Process industries typically use a large amount of energy to product their products, to reduce environmental emissions and need significant improvements to rise to the challenges of net zero. As energy producers move away from oil and gas energy towards wind, solar and other alternative energy sources the risks of loss of electricity needs a different approach from diesel stand-by compressors for emergency shutdown and maintaining production. This project will build on work in our group to provide a modelling tool to allow manufacturers to select the right strategy for energy for their sites, their back up/ emergency supplies and how they will store and use energy. This is important for resilience of manufacturing. Typically, energy provision is capitally intensive for process industry sites, and therefore making the risk decisions for such long term investments are critical and can make a significant difference of the viability of sites. Many such sites are already aging in infrastructure so the tool and algorithms need to take into account existing sites and infrastructure issues as well as decision for new sites. How this integrates with the risk management and preparedness for emerging risks and opportunities for manufacturing will need to be considered.
A full program of development is provided for PhD students including relevant training dependant on the needs of the student in programming, modelling and other aspects relevant to the project. In addition significant employability support will be given.
Students from our department and in particular with modelling PhD’s are very successful in gaining excellent roles in industry in modelling, Machine learning, other roles in a wide range of companies. The department and the PI have very significant links in industry and mentors will be sought to support the student’s future interests in industry or academia in addition to the PI. Students are also very successful in moving to very strong Post Doc positions and onto academic positions.
The student will be part of the Cordiner and Sol Brown Groups, very significant opportunities to work with partner companies in the process industries. This is in addition to the excellent University and Department structured support and training for PhD students.
Please see this link for information on how to apply: https://www.sheffield.ac.uk/cbe/postgraduate/phd/how-apply. Please include the name of your proposed supervisor and the title of the PhD project within your application.
A degree in Engineering is required. Some experience in modelling, machine learning and python programming would be very helpful. If English is not your first language then you must have an International English Language Testing System (IELTS) average of 6.5 or above with at least 6.0 in each component, or equivalent. Please see this link for further information: https://www.sheffield.ac.uk/postgraduate/phd/apply/english-language.