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Control and Optimization of Biomanufacturing Systems

生物制造系统的控制和优化

基本信息

批准号：
1334933
负责人：
Ananth Krishnamurthy
金额：
$ 32.5万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-01 至 2018-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1334933&HistoricalAwards=false
关键词：
Control Optimization Biomanufacturing Systems

项目摘要

The objective of this award is to develop theories and methods for the control and optimization of bio-manufacturing systems. The research will address issues related to upstream bio-manufacturing processes (where cell cultures secrete proteins through fermentation) and downstream bio-manufacturing processes (consisting of purification steps such as chromatography and filtration). At each step, the yield, throughput, and quality of individual production runs are subject to significant variability due to time varying nature of the cell lines, complex nature of the underlying biological reactions, and stochastic failures due to process uncertainty. The goal of this research is to integrate the theories of the underlying chemical and biological processes with the principles from uncertainty theory and optimization, and develop a knowledge base that will enable improvements yield, quality and throughput of bio-manufacturing operations.If successful, this research will provide decision support to help bio-manufacturers improve efficiency, reduce timelines, and costs. For the upstream bioreactor operations, new stochastic optimization models will be developed to capture the trade-offs related to operating policies and their impact on quality, yield, and total costs. For the downstream operations, new performance evaluation models will be developed to analyze trade-offs related to downstream parameters, such as, the quality of the batch, the resin binding capability, purification cycle times, and efficiency. Leveraging the functional relationships obtained from specialized models for upstream and downstream operations, the research will develop mathematical models for optimizing system design and production schedules. All models will be validated with industry partners and insights obtained from this research will be incorporated into practice. The methodologies developed in this research have potential applications in other emerging areas of advanced manufacturing such as bio-fuels and nano-manufacturing. The research will also train several students and influence university curricula in related areas.

该奖项的目的是开发生物制造系统控制和优化的理论和方法。该研究将解决与上游生物制造过程（细胞培养物通过发酵分泌蛋白质）和下游生物制造过程（包括色谱和过滤等纯化步骤）相关的问题。在每个步骤中，由于细胞系的时变性质、基础生物反应的复杂性质以及由于工艺不确定性导致的随机故障，单个生产运行的产率、通量和质量会发生显著变化。本研究的目标是将不确定性理论和优化原理与潜在的化学和生物过程的理论相结合，并开发一个知识库，以提高生物制造操作的产量，质量和产量。如果成功，本研究将提供决策支持，以帮助生物制造商提高效率，缩短时间和成本。对于上游生物反应器操作，将开发新的随机优化模型，以捕获与操作政策及其对质量、产量和总成本的影响相关的权衡。对于下游操作，将开发新的性能评估模型，以分析与下游参数相关的权衡，例如批次质量、树脂结合能力、纯化周期时间和效率。利用从上游和下游操作的专门模型中获得的功能关系，研究将开发用于优化系统设计和生产计划的数学模型。所有模型都将与行业合作伙伴进行验证，从这项研究中获得的见解将被纳入实践。在这项研究中开发的方法在先进制造业的其他新兴领域，如生物燃料和纳米制造有潜在的应用。这项研究还将培训几名学生，并影响相关领域的大学课程。