CAREER: Computational design of sustainable hydrogenation systems via a novel combination of data science, optimization, and ab initio methods

职业：通过数据科学、优化和从头算方法的新颖组合进行可持续加氢系统的计算设计

• 批准号: 2045550
• 负责人: Srinivas Rangarajan
• 金额: $ 50万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045550&HistoricalAwards=false
• 关键词: CAREER; Computational; design; sustainable; hydrogenation

Sustainable, safe, and process-intensified hydrogenation technologies are essential for distributed, small-scale, and on-demand manufacturing of chemicals and fuels from shale gas and biomass, upgrading carbon dioxide to useful organic chemicals, and upcycling plastic waste. New technological developments in this area would contribute to increasing international competitiveness of the U.S. chemical manufacturing industries and meeting relevant U.N. goals on sustainable development. A promising chemistry to this end is catalytic transfer hydrogenation (CTH), a process that is carried out using hydrogen donors instead of pure molecular H2, thereby offering a safe, H2- and potentially CO2-free hydrogenation technology. A critical step towards deploying CTH is to optimally design the underlying process, a challenging task because atomic-scale information such as reaction thermodynamics, pathways, and rates have implications at the microscopic (e.g., product yield) and macroscopic levels (e.g., process economics). The research vision of this project is to develop and apply novel computational tools, in synergy with experiments, to design CTH processes by integrating information and decisions across the different size scales. In parallel with this research, the educational vision of this project is to promote computational thinking and programming literacy at various levels of STEM education. These two skills are well-recognized as being essential for the next generation of science and engineering innovators to tackle emerging grand challenges in the energy, health, and environmental spheres.This CAREER proposal specifically aims to computationally design a vapor-phase transition-metal catalyzed CTH reaction system of a model oxygenate, viz. acrolein, which is the smallest molecule having both C-C and C-O unsaturation; as such, it can be considered a model representative of biomass-derived molecules and functionalized intermediates in the chemical industry. Designing the acrolein CTH reaction system ultimately requires identifying the optimal donor-catalyst combination that maximizes the yield of a desired product, e.g., hydrogenation selectivity of acrolein to propanal versus propenol. To this end, a novel computational framework that integrates density functional theory (DFT), informatics, machine learning, and several other process systems engineering computational methods including nonlinear optimization and advanced data sampling via reinforcement and transfer learning, will be developed as part of this research to (i) build Gaussian Process surrogate models, (ii) formulate and solve coverage-cognizant microkinetic models, and (iii) solve reaction system optimization problems. This framework will allow the PI to address a critical gap in the fundamental mechanistic elucidation and multiscale design of acrolein CTH reaction systems and thereby identify the optimal donor-catalyst combination from a representative subset of donors and transition metal catalysts. A well-integrated educational program will be developed to target different age groups at Lehigh University and the broader Lehigh valley. This includes engaging high-school and undergraduate students in cutting-edge research at the intersection of data science and catalysis, developing online interactive visualization-based modules to explain high-school science and undergraduate engineering concepts via enquiry-based learning, and developing and offering an interdisciplinary elective to train chemical engineers in the burgeoning area of data science and machine learning.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.

可持续、安全和过程强化的加氢技术对于从页岩气和生物质中分布式、小规模和按需制造化学品和燃料、将二氧化碳升级为有用的有机化学品以及塑料废物的升级循环至关重要。这一领域的新技术发展将有助于提高美国化学制造业的国际竞争力，并实现联合国有关可持续发展的目标。为此，一种有前途的化学方法是催化转移氢化（CTH），这是一种使用氢供体而不是纯分子H2进行的方法，从而提供了一种安全的、不含H2和可能不含CO2的氢化技术。部署CTH的关键一步是优化设计底层过程，这是一项具有挑战性的任务，因为原子尺度的信息，如反应热力学、途径和速率，在微观上具有影响（例如，产物产率）和宏观水平（例如，过程经济学）。该项目的研究愿景是开发和应用新的计算工具，与实验协同，通过整合不同规模的信息和决策来设计CTH过程。与此同时，该项目的教育愿景是在各级STEM教育中促进计算思维和编程素养。这两项技能被公认为是下一代科学和工程创新者应对能源、健康和环境领域新出现的重大挑战所必不可少的。本CAREER提案的具体目标是通过计算设计一个汽相过渡金属催化的CTH反应系统，该反应系统的模型是丙烯醛，丙烯醛是同时具有C-C和C-O不饱和度的最小分子;因此，它可以被认为是化学工业中生物质衍生分子和官能化中间体的代表模型。设计丙烯醛CTH反应系统最终需要确定使所需产物的产率最大化的最佳供体-催化剂组合，丙烯醛氢化为丙醛相对于丙烯醇的选择性。为此，将开发一种新的计算框架，该框架集成了密度泛函理论（DFT），信息学，机器学习和其他几种过程系统工程计算方法，包括非线性优化和通过强化和迁移学习的高级数据采样，作为本研究的一部分，以（i）构建高斯过程代理模型，（ii）制定和解决覆盖认知微观动力学模型，以及（iii）解决反应系统优化问题。该框架将允许PI解决丙烯醛CTH反应系统的基本机理阐明和多尺度设计中的关键差距，从而从供体和过渡金属催化剂的代表性子集中确定最佳供体-催化剂组合。一个良好的综合教育计划将制定针对不同年龄组在利哈伊大学和更广泛的利哈伊山谷。这包括让高中和本科生参与数据科学和催化交叉领域的前沿研究，开发基于在线交互式可视化的模块，通过基于探究的学习来解释高中科学和本科工程概念，开发和提供跨学科选修课程，以培训数据科学和机器学习新兴领域的化学工程师。该奖项反映了NSF的法定基金会的使命是履行其使命，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价，被认为值得支持。

项目成果

A deep neural network for oxidative coupling of methane trained on high-throughput experimental data

• DOI: 10.1088/2515-7655/aca797
• 发表时间: 2022-11
• 期刊: Journal of Physics: Energy
• 作者: Klea Ziu;Rubén Solozabal;S. Rangarajan;Martin Takác

Improving the predictive power of microkinetic models via machine learning

通过机器学习提高微动力学模型的预测能力

• DOI: 10.1016/j.coche.2022.100858
• 发表时间: 2022
• 期刊: Current Opinion in Chemical Engineering
• 影响因子: 6.6
• 作者: Rangarajan, Srinivas;Tian, Huijie
• 通讯作者: Tian, Huijie