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.

概念流程设计对于创建创新化工厂以应对能源和可持续性的突出挑战至关重要。计算机辅助方法对于在众多现有技术中快速筛选最佳流程设计甚至发现新的技术至关重要。然而，它们的潜力尚未得到充分利用。朝着这个方向，该项目的愿景是通过增强系统物理定律、人工智能 (AI) 和量子计算 (QC) 来推动化学工艺设计的创新。我们的目标是开发一部小说。基于现象的工艺合成方法为重新发明单元操作提供了机会，从而实现突破性的工艺性能，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，能够加快开发速度。首席研究员和研究生学员与康奈尔人工智能科学研究所合作开发的方法和技能，通过结合先进的科学计算来开发下一代化学和能源工艺技术，同时显着节省人力。大学 (WVU) 处于先进科学的前沿该项目交付的内容还将纳入为培训不同本科生和研究生而定制的课程、研讨会和在线学习模块。该研究基础设施改进 Track-4 EPSCoR 研究人员 (RII Track-4) 项目将提供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来系统地生成新颖、优化和可持续的化学工艺设计。它建立在通用化学过程表示的基础上。使用物理化学现象，通过优化基本的传质和传热以达到热力学极限，可以合成传统或强化的工艺设计。使用唯象构建块的自下而上的工艺合成与传统的基于单元操作的设计不同，这可能会阻碍设计。将开发人工智能和量子计算辅助算法，通过协同作用实现流程设计、优化和创新：（i）强化学习，智能搜索基于物理的流程设计空间。 （ii）自动编码器神经网络对可行和不可行的流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，该项目开发的方法将自动识别最佳（该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。该项目将改变PI 的个人职业生涯将通过引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来共同开发学习。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。