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Development of next generation integrated control systems for the pharmaceutical industry

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  • Full or part time
    Dr Philip Donnellan
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

This project is aiming to contribute to the transition from traditional batch pharmaceutical processing (centred around ridged recipes and low tolerances for change during manufacturing) to a model enabled design approach facilitating
- Flexible plug and play continuous manufacturing skids
- Design based operating spaces
- The ability to react upon demand

However this ability to react and adapt as required during operation requires significant understanding of how the continuous manufacturing system will behave when an unexpected change in encountered:
- What happens if an unwanted concentration is discovered at one point in a connected series of operations?
- What has been the time behaviour throughout the system?
- Where has this concentration spread to?
- How much of the product must be sent to waste?

As reactions and unit operations increasingly involve combinations of liquids, gases and solids to produce complex APIs, these questions become increasingly difficult to answer and thus accurate prediction and subsequent control of the system’s time-response requires integrated modelling techniques which can react in real time to minimise disturbances and avoid product loss. The focus of this project will be the use of machine learning for the development of predictive, time-responsive, modelling techniques integrated with appropriate real time measurements to achieve automated, optimised control of manufacturing processes encountering off-specification conditions.

The purpose of the project is to try and advance control methodologies which are currently applied to pharmaceutical manufacturing. The work aims to establish an integrated control framework in which various modelling layers are combined to enable real time analysis of data to be combined with the outputs from advanced simulations to achieve real time, dynamic control.

The successful applicant should have a keen interest and aptitude for:
- Advanced mathematical modelling
- Experimental work
- Engineering problem solving
- Engineering design

This project is fully funded by the SSPC research cluster (, and will therefore include industrial collaborations and the chance to conduct an industrial placement in a suitable company. The successful applicant will join a group of researchers in University College Dublin ( focusing on similar topics and will have the chance to establish significant collaborations.

Candidates should have a first class honours degree in Chemical or Mechanical Engineering (or close equivalent)

Funding Notes

Fully funded position

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