|Ph.D Student||Dassau Eyal|
|Subject||Yield Enhancement in Bioprocessing through Integrated|
Design and Control
|Department||Department of Chemical Engineering||Supervisor||Professor Daniel Lewin|
Controlling bioprocesses at their optimal states should be of considerable interest to the bio-tech industry since it enables the reduction of production costs and the increase of yields while at the same time maintaining quality. As estimated by the Food and Drug Administration (FDA), poor quality design is responsible for more than 40% of product recalls.
This work presents two main contributions to influence and improve process design and product quality. The first is a novel plantwide Process Systems Engineering (PSE) concept that integrates process design and control with six-sigma methodology as a tool to find bottlenecks and overcome them, with the main intention being to enhance yield of bioprocesses, specifically in the pharmaceutical industry. The second one is optimization-based root cause analysis to improve the search for the root cause of poor process performance as part of the six-sigma methodology. These contributions were realized using Matlab® and Simulink® based on first-principles modeling and physical knowledge on two examples: a section of the Penicillin production process, including the fermentation step and the first product purification stage, and Aspergillus nigger fermentation.
Appling the PSE concept along with optimization-based root cause analysis on the Penicillin production process reduces the batch time by 64%, increases the product purity by 45% and improves the throughput yield by 25%. In the Aspergillus nigger case study, the RCA mechanism generates a modified design that not only produces a higher concentration of the desired product without significant change to the critical-to-quality (CTQ) variables, but is obviously a cost-effective one since less supporting equipment is needed.
These contributions can best serve business targets, capable of improving process quality, yield and ultimately speeding-up production time. This can make a difference in the pharmaceutical industry in terms of product quality, investment and time. A process that will show lower defects-per-million-opportunities (DPMO) level will receive faster approval by the FDA, which translates directly to a faster return on investment. Generalization of this methodology to other chemical processes or applications is relatively straightforward and is strongly recommended