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Developing A Smart Venturi Wet-Gas Meters To Foster Efficient Gas Production And Digital Transformation


School of Computing, Engineering & the Built Environment

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Prof Don McGlinchey , Dr S Smith , Dr Gabriele Chinello Applications accepted all year round Funded PhD Project (Students Worldwide)
Glasgow United Kingdom Chemical Engineering Fluid Mechanics Mechanical Engineering

About the Project

Reference number: SCEBE-20-020-DMSF

Aim and Scope

Wet gas metering is essential to enable optimized and cost-effective production of natural gas. There is demonstrably significant industrial demand for a smart metering device with diagnostic capabilities.

The project addresses major technological challenges:

• measurement of the liquid loading at low cost.

• asset health monitoring with diagnostic capabilities.

• models/correlations proven at field conditions.

The project partners, GCU, TUV-SUD-National Engineering Laboratory and McMenon, will contribute significant resources to design, manufacture, test and train an AI enabled instrument and bring a device to market which can play a vital role in bringing marginal fields into operation and in optimizing production of natural gas.

https://www.tuvsud.com/en-gb/industries/chemical-and-process/national-measurement-system

https://www.mcmenon.com

To apply for this project

· As a full-time student: https://evision.prod.gcu.tribalsits.com/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=D27ENGXXXFT&code2=0006

· As a part-time student: https://evision.prod.gcu.tribalsits.com/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=D27ENGXXXPT&code2=0006


Funding Notes

The studentship covers the payment of tuition fees plus an annual stipend rising to £16,163 and is for a period of three and a half years, subject to satisfactory progress.

The successful applicant will hold a minimum of a Bachelor’s degree in a relevant subject (UK 1.1 or 2.1 classification). Successful candidates will test novel Venturi designs using experimental work at GCU and the National Engineering Laboratory. Data from these experiments will be used to develop an AI enhanced measurement instrument. A good understanding and prior experience of fluid mechanics, instrumentation or machine learning will be an advantage.

References

For further information, please contact:
Prof Don McGlinchey - D.McGlinchey@gcu.ac.uk


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