PhD Studentship - Constructing AI-Powered Optimisation Frameworks to Enhance Manufacturing Operations

Research theme: Process Systems Engineering, Machine Learning, Chemical Engineering, Artificial Intelligence

How to apply: uom.link/pgr-apply-2425

How many positions: 1

This 3.5 year PhD is a fully funded project between the University of Manchester and Croda International Plc. You must be a home student to be eligible to apply. The successful candidate will receive an annual tax free stipend set at the UKRI rate (£19,237 for 2024/25) and tuition fees will be paid. We expect the stipend to increase each year.

Digital manufacturing is a cornerstone of Industry 4.0, with data-driven technologies increasingly revolutionising the chemical and process industries. The integration of artificial intelligence (AI) and data analytics in manufacturing is enabling transformative innovations, including operational efficiency, predictive maintenance, and effective production planning and scheduling. These advancements are critical to achieving higher productivity, minimising unplanned downtime, and ensuring optimal resource utilisation to meet customer needs.

This PhD project focuses on leveraging data intelligence to uncover hidden process knowledge for accurate prediction of overall equipment effectiveness (OEE), a key metric for asset utilisation. Advanced machine learning models will be developed to predict OEE in real time, integrating diverse datasets including sensor readings, operational parameters, and historical performance metrics. These predictions will inform proactive maintenance strategies, enhancing equipment reliability and minimising downtime.

Additionally, the project will design advanced planning and scheduling tools that incorporate OEE predictions and maintenance timings. Optimising production workflows will balance operational efficiency with resource availability and maintenance; and assist the sales and operations planning for capacity analysis from product mix and OEE data.

The methodologies will be validated using industrial-scale data to ensure robustness and applicability. The research outcomes will contribute to smart, sustainable manufacturing systems, empowering decision-makers with actionable insights.

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. A strong experience in python programming is desired.

To apply, please  contact the supervisors; Dr Zhang - dongda.zhang@manchester.ac.uk and Dr Zhang - nan.zhang@manchester.ac.uk. Please include details of your current level of study, academic background and any relevant experience and include a paragraph about your motivation to study this PhD project.

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