Big Data analytics for plantwide fault detection and process intensification in the pharma industry

This collaborative research project between the University of Edinburgh (UoE) and a major multinational pharmaceutical company will employ advanced data-driven modelling and statistical methods in order to gain new process knowledge from datastreams, identify and visualise suboptimal modes/regimes, and optimise the operation of key unit operations (e.g. upstream separations) for sustainable, environmentally benign production of medications.


In particular, this project will focus on data-driven process modelling and robust statistics, to probe batch process input/output variability and expand our understanding of multiscale (micro-macro) effects which frequently but unexpectedly hamper various unit operations. The lack and/or prohibitive cost of developing, parameterising and routinely using intractable first-principles models poses a definite hindrance vs. robust operation and intensification. Highly variant feedstock composition and intra-plant multicomponent stream mixing tend to adversely affect manufacturing performance, even at guaranteed product quality, because unforeseen faults and suboptimal operation induce hefty outsourcing and maintenance costs.


This project will develop computational tools and novel statistical methodologies to eliminate undesirable deviations and attain measurable improvements in upstream plant performance, via on sensor abundance, Big Data availability, filtering and correlation. The strategic goal is a framework for higher plant efficiency, thus guaranteeing lower financial and environmental risks. Industrial placements, plant visits and collaboration are an integral part of this project.


The Gerogiorgis Research Group at the School of Engineering (University of Edinburgh) employs high-fidelity first-principles modelling and advanced numerical methods for systematic synthesis, design and optimisation of complex chemical processes, with emphasis on continuous pharmaceutical manufacturing and comparative technoeconomic analyses for pharmaceuticals, bioproducts, food/drinks and energy. Their research is recognized with multiple IChemE Global Award distinctions, an Academy of Athens research publication prize, and a recent Royal Academy of Engineering (RAEng) Industrial Fellowship.

Further Information: 


Applications are welcomed from self-funded students, or students who are applying for Scholarships from the University of Edinburgh or elsewhere.

Closing Date: 

Monday, March 15, 2021

Principal Supervisor: 

Assistant Supervisor: 


Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.


Applications are welcomed from self-funded students, or students who are applying for Scholarships from the University of Edinburgh or elsewhere.

Further information and other funding options.