Computer Vision Methods for Enhanced Understanding of Mixing Phenomena and Object Tracking in Process-scale Chemical Reactions

用于增强对过程规模化学反应中混合现象和对象跟踪的理解的计算机视觉方法

• 批准号: 2889116
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2889116
• 关键词: Computer; Vision; Methods; Enhanced; Understanding

AIMS IN BRIEF: (i) Investigate the application of imaging-based mixing analysis algorithms as a non-contact reaction monitoring approach to Grignard reagent synthesis on scale.(ii) Investigate the use of recently-developed mixing and object tracking imaging methods towards novel non-contact reaction monitoring of nucleophilic aromatic substitutions on across small and large scales.This studentship is supported by a £10k cash contribution from CPACT. The support follows an earlier £5k combined contribution across feasibility and internship studies to establish proof-of-concept applications of our team's Kineticolor technology on chemical problems of interest to CPACT member companies CatSci and Takeda. Additional contributions are being made from the supervisor's available funds to make up the 3-year EPSRC studentship package.ABSTRACT: Outside chemical modification, mixing is one of the most important process control parameters to consider in reaction scale-up. While several analytical tools are well-established in mixing analysis, non-contact imaging approaches of high temporal resolution remain in their relative infancy. Building on recent CPACT feasibility and internship studies, we will explore the applications of our computer vision kinetics and mixing analyses in the process-scale study of two reaction classes identified by collaborators at Takeda and CatSci.

目标简介：（i）研究将基于成像的混合分析算法作为一种非接触式反应监测方法应用于大规模合成格氏试剂。(ii)研究使用最近开发的混合和物体跟踪成像方法，在小规模和大规模上对亲核芳香取代的新型非接触反应进行监测。该奖学金由CPACT的1万英镑现金捐款支持。在此之前，我们在可行性和实习研究中提供了5000英镑的综合资助，以建立我们团队的Kineticolor技术在CPACT成员公司CatSci和武田感兴趣的化学问题上的概念验证应用。额外的贡献正在作出从主管的可用资金，以弥补3年的EPSRC studentship package.AbSTRACT：化学改性外，混合是最重要的过程控制参数之一，考虑在反应放大。虽然有几种分析工具在混合分析中得到了很好的应用，但高时间分辨率的非接触成像方法仍处于相对初期。基于最近的CPACT可行性和实习研究，我们将探索我们的计算机视觉动力学和混合分析在武田和CatSci的合作者确定的两种反应类别的过程规模研究中的应用。