EAGER:REAL-D: Smart Decision Making using Data and Advanced Modeling Approaches

EAGER:REAL-D：使用数据和高级建模方法进行智能决策

• 批准号: 1839007
• 负责人: Rohit Ramachandran
• 金额: $ 20万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839007&HistoricalAwards=false
• 关键词: EAGER; REAL; Smart; Decision; Making

The proposed exploratory research project aims to develop a next-generation autonomous manufacturing process for pharmaceutical production that integrates product and process informatics with knowledge management. The integration of process data, process models, and information management tools will enable adaptive adjustment to the operating conditions to compensate for variability in raw materials and changing product needs. The research team will take advantage of the facilities of the Center for Structured Organic Particulate Systems (C-SOPS) at Rutgers University for proof of principle studies and generation of experimental data for advancing fundamental understanding of each process.To enable the transition towards more autonomous and de-centralized decisions across the entire manufacturing supply chain, it is imperative to develop an integrated platform to: (a) acquire data regarding process and product operations from the manufacturing facility using data historian platforms; (b) utilize the data to extract further knowledge on process understanding; and (c) use this knowledge to dynamically and adaptively improve process operations. For task (a), the use of a data management system, such as OSI PI, is proposed with the ability to receive data from multiple sources including the control platform as well as the Process Analytical Technology (PAT) data management tool. This platform has the capability to build up recipe hierarchical structure using Event Frame functionality and periodically push the data into a cloud system for permanent enterprise-wide data storage and efficient sharing. For task (b), the use of advanced statistical and machine learning methods is proposed, in combination with data reconciliation methods. Finally, for task (c), information acquired will be utilized to adapt the model feasible space by building accurate surrogate models and adaptively refine them using the online data acquisition. Although the focus will be on pharmaceutical production processes, the proposed work, if successful, can have significant broader impacts on a variety of industrial processes. Two PhD students will be trained on the development of a cutting-edge framework for autonomous manufacturing processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

拟议的探索性研究项目旨在开发下一代自主制药生产工艺，将产品和工艺信息学与知识管理相结合。过程数据、过程模型和信息管理工具的集成将能够对操作条件进行适应性调整，以补偿原材料的可变性和不断变化的产品需求。研究小组将利用罗格斯大学结构有机微粒系统中心（C-SOPS）的设施，对原理研究进行验证，并生成实验数据，以推进对每个过程的基本理解。为了使整个制造供应链向更加自主和分散的决策过渡，必须开发一个集成平台来：(a)使用数据历史平台从制造设施获取有关过程和产品操作的数据；(b)利用数据进一步提取有关过程理解的知识；(c)利用这些知识动态地、自适应地改进工艺操作。对于任务(a)，建议使用数据管理系统，例如OSI PI，具有从多个来源接收数据的能力，包括控制平台以及过程分析技术（PAT）数据管理工具。该平台具有使用Event Frame功能构建配方分层结构的能力，并定期将数据推送到云系统中，以实现企业级的永久数据存储和高效共享。对于任务(b)，建议使用先进的统计和机器学习方法，并结合数据调和方法。最后，对于任务(c)，将利用获取的信息通过构建准确的代理模型来适应模型可行空间，并利用在线数据采集自适应地改进模型。虽然重点将放在药品生产过程上，但拟议的工作如果成功，可以对各种工业过程产生重大而广泛的影响。两名博士生将接受自主制造流程前沿框架开发方面的培训。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Accelerating multi-dimensional population balance model simulations via a highly scalable framework using GPUs

• DOI: 10.1016/j.compchemeng.2020.106935
• 发表时间: 2020-09
• 期刊: Comput. Chem. Eng.
• 作者: Chaitanya Sampat;Y. Baranwal;R. Ramachandran