Computer Vision Methods for Enhanced Understanding of Mixing Phenomena and Object Tracking in Process-scale Chemical Reactions
Computer Vision Methods for Enhanced Understanding of Mixing Phenomena and Object Tracking in Process-scale Chemical Reactions
批准号:
2889116
负责人:
金额:
$0.0万
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2023
资助国家:
英国
项目状态:
未结题
起止时间:
2023 至 --
中文摘要
点击翻译按钮获取中文摘要
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
国内基金
海外基金
老年人群视障风险VISION管控模式构建与实证研究
-
批准号:71974198
-
项目类别:面上项目
-
资助金额:48.5万元
-
批准年份:2019
-
负责人:王爱平
-
依托单位: