Self-Learning Reactor Systems for Automated Development of Kinetic Models

用于自动开发动力学模型的自学习反应器系统

• 批准号: 2051147
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2051147
• 关键词: Self; Learning; Reactor; Systems; Automated

Context of Research:This project proposes to couple the automated reactor platforms developed at Leeds and AZ to mixed integer linear programming techniques capable of kinetic model discrimination to create a truly autonomous system for the evaluation and development of scalable process models. These will allow manufacture of pharmaceuticals in accelerated timeframes.Thus far the work using self-optimising flow systems at Leeds has been applied to statistical optimisations and feedback algorithms. This project aims to utilise this reactor platform to enable model discrimination and generate automatically discriminate between kinetic models and thus generate a process model capable to being transferred to other equipment types. The Project: has three key elements, each with its own significant academic research challenges and questions to be answered: I. Automated generation of kinetic profiles via integration of multipoint sampling and sensors. The use of sampling loops connected along the length of a flow reactor to rapidly generate whole kinetic profiles without requiring changes in flow rate (and hence mixing) will be investigated. II. Generation of feasible kinetic models and discrimination will be performed using mixed integer linear programming (see http://dx.doi.org/10.1016/j.compchemeng.2016.04.019). These techniques will be integrated within the reactor platform to allow rapid evaluation and discrimination of kinetic models.III. Demonstration of model transfer and scale-up. Applicability of the generated models will be demonstrated via evaluation in large scale equipment and different equipment types.

研究背景：该项目建议将利兹和亚利桑那州开发的自动化反应堆平台与能够识别动力学模型的混合整数线性规划技术相结合，以创建一个真正自主的系统，用于评估和开发可扩展的过程模型。到目前为止，利兹使用自我优化流程系统的工作已经应用于统计优化和反馈算法。该项目的目的是利用该反应堆平台实现模型识别，并自动生成动力学模型之间的识别，从而生成能够转移到其他设备类型的过程模型。该项目有三个关键要素，每个要素都有其自己的重大学术研究挑战和需要回答的问题：i.通过集成多点采样和传感器自动生成动力学曲线。将研究使用沿流动反应器长度连接的采样回路来快速生成整个动力学分布，而不需要改变流速(从而改变混合)。二.将使用混合整数线性规划生成可行的动力学模型并进行判别(见http://dx.doi.org/10.1016/j.compchemeng.2016.04.019).这些技术将被整合到反应堆平台中，以便能够对动力学模型进行快速评估和识别。通过在大型设备和不同设备类型上的评估，验证了所生成模型的适用性。