Digital Twins for waste reduction in Industry 5.0 RAZAM_U27SCI
Key details
- Application Deadline
- 24 February 2027 (11:59pm UK Time)
- Location
- UEA
- Funding Type
- Competition Funded (Home students only)
- Start Date
- 1 October 2027
- Mode of Study
- Full time
- Programme Type
- PhD
)
Welcome to Norwich
According to the Sunday Times, this city is one of the best places to live in the UK.
Project Description
Primary Supervisor: Dr. Mohsin Raza(opens in a new window)
Reducing industrial waste is a key challenge in achieving sustainable manufacturing and advancing the
vision of Industry 5.0. Manufacturing processes can generate significant waste through material
inefficiencies, defective products, equipment failures, excessive energy consumption, and suboptimal
production planning. Digital twins, integrated with artificial intelligence (AI), the Internet of Things (IoT),
and advanced data analytics, offer opportunities to monitor manufacturing operations, identify sources of
waste, and support proactive, data-driven interventions.
This PhD project will investigate the development of intelligent digital twin frameworks to predict, monitor,
and reduce waste in manufacturing processes. The research will explore how near real-time data from
industrial sensors, machinery, and production systems can be integrated into digital twins to identify
2
inefficiencies, detect anomalies and predict product defects, to optimise the use of raw materials and
reduce waste. AI and machine learning techniques will be investigated to support waste prediction and
root-cause analysis.
The project will explore simulation-based approaches that enable manufacturers to evaluate alternative
production scenarios and assess their potential impact on waste generation before implementing
changes in real-world environments. Relevant sustainability indicators may be incorporated to measure
improvements in material utilisation and overall waste reduction. The research will further investigate key
metrics to understand waste patterns and make informed operational decisions.
The successful candidate will contribute to innovative research addressing industrial waste and
environmental sustainability. The project aims to develop intelligent digital twin solutions that help
manufacturers minimise waste, improve resource efficiency, in line with Industry 5.0. The project will offer
opportunities to develop innovative digital twin solutions, publish research in leading academic venues,
and explore practical applications.
Entry Requirements
The standard minimum entry requirement is a 2:1 bachelor's degree.
Funding
This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years. The current UEA / institutional PhD stipend rate is £21,805 and will be subject to any agreed inflationary uplift for 2027/28.