CAREER: Parallel Nonlinear Programming Techniques for Optimization in Rapid Therapeutics Manufacturing

职业：用于快速治疗制造优化的并行非线性编程技术

• 批准号: 1418972
• 负责人: Carl Laird
• 金额: $ 11.35万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1418972&HistoricalAwards=false
• 关键词: CAREER; Parallel; Nonlinear; Programming; Techniques

0955205LairdIntellectual Merit: 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 andMulticore 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 ParallelArchitectures -- 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 TherapeuticsManufacturing -- 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 TechnicalCommunities -- 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 workactively 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.

0955205 Laird智力优势：考虑到美国经常出现药物短缺的潜在后果，需要能够快速更换产品的可重新配置的设备。在快速治疗剂制造（RTM）设施中，人们关注的是进行用于给定产品的快速生产的批量过程的放大和优化的能力。这些问题可以表示为具有不确定性的大规模动态优化问题。本项目旨在开发新的优化策略，在新兴的科学计算体系结构上使用高效的并行计算来解决这些批优化问题。结构化非线性规划问题在集群和多核机器上的并行求解--专注于块结构化非线性规划（NLP）问题的内部分解方法的发展，使以前难以解决的问题在严格的非线性框架下得到有效的并行求解.新兴大规模并行架构上结构化NLP问题的并行解决方案--专注于扩展这些NLP算法，以有效地利用这些负担得起的大规模并行架构的功能。批量过程优化可重构系统快速治疗制造-集成并行算法与模块化建模框架和开发严格的动态模型，以支持规模和批量优化可重构治疗制造facility.Broader影响：这是一个综合计划，以满足以下教育目标：1。提高大学生的科研意识--PI已经对几名有前途的大学生进行了职业发展，每年将培养两名大学生从事研究工作。通过积极参与在线技术社区鼓励终身学习-为了鼓励终身学习和积极参与在线技术社区，PI将把wiki和论坛软件的使用与数值方法的本科课程结合起来。学生团队将被要求贡献内容，并将根据参与程度和他们发布的材料的同行评级进行评分。为研究生和专业人员有效使用和开发下一代PSE工具做准备-PI将开发一个新的研究生课程，旨在展示现代计算架构在科学计算中的潜力。此外，还将在瑞典隆德大学和得克萨斯A M大学国家治疗药物制造中心开设短期课程。促进弱势群体的研究事业-PI将积极与德克萨斯州A M大学的Louis Stokes少数民族参与联盟（LSAMP）合作，作为研究导师，鼓励少数民族本科生和硕士生攻读博士学位和从事研究工作。这项研究有可能通过对快速制造业的影响，显着提高治疗制造业的弹性。此外，新的并行算法本质上是通用的，将通过COIN-OR开源基金会提供，供其他研究人员使用，以解决以前棘手的非线性优化问题。研究结果将在经评审的期刊和国家会议上传播。