18:19:43Z | - |
dc.date.available | 2025-09-23T18:19:43Z | - |
dc.date.issued | 2025 | - |
dc.identifier.uri | http://hdl.handle.net/11375/32365 | - |
dc.description.abstract | Integrated continuous bioprocessing (ICB) is increasingly adopted in biopharmaceutical manufacturing due to its potential to improve process economics and enhance product accessibility. A critical barrier to broader adoption is the challenge of maintaining robust operation of the integrated downstream purification processes, particularly chromatography, under variability from upstream processes like dynamic perfusion bioreactors. Managing variability is difficult as bind-elute chromatography is inherently a semi-continuous process and standard commercial equipment offers limited sensing and control capabilities. This research addresses this challenge through two complementary innovations. First, an optimal scheduling and control framework is developed for single-column bind-elute chromatography integrated with a dynamic bioreactor harvest and surge vessel. A core innovation is the application of a mixed-integer nonlinear programming formulation within an open-loop rolling horizon scheme that simultaneously optimizes the chromatography loading flow rate and duration based on dynamic process models and feedforward upstream forecasts. Critically, this approach utilizes the surge vessel as a degree of freedom to effectively buffer upstream variability and enable a variable loading flow rate strategy by decoupling upstream and downstream operations. Complementing this control strategy, a multivariate wavelength selection method is applied for selective protein quantification using the limited UV-Vis absorbance wavelengths available on standard chromatography equipment. This method improves quantification accuracy of commercially available proteins on standard equipment compared to conventional approaches. Experimental validation using perfusion permeate containing monoclonal antibody demonstrates that the control system effectively adapts the chromatography process to dynamic upstream conditions and outperforms a naïve control strategy, improving performance metrics such as increased product throughput and reduced product losses compared to a naïve control strategy. Together, these contributions provide building blocks for more flexible and robust platforms to advance the transition toward fully continuous biomanufacturing. | en_US |
dc.language.iso | en | en_US |
dc.subject | Chromatography | en_US |
dc.subject | Optimization | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Antibodies | en_US |
dc.subject | Biomanufacturing | en_US |
dc.title | An Automation Framework for Chromatography in Integrated Continuous Biomanufacturing | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Chemical Engineering | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Doctor of Philosophy (PhD) | en_US |
dc.description.layabstract | Manufacturing next-generation medicines such as monoclonal antibodies is complex as they are made by cells in a bioreactor followed by several necessary separation technologies to purify the medicine. While traditionally manufactured in batches, it is understood that moving from batch production to continuous processes can improve the economics of these medicines. However, connecting the many technologies continuously is difficult because the bioreactor production rate varies with time and the primary chromatographic separation technology is inherently a semi-continuous process. This research is aimed at addressing these challenges by developing and using advanced sensing and modeling and control techniques. In one direction, a chromatography control system was created to adapt the process to time-varying upstream conditions while considering the periodic nature of chromatography. Experiments showed this system can handle dynamic upstream conditions and yield improved performance compared to the state of the art. In another direction, a method was developed to quantify proteins using the limited UV sensors available on standard equipment. These contributions serve as building blocks to adoption of continuous manufacturing processes by improving their flexibility and robustness to dynamic conditions. | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Gough_Ian_A_202509_PhD.pdf | 5.56 MB | Adobe PDF | View/Open |
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