| 项目编号: | 1547171
|
| 项目名称: | EAGER: Cybermanufacturing: Advanced Modeling and Information Management in Pharmaceutical Manufacturing |
| 作者: | Marianthi Ierapetritou
|
| 承担单位: | Rutgers University New Brunswick
|
| 批准年: | 2014
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| 开始日期: | 2015-09-01
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| 结束日期: | 2018-01-31
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| 资助金额: | USD284184
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | pharmaceutical industry
; multi-scale process modeling
; multi-scale
; advanced high-performance
; advanced computing platform
; pharmaceutical process
; pi
|
| 英文摘要: | Ierapetritou, 1547171
The 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/93439
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Marianthi Ierapetritou. EAGER: Cybermanufacturing: Advanced Modeling and Information Management in Pharmaceutical Manufacturing. 2014-01-01.
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