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; 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，安全和过程强化的氢化技术对于分布式，小规模和按需制造的化学品和燃料从页岩气和生物量，将二氧化碳升级为有用的有机化学物质以及升级塑料废物至关重要。该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，并达到联合国可持续发展的相关目标。这一目的的有希望的化学是催化转移氢化（CTH），该过程是使用氢供体而不是纯分子H2进行的，从而提供了安全，H2-且潜在的无二氧化碳氢化技术。部署CTH的关键步骤是最佳设计基础过程，这是一个具有挑战性的任务，因为原子尺度信息（例如反应热力学，途径和速率）在微观（例如产品产量）和宏观水平（例如，过程经济学）上具有影响。该项目的研究愿景是开发和应用与实验协同作用的新型计算工具，以通过整合不同尺寸尺度的信息和决策来设计CTH过程。与这项研究的同时，该项目的教育愿景是促进各种STEM教育的计算思维和编程素养。这两种技能被众所周知，这对于下一代科学和工程创新者至关重要，以应对能源，健康和环境领域的新兴挑战。该职业建议专门旨在计算蒸气阶段过渡 - 金属型催化催化的催化的CTH催化的CTH型CTH反应系统模型的氧化物，viz，viz。丙烯醛是具有C-C和C-O不饱和的最小分子；因此，它可以被认为是化学工业中生物质衍生分子和功能化中间体的模型代表。设计丙烯醛CTH反应系统最终需要识别最佳供体 - 催化剂组合，从而最大化所需产物的产量，例如，丙烯醛对丙烯酚对丙醇与丙醇的氢化选择性。为此，将开发一个新的计算框架，该框架将密度功能理论（DFT），信息学，机器学习以及其他几种过程系统的工程系统计算方法（包括非线性优化和通过增强和传输学习）进行开发，作为这项研究的一部分，以（i）构建高斯工艺替代模型，（II）构建覆盖系统和解决覆盖率的模型（II），并构建覆盖范围的模型（ii），并构建了覆盖范围的模型（II）。该框架将使PI能够解决丙烯醛CTH反应系统的基本机械阐明和多尺度设计中的临界差距，从而从代表性的捐助者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。将制定一项良好的教育计划，以针对Lehigh University和Lehigh Valley的不同年龄段。这包括吸引高中和本科生参与数据科学与催化的交集，开发在线互动互动的基于可视化的模块，以解释高中科学和通过基于Isquiry的学习，并开发和开发和提供跨学科的选举者，以培训数据科学和机器的统计数据，并提供了跨学科的培训。值得通过基金会的智力优点和更广泛的影响审查标准来通过评估来支持。