EAGER: Cybermanufacturing: Advanced Modeling and Information Management in Pharmaceutical Manufacturing

EAGER：网络制造：药品制造中的高级建模和信息管理

• 批准号: 1547171
• 负责人: Marianthi Ierapetritou
• 金额: $ 28.42万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547171&HistoricalAwards=false
• 关键词: EAGER; Cybermanufacturing; Advanced; Modeling; Information

Ierapetritou, 1547171The overall objective of this project is to develop a data-enabled computational framework for the efficient design and improved operation of pharmaceutical processes using advances in cyberinfrastructure (CI). At present, product/process design in the pharmaceutical industry is largely performed in an empirical manner relying primarily on heuristic experimentation - hence the alarm raised by the FDA in the Critical Path Initiative to transition toward a more Quality-by-Design (Qbd) paradigm. For such QbD-based decision making to be practically applicable in the pharmaceutical industry, a robust computational framework is required. The proposed framework will allow the seamless integration of high-fidelity simulations, experimental data and physical sensors with a runtime system that supports the dynamic execution of model simulations, validation and refinement, on advanced computing platforms.Intellectual Merit :Motivated by the these considerations, this work will target the following specific aims: 1) CI enabled multi-scale process modeling of an integrated production line; and 2) model integration within a pilot-plant experimental facility and real-time refinement of the multi-scale model. A pilot plant available to the PIs via the NSF-ERC on structured organic particulate systems and will allow the validation and testing in a realistic setting. The work should lead to several theoretical advances namely: 1) an efficient method to develop a multi-scale model of a mixer-granulator process, and 2) strategies to integrate developed models with physical sensors and processes, experimental data, in combination with a robust computational framework. The proposed approach will result in flexible solutions for decision-makers as they can utilize the developed framework as a virtual experimental toolkit to perform what-if-scenarios in silico to obtain optimal operating conditions, prior to implementation in the real plant. Broader Impacts :Research findings can be used to enhance the profitability and sustainability of many industries that deal with particulate processes, thus directly impacting the US economy including food, pharmaceutical, and chemical industries. Software prototypes and a library of solutions to problems developed during the project will be made available for other researchers in the field to use and improve upon. The PIs will integrate research findings into the current undergraduate design course. This will enable seniors to not only work on current chemical/biochemical problems but also on problems relevant to the predominantly particulate-based industries that surround Rutgers and are critical to the New Jersey economy. Co-PI Jha will also introduce a new elective in ECE titled "Advanced High-Performance and Distributed Computing" and will use research problems and findings as case-studies. The PIs will work with industrial collaborators involved in this proposal to obtain realistic case studies that are highly industrially relevant, thereby increasing the employability of the graduating senior class. To encourage under represented groups, the PIs will also work with minority societies within Rutgers, (National Society of Black Engineers), and the Douglas Science Institute for Women, which have established programs in place, to expose and thereafter recruit qualified women and minority students at the graduate level.

Ierapetritou，1547171 该项目的总体目标是开发一个数据驱动的计算框架，利用网络基础设施 (CI) 的进步来高效设计和改进制药流程的操作。目前，制药行业的产品/工艺设计主要以经验方式进行，主要依靠启发式实验 - 因此 FDA 在关键路径倡议中发出警报，要求向更加注重设计质量 (Qbd) 范式过渡。为了使这种基于 QbD 的决策在制药行业中实际应用，需要一个强大的计算框架。所提出的框架将允许高保真模拟、实验数据和物理传感器与运行时系统无缝集成，该运行时系统支持在先进计算平台上动态执行模型模拟、验证和细化。智力优点：受这些考虑因素的启发，这项工作将实现以下具体目标：1）CI 支持集成生产线的多尺度流程建模； 2）中试装置内的模型集成和多尺度模型的实时细化。通过 NSF-ERC 向 PI 提供结构化有机颗粒系统试点工厂，并将允许在现实环境中进行验证和测试。这项工作应该带来几项理论进展，即：1）开发混合造粒机过程多尺度模型的有效方法，2）将开发的模型与物理传感器和过程、实验数据结合起来的策略强大的计算框架。所提出的方法将为决策者带来灵活的解决方案，因为他们可以利用开发的框架作为虚拟实验工具包，在计算机中执行假设场景，以获得最佳操作条件，然后再在实际工厂中实施。更广泛的影响：研究结果可用于提高许多处理颗粒过程的行业的盈利能力和可持续性，从而直接影响美国经济，包括食品、制药和化学工业。项目期间开发的软件原型和问题解决方案库将可供该领域的其他研究人员使用和改进。 PI 将把研究成果整合到当前的本科生设计课程中。这将使老年人不仅能够解决当前的化学/生化问题，而且还能解决与罗格斯大学周围的主要以颗粒物为基础的行业相关的问题，这些行业对新泽西州的经济至关重要。联合 PI Jha 还将介绍 ECE 中的一门新选修课，题为“高级高性能和分布式计算”，并将使用研究问题和发现作为案例研究。 PI 将与参与该提案的工业合作者合作，以获得与工业高度相关的现实案例研究，从而提高应届毕业生的就业能力。为了鼓励代表性不足的群体，PI 还将与罗格斯大学内的少数群体协会（全国黑人工程师协会）和道格拉斯妇女科学研究所合作，这些机构已经制定了适当的计划，以揭露并招募合格的女性和少数族裔学生在研究生阶段。