Digital Twins and Machine Learning for Flow Process Control


   Department of Computer Science

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  Dr John Oyekan  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Background: The University of York has an exciting research opportunity for a self-motivated PhD student with skills or interests in mathematics, reinforcement learning, optimisation, robotics, materials, controls and simulation to join a dynamic research team in the development of Digital Twins for various flow manufacturing processes. Flow processes are used to manufacture a variety of goods from toothpaste to chocolate. However, modelling and controlling these processes for precise manufacturing of products is still a challenge. 

Aim: This PhD would investigate various methodologies for use in simulating flow processes. Potential methodologies include physics-informed machine learning. The successful candidate would also interface with hardware platforms in order to validate the developed Digital Twins.

Objectives:

  1. Conduct a literature review to identify various physics-informed methodologies for building a Digital Twin framework.
  2. Identify and develop a physical platform that will be used in validating the developed Digital twin. The successful student would work with our industrial partners to create various use cases that suit their industrial needs. 
  3. Develop a physics informed digital framework to simulate flow processes
  4. Integrate the digital framework with the physical platform towards predicting the effects of parameter changes on the flow processes
  5. Validate the digital twin and apply it in the autonomous control of the physical platform for improved precise control of the flow process. 

This project will be informed by the disciplines of physics, chemistry, materials and biology. This cross-disciplinary project will involve the creation of new algorithms to control autonomous platforms for various applications. As a result, the candidate must have demonstrable skills in interfacing with and controlling hardware platforms. Furthermore, the candidate must have demonstrable understanding of mathematical models and their applications to engineering problems. 

This work will be carried out in Department of Computer Science (top 10 in UK for research quality), YorRobots (https://www.york.ac.uk/yorrobots/) and at the University of York’s £15m Institute for Safe Autonomy (https://www.york.ac.uk/safe-autonomy/). The Institute for Safe Autonomy is UK’s first research centre dedicated to the design, development, safety and communications for robotics and connected autonomous systems. The Institute provides a world-leading ecosystem for research and innovation, education, public engagement and commercial realisation. 

The University of York is part of the research-intensive Russell Group of universities in the United Kingdom which inject nearly £87 billion into the national economy every year. We believe that people and ideas are the key to meeting global challenges. Through world-class research and education we are helping to create a dynamic economy, stronger communities and a better future for the world. We maintain the very best research, an outstanding teaching and learning experience and unrivalled links with local and national business and the public sector.

Russell Group universities have huge social, economic and cultural impacts locally, across the UK and around the globe. The Russell Group of universities produce more than two-thirds of the world-leading research produced in UK universities and support more than 260,000 jobs across the country. 32% of students are of non-UK nationality, attracted to our universities by the quality, relevance and reputation of research. Russell Group members have a strong role and influence within their regional and local communities, collaborate with businesses on joint research projects and supply highly-qualified and highly-motivated graduates to the local workforce. 


Computer Science (8)

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 About the Project