Date of Award
8-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Chemical Engineering
Committee Chair/Advisor
Scott M. Husson
Committee Member
Eleanor W. Jenkins
Committee Member
Marc R. Birtwistle
Committee Member
Eric M. Davis
Abstract
This dissertation describes developing and implementing computational frameworks for simulating and optimizing purification processes in the biopharmaceutical industry. The framework performs techno-economic analyses to establish value propositions for new process alternatives, especially membrane technologies. Initially, the focus is developing a framework capable of simulating monoclonal antibody (mAb) capture using membrane and resin media in multi-column chromatography (MCC) platforms for continuous manufacturing. Subsequently, the impact of capture MCC is compared against other intensification strategies in established mAb manufacturing facilities. Finally, the framework application expands to simulate the purification of adeno-associated virus (AAV) vectors for gene therapy.
Chapter 2 details the framework structure, which integrates different software components to perform simulation and optimize key performance indicators (KPI). Mechanistic models validated using experimental data are used for affinity chromatography simulation. A flowsheet simulation is used for economic and scheduling calculations. Genetic algorithms perform multi-objective optimization of KPI, enabling process comparison at optimal operating conditions. A case study compares six alternatives (membranes and resin chromatography for disposable and reusable batch platforms), revealing trade-offs between cost, process time, and buffer utilization.
In Chapter 3, this framework is improved by using a library of breakthrough curves to generate surrogate functions, reducing calculation times by 92%. This strategy enables the framework to handle additional variables and objectives without increasing computational resource demands. This key feature allows the framework to simulate and optimize MCC capture processes in Chapter 4. In this study, surrogate functions for different media are generated, and the cost model is extended to include labor and facility costs. LASSO regression guides variable selection in formulating the optimization problems. The multi-objective optimization results highlight performance differences between membrane and resin platforms, demonstrating the potential economic advantages of membrane adsorbers under specific circumstances.
Chapter 5 assesses the techno-economic impact of process changes in real manufacturing sites, comparing MCC with other intensification strategies. The study finds that scheduling practices significantly impact productivity, while MCC greatly affects operating costs.
Chapter 6 adapts the framework to AAV vector purification using membrane technology. The adapted framework simulates harvesting by tangential flow filtration, capture by affinity membrane chromatography, and polishing by ion-exchange chromatography. It performs sensitivity analyses to assesses the impact of membrane parameters on KPI, revealing possible paths for process and product development.
In summary, this dissertation establishes flexible, robust computational frameworks for techno-economic assessment and optimization of purification processes, highlighting the benefits of membrane technologies in specific scenarios. These frameworks are adaptable and accessible tools for evaluating the impact of new technologies on process performance, considering the needs and resources of future users.
Recommended Citation
Romero Conde, Juan Jose, "Frameworks for the Techno-Economic Assessment of Membrane-Based Bioprocessing Platforms" (2024). All Dissertations. 3704.
https://open.clemson.edu/all_dissertations/3704
Author ORCID Identifier
0009-0003-4433-2971