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Applications of Industry 4.0, Data-Driven Modelling and Analytics to Improve Industrial Energy Efficiency and Predictive Maintenance


   Centre for Sustainable Engineering

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  Dr Mike Knowles, Dr M Short, Dr Chris Ogwumike  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

This PhD project will investigate applications of Industry 4.0, data-driven modelling and analytics in industrial applications, with a specific focus upon process energy efficiency, asset management and predictive maintenance. The link between the energy efficiency of engineering assets and their condition is now well established (c.f Morris, Baglee and Knowles 2020, 2019; Knowles and Baglee 2012, 2011). The importance of accurate asset models and analytics for effective condition monitoring and energy-efficient operation is also now emerging (see, e.g. Short, 2021; Short & Twiddle, 2019), but is much less established as a research area.

To this end, this project will explore how the application of advanced, digitally enabled modelling and analytics can be used to support Condition Monitoring (CM) and Predictive Maintenance (PM) approaches to improve process energy efficiency and availability whilst also reducing CO2 emissions. The advent of Industry 4.0 and the enabling technologies that underpin it open a range of opportunities to enhance how equipment is monitored and modelled, how data is analysed, and how equipment performance can be predicted - with a goal of optimising how assets should be maintained to ensure they are reliable, efficient and of low emissions.

The precise workplan for this research will be determined by the successful application in collaboration with the supervisory team but the following phases are envisaged:

  1. Structured literature review to establish the current research on the topics of data-driven modelling, energy efficiency and asset management, the applications of Industry 4.0 (including and real or perceived barriers), advance maintenance methodologies and the use of machine learning/AI to enhance maintenance/reliability.
  2. Data collection to potentially include large scale survey of industrial organisations to identify current practice in this area, and to identify patterns across/between industries and types of companies (e.g. large manufacturers vs. SMEs). It is also envisaged that based on survey data, in depth case studies will be identified where the researcher will engage more deeply with specific companies to investigate practice and attitudes in their business.
  3. Analysis and benchmarking of collected data from (1) and (2) above to identify key outcomes and opportunities for culture change, future research and enhancements.
  4. Development of a tool for companies to evaluate and enhance their maintenance practices in terms of energy efficiency which will be evaluated in practice by selected organisations.
  5.  Recommendations for future research projects underpinned by the findings of this research.

The successful applicant will have access to facilities in the Industrial Digitalisation Technology Centre (IDTC) and Industrial Net Zero Innovation Centre (INZIC) to support their research.

Entry Requirements

Applicants should hold or expect to obtain a good honours degree (2:1 or above) in a relevant discipline. A masters level qualification in a relevant discipline is desirable, but not essential, as well as a demonstrable understanding of the research area. Further details of the expected background may appear in the specific project details. International students will be subject to the standard entry criteria relating to English language ability, ATAS clearance and, when relevant, UK visa requirements and procedures.

How to Apply

Applicants should apply online for this opportunity at: https://e-vision.tees.ac.uk/si_prod/userdocs/web/apply.html?CourseID=1191

Please use the Online Application (Funded PHD) application form. When asked to specify funding select “other” and enter ‘RDS’ and the title of the PhD project that you are applying for. You should ensure that you clearly indicate that you are applying for a Funded Studentship and the title of the topic or project on the proposal that you will need to upload when applying. If you would like to apply for more than one project, you will need to complete a further application form and specify the relevant title for each application to a topic or project.

Applications for studentships that do not clearly indicate that the application is for a Funded Studentship and state the title of the project applied for on the proposal may mean that your application may not be considered for the appropriate funding.

For academic enquiries, please contact Dr Mike Knowles [Email Address Removed], Prof Michael Short [Email Address Removed].

For administrative enquiries before or when making your application, contact [Email Address Removed].  


Funding Notes

The Fees-Paid PhD studentship will cover all tuition fees for the period of a full-time PhD Registration of up to four years. Successful applicants who are eligible will be able to access the UK Doctoral Loan scheme https://www.gov.uk/doctoral-loan to support with living costs. The Fully Funded PhD Studentship covers tuition fees for the period of a full-time PhD Registration of up to four years and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress. Applicants who are employed and their employer is interested in funding a PhD, can apply for a Collaborative Studentship.

References

Short, M. (2021) “Control and Informatics for Demand Response and Renewables Integration”. In: Hussain, C.H. and Di Sia, P. (Eds), Handbook of Smart Materials, Technologies, and Devices – Applications of Industry 4.0, Springer Nature, November 2021.
Morris, Adrian, Baglee, David and Knowles, Michael (2020) Using energy consumption profiles as an indicator of equipment condition. International Journal of COMADEM, 23 (2). p. 1. ISSN 1363-7681
Short, M. and Twiddle, J. (2019) “An Industrial Digitalisation Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment”, Sensors, Vol. 19, No. 17, pp. 3781, August 2019.
Morris, Adrian, Baglee, David and Knowles, Michael (2019) Using Energy Consumption Profiles as an Indicator of Equipment Condition In: COMADEM 2019: International Congress on Condition Monitoring and Diagnostics Engineering Management, Huddersfield, UK.
Knowles, Michael and Baglee, David (2012) The role of maintenance in energy saving in commercial refrigeration. Journal of Quality in Maintenance Engineering, 18 (3). pp. 282-294. ISSN 1355-2511
Knowles, Michael and Baglee, David (2011) Energy usage modelling as a condition monitoring tool. In: 7th International Conference on Modelling in Industrial Maintenance and Reliability (MIMAR2011), 18-19 Apr 2011, Cambridge, UK
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