Self-Learning Reactor Systems for Automated Development of Kinetic Models

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.