Artificially Intelligent, Autonomous Microreactors for the Discovery of Polyolefin Catalysis

用于发现聚烯烃催化的人工智能自主微反应器

• 批准号: 1701393
• 负责人: Ryan Hartman
• 金额: $ 29.8万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1701393&HistoricalAwards=false
• 关键词: Artificially; Intelligent; Autonomous; Microreactors; Discovery

The current technology gaps of laboratory-scale reaction systems have limited the commercial availability of: 1) automated microreactor platforms that deliver first principles kinetics, 2) automated microreactor platforms for catalyst activity screening, 3) online analytics that capture real-time concentration-response profiles, and 4) computational data analytics able to analyze, report, and recommend. Process development and reaction engineers could potentially implement such reaction systems to expedite their laborious research and development activities. The research project aims at closing these technology gaps in laboratory-scale catalyst screening and characterization to broadly accelerate materials development and polymer commercialization timelines. The study of artificially intelligent, autonomous microreactors (microAIRs) is proposed to address the technical challenges that currently limit the next-generation needs in catalyst discovery research. The governing hypothesis for the study is that microAIRs engineered with online analytics can accelerate, improve accuracy, and minimize the energy and environmental impacts during the iterative discovery of a next-generation olefin catalyst system. Successful testing of this hypothesis will address the principal need to combine real-time, multiphase microfluidics tracking and feedback algorithms with a non-invasive analytical method that can directly measure a reaction parameter. The ability of a reactor system to decipher phase behaviors, analyze the reaction progress, decide which catalyst is the most active, and identify accurate kinetic expressions are tremendous challenges that microAIRs can solve in order to more efficiently screen catalyst and discover kinetics. Opportunities exist to i) improve the accuracy of process kinetics, ii) establish input/output responses that fully fingerprint a catalyst in a business portfolio, and iii) improve the presentation of real-time analytics and accelerated decision making in catalyst discovery. Review of the state-of-the-art reveals that current technical challenges for laboratory-scale, homogeneous polyolefin catalytic systems include: 1) combinatorial challenge to fully fingerprint catalyst performance, 2) evaluation of large commercial libraries of catalyst systems, 3) engineering for control of chemical transport challenges in flow, 4) sensing with adaptive response to catalyst activity, 5) on-chip analytics coupled with unique reactor/mixer designs in flow, and 6) real-time data analysis with adaptive experimental design and execution. The proposed research, if successful, will broadly impact polymers manufacturing, and it will also introduce novel laboratory techniques for the discovery of new science. The project will also involve curriculum development activities and outreach to the community through NYU's incubator program.

实验室规模反应系统当前的技术差距限制了以下产品的商业可用性：1）提供第一原理动力学的自动化微反应器平台，2）用于催化剂活性筛选的自动化微反应器平台，3）捕获实时浓度响应的在线分析配置文件，以及 4) 能够进行分析、报告和推荐的计算数据分析。 工艺开发和反应工程师有可能实施此类反应系统，以加快他们艰苦的研究和开发活动。 该研究项目旨在缩小实验室规模催化剂筛选和表征方面的技术差距，以广泛加快材料开发和聚合物商业化时间表。 人工智能自主微反应器（microAIR）的研究旨在解决目前限制下一代催化剂发现研究需求的技术挑战。 该研究的主导假设是，通过在线分析设计的 microAIR 可以在下一代烯烃催化剂系统的迭代发现过程中加速、提高准确性并最大限度地减少能源和环境影响。 这一假设的成功测试将解决将实时多相微流体跟踪和反馈算法与可直接测量反应参数的非侵入性分析方法相结合的主要需求。 反应器系统破译相行为、分析反应进程、确定哪种催化剂最活跃以及识别准确的动力学表达式的能力是 microAIR 可以解决的巨大挑战，以便更有效地筛选催化剂和发现动力学。存在以下机会：i) 提高过程动力学的准确性，ii) 建立可全面识别业务组合中催化剂的输入/输出响应，以及 iii) 改进催化剂发现中实时分析的呈现并加速决策制定。 对最先进技术的回顾表明，实验室规模的均相聚烯烃催化系统当前面临的技术挑战包括：1）完全指纹催化剂性能的组合挑战，2）大型商业催化剂系统库的评估，3）工程用于控制流动中的化学传输挑战，4) 对催化剂活性进行自适应响应的传感，5) 片上分析与流动中独特的反应器/混合器设计相结合，以及 6) 具有自适应实验设计和执行的实时数据分析。 拟议的研究如果成功，将广泛影响聚合物制造，并且还将引入新的实验室技术来发现新科学。该项目还将涉及课程开发活动以及通过纽约大学孵化器计划向社区进行推广。

项目成果

Supervised machine learning for prediction of zirconocene-catalyzed α-olefin polymerization

用于预测二茂锆催化α-烯烃聚合的监督机器学习

• DOI: 10.1016/j.ces.2019.115224
• 发表时间: 2019-12
• 期刊: Chemical Engineering Science
• 影响因子: 4.7
• 作者: Rizkin, Benjamin A.;Hartman, Ryan L.
• 通讯作者: Hartman, Ryan L.

Activation of homogenous polyolefin catalysis with a machine-assisted reactor laboratory-in-a-box (μAIR-LAB)

使用机器辅助反应器盒式实验室 (μAIR-LAB) 活化均相聚烯烃催化

• DOI: 10.1039/d0re00139b
• 发表时间: 2020-07-28
• 期刊: Reaction Chemistry & Engineering
• 影响因子: 3.9
• 作者: Benjamin A. Rizkin;Ryan L. Hartman
• 通讯作者: Ryan L. Hartman

Combining automated microfluidic experimentation with machine learning for efficient polymerization design

将自动化微流体实验与机器学习相结合，实现高效的聚合设计

• DOI: 10.1038/s42256-020-0166-5
• 发表时间: 2020-01-08
• 期刊: Nature Machine Intelligence
• 影响因子: 23.8
• 作者: Benjamin A. Rizkin;Albert S. Shkolnik;Neil J. Ferraro;Ryan L. Hartman
• 通讯作者: Ryan L. Hartman

Flow chemistry remains an opportunity for chemists and chemical engineers

流动化学仍然是化学家和化学工程师的一个机会

• DOI: 10.1016/j.coche.2020.05.002
• 发表时间: 2020-09
• 期刊: Current Opinion in Chemical Engineering
• 影响因子: 6.6
• 作者: Hartman; Ryan L
• 通讯作者: Ryan L

Artificial Neural Network control of thermoelectrically-cooled microfluidics using computer vision based on IR thermography

基于红外热成像的计算机视觉人工神经网络控制热电冷却微流体

• DOI: 10.1016/j.compchemeng.2018.11.016
• 发表时间: 2019-02-01
• 期刊: Comput. Chem. Eng.
• 作者: Benjamin A. Rizkin;K. Popovich;Ryan L. Hartman
• 通讯作者: Ryan L. Hartman