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.

概念流程设计在创建创新化工厂以解决能源和可持续性方面的突出挑战方面起着关键作用。计算机辅助方法对于在众多现有技术中快速筛选最佳工艺设计，甚至发现当前工业实践之外的新工艺设计至关重要。然而，它们的潜力尚未得到充分利用。在这个方向上，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性创新。我们的目标是开发一种新的基于现象的过程综合方法，该方法为重新发明单元操作提供了机会，从而实现突破性的过程性能，同时结合量子机器学习来智能地学习设计改进的路径。由此产生的方法将是独特的，通过结合先进的科学计算，加速下一代化学和能源过程技术的发展，同时显着节省人力。PI和研究生实习生与康奈尔大学人工智能科学研究所合作开发的方法和技能将大大加强西弗吉尼亚大学（WVU）在先进科学计算前沿的研究能力。项目交付也将被纳入课程课程、研讨会和在线学习模块，为不同的本科生和研究生提供培训。这项研究基础设施改进轨道4 EPSCoR研究研究员（RII轨道4）项目将为西弗吉尼亚大学的一名助理教授提供奖学金，并为一名研究生提供培训。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，系统地生成新颖、最佳和可持续的化学过程设计。它建立在使用物理化学现象的广义化学过程表示的基础上，通过优化基本的质量和热量传递到热力学极限，可以合成常规或强化的过程设计。使用现象学构建块的自下而上的过程综合与传统的基于单元操作的设计不同，这可能会阻碍创造性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，通过协同实现过程设计、优化和创新：(i)强化学习，以智能地搜索以基于物理的可达区域为特征的过程设计空间；（ii）自动编码器神经网络，以对可行和不可行的过程设计空间进行定量理解；（iii）量子强化学习，以加快设计发现的速度。因此，从这个项目中开发出来的方法将自动识别出最优的（潜在的开箱即用的）设计解决方案，这些方案具有显著改进的过程性能，通常依赖于工程专业知识和努力。应用展示将用于提高集中式或分布式天然气利用可持续制氢的经济竞争力。该项目将通过激发与康奈尔大学的首次合作，在QC前沿开辟新的研究方向，获得人工智能和QC的正式培训，以及访问最先进的云计算平台，改变PI的个人职业生涯。作为该项目的延续，西弗吉尼亚大学和康奈尔大学将共同开发学习材料、会议演讲、期刊论文和竞争性提案。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。