RII Track-4:NSF: Automated Design and Innovation of Chemical Production Processes with Intelligent Computing

RII Track-4：NSF：利用智能计算进行化学品生产过程的自动化设计和创新

• 批准号: 2327303
• 负责人: Yuhe Tian
• 金额: $ 24.06万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327303&HistoricalAwards=false
• 关键词: RII; Track; NSF; Automated; Design

Conceptual process design plays a critical role toward creating innovative chemical plants to address the outstanding challenges of energy and sustainability. Computer-aided methods are essential to rapidly screen the optimal process design among a plethora of existing technologies or even to discover new ones outside the box of current industrial practice. However, their potential is yet to be fully exploited. Toward this direction, the vision of this project is to drive systematic innovation of chemical process designs by augmenting physical laws, artificial intelligence (AI), and quantum computing (QC). We aim to develop a novel phenomena-based process synthesis approach which opens the opportunity to re-invent unit operations leading to breakthrough process performances, while coupled with quantum machine learning to intelligently learn the path for design improvements. The resulting methodology will be unique with the capacity to expedite the development of next-generation chemical and energy process technologies by incorporating advanced scientific computing while significantly saving human efforts. The methods and skills developed by the PI and graduate trainee, in collaboration with Cornell AI for Science Institute, will greatly strengthen the research capacity in West Virginia University (WVU) at the forefront of advanced scientific computing. The project deliveries will also be incorporated to curriculum courses, workshops, and online learning modules tailored for the training of diverse undergraduate and graduate students. This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project will provide a fellowship to an Assistant Professor and training for a graduate student at West Virginia University. This work would be conducted in collaboration with researchers at Cornell University. The project will develop a computer-aided approach to systematically generate novel, optimal, and sustainable chemical process designs. It builds on a generalized chemical process representation using physicochemical phenomena which allows to synthesize process designs, conventional or intensified, by optimizing the fundamental mass and heat transfer toward thermodynamic limits. The bottom-up process synthesis using phenomenological building blocks serves as a departure from traditional unit operation-based design which may hinder the generation of creative process solutions. Artificial intelligence and quantum computing-assisted algorithms will be developed to achieve process design, optimization, and innovation by synergizing: (i) Reinforcement learning to smartly search the process design space characterized by physics-based attainable region, (ii) Autoencoder neural network to develop a quantitative understanding on the feasible and infeasible process design space, (iii) Quantum reinforcement learning to accelerate the speed of design discovery. Thus, the methodology developed from this project will automatedly identify optimal (and potentially out-of-the-box) design solutions with substantially improved process performances, which typically rely on engineering expertise and efforts. The application showcase will be used to increase the economic competitiveness of sustainable hydrogen production from centralized or distributed natural gas utilization. This project will transform the PI’s individual career by sparking the first-time collaboration with Cornell, opening new research directions on the QC frontier, obtaining formal training on AI and QC, and accessing state-of-the-art cloud computing platforms. WVU and Cornell will jointly develop learning materials, conference presentations, journal papers, and competitive proposals as continuation of this project.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.

概念过程设计在创建创新的化学工厂以应对能源和可持续性的杰出挑战方面起着至关重要的作用。计算机辅助方法对于在现有技术中迅速筛选最佳过程设计至关重要，甚至可以在当前工业实践的框外发现新技术。但是，它们的潜力尚未得到充分利用。朝这个方向发展，该项目的愿景是通过增强物理定律，人工智能（AI）和量子计算（QC）来推动化学过程设计的系统创新。我们旨在开发一种新型现象的过程综合方法，该方法为重新发明单位操作提供了机会，从而导致突破性的过程性能，同时与量子机器学习相结合，以明智地学习设计改进的道路。由此产生的方法将是独一无二的，具有通过融合高级科学计算的同时大大节省人类努力来加快下一代化学和能源过程技术的发展。 PI和研究生实习生开发的方法和技能与科学学院的康奈尔AI合作将大大增强西弗吉尼亚大学（WVU）的研究能力，这是高级科学计算的最前沿。该项目的交付还将纳入课程课程，讲习班和在线学习模块，该模块量身定制，适合培训不同的本科生和研究生。这项研究基础设施改进Track-4 Epscor Research Fellows（RII Track-4）项目将为西弗吉尼亚大学的一名研究生助理教授和培训提供奖学金。这项工作将与康奈尔大学的研究人员合作进行。该项目将开发一种计算机辅助方法，以系统地生成新颖，最佳和可持续的化学过程设计。它使用物理现象建立在广义的化学过程表示基础上，该现象可以通过优化基本质量和向热力学限制的基本质量和热传递来合成传统或启发的过程设计。使用现象学构建块的自下而上的过程合成是与传统的基于单元操作的设计背道而驰，这可能会阻碍创造过程解决方案的产生。将开发人工智能和量子计算辅助算法，以通过协同作用来实现过程设计，优化和创新：（i）强化学习以智能搜索以基于物理学的可达到的区域为特征的过程设计空间，（ii）自动编码器神经网络对可行的量化和iNfease vors for n osce noce vaste nexing for n osce nose doce nose vorse nefore varsemim interemii forne vorsemii forne vorsemim interemim forne vorsemim interemim inde neforme demborem deforme vorsemim（iii）（iii）（III）（iii）。这是从该项目开发的方法，将自动确定具有大大改进的过程性能的最佳（可能是现成的）设计解决方案，这通常依赖于工程专业知识和努力。应用展示柜将用于提高集中或分布式天然气利用率的可持续氢生产的经济竞争力。该项目将通过与康奈尔（Cornell）进行首次合作，在QC Frontier上打开新的研究指示，在AI和QC上进行正式培训，并访问最先进的云计算平台，从而改变PI的个人职业。 WVU和Cornell将共同开发学习材料，会议演讲，期刊论文和竞争性建议，作为该项目的延续。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估，被认为是宝贵的支持。