Date of Award


Document Type

Master Thesis

Degree Name

Master of Engineering (Research)


School of Science, Technology, Engineering and Mathematics (STEM)

First Advisor

Dr. Joseph Walsh

Second Advisor

Dr. Daniel Riordan


Increased globalisation and competitiveness has given rise to a more dynamic and diverse global market that has begun to place incredible strain on manufacturing supply chains. Conventional manufacturing methods such as the batch manufacturing methods employed largely by the materials processing industries i.e. pharmaceuticals, polymers and magnesium processing plants are ill-suited to cope with the demands of today. As products evolve with technological advancements they become more complex to produce and require alternative approaches to manufacturing to ensure product quality while reducing costs and increasing manufacturing efficiencies.

A potential solution to the growing demands on today's supply chain is process analytical technology (PAT). PAT is a system to continuously monitor and control process parameters that affect the quality of a product through multivariate analytics of in-process data in real-time. This allows for the realtime adjustments of process parameters to maintain a predetermined level of quality while significantly increasing quality, reducing costs and downtime. PAT utilises the latest in sensory technologies to monitor in process material through continuous sampling, reducing the need for manual sampling and analytics. The data acquired from integrated sensors is analysed in real time using multivariate analytics that generate a corrective response to plant equipment.

This research provides an in-depth study of the key drivers of process analytical technology in the manufacturing industry and the design and development of a Modular Prototype Sensing Unit (MPSU) with integrated process analysers and smart sensors. Development of the MPSU and selection of suitable sensors and process analysers was undertaken as part of a Horizon 2020 Spire initiative project titled "ProPAT" and was aimed at developing a modular PAT system for the materials processing industry. This research focused on the development of PAT systems for end users, namely GlaxoSmithKline and MBM Nanotmaterialia, and included an assessment of end user current states and project requirements prior to sensor selection and development of the MPSU.

Rapid Prototyping techniques were employed to develop suitable prototype components for the MPSU systems prior to assembly of the unit. The MPSU accommodated the integration of smart sensors and process analysers that monitored a blend of material conveyed through the unit. The data acquired from the integrated hardware was then analysed using a multivariate analysis software package where a statistical model was generated using partial least squares regression techniques, thus providing the required response to potentially control plant equipment. This thesis presents the results and analysis of the MPSU under a variety of test conditions.

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