Given the potential consequences of recurring drug shortages within the U.S., there is a need for reconfigurable facilities that are capable of rapid product changeover. In a rapid therapeutics manufacturing (RTM) facility, one is concerned with the ability to perform scale-up and optimization of the batch process for the rapid production of a given product. These problems can be represented as large-scale dynamic optimization problems with uncertainty. This project seeks to develop novel optimization strategies that can solve these batch optimization problems using efficient parallel computation on emerging scientific computing architectures.
The work has the following research objectives: 1. Parallel Solution of Structured Nonlinear Programming Problems on Clusters and Multicore Machines -- focus on the development of an internal decomposition approach for block structured nonlinear programming (NLP) problems, enabling efficient parallel solution of previously intractable problems in a rigorous nonlinear framework. 2. Parallel Solution of Structured NLP Problems on Emerging Massively Parallel Architectures -- focus on extending these NLP algorithms to effectively exploit the capabilities of these affordable, massively parallel architectures. 3. Batch Process Optimization in Reconfigurable Systems for Rapid Therapeutics Manufacturing -- integrate the parallel algorithms with a modular modeling framework and develop rigorous dynamic models to support scale-up and batch optimization in reconfigurable therapeutics manufacturing facilities.
Broader Impacts:
This is an integrated plan to meet the following education goals: 1. Increasing Scientific Research Awareness Among Undergraduates -- the PI has pursued the career development of several promising undergraduates, and two undergraduates per year will be trained for a career in research. 2. Encouraging Lifelong Learning Through Active Participation in Online Technical Communities -- to encourage lifelong learning and active participation in an online technical community, the PI will integrate the use of wiki and forum software with the undergraduate course on numerical methods. Student teams will be required to contribute content and will be graded on the level of involvement and peer rating of their posted material. 3. Preparation of Graduate Students and Professionals for Effective Use and Development of the Next Generation of PSE Tools -- the PI will develop a new graduate level course designed to demonstrate the potential of modern computing architectures for scientific computing. In addition, a short course will be developed to be taught at Lund University (Sweden) and within the National Center for Therapeutics Manufacturing at Texas A&M University. 4. Promoting Research Careers Among Underrepresented Groups -- the PI will work actively with the Louis Stokes Alliance for Minority Participation (LSAMP) at Texas A&M University as a research mentor to encourage minority undergraduates and Masters students to pursue doctoral studies and a career in research.
This research has the potential to significantly improve resiliency in the therapeutics manufacturing industry through its impact on rapid manufacturing. In addition, the novel parallel algorithms are general in nature and will be made available through the COIN-OR open-source foundation to be used by other researchers to tackle previously intractable nonlinear optimization problems. Research results will be disseminated in refereed journals and at national conferences.
