An aging plant is one that has typically been operating over several decades and is likely to have gone through significant changes, ranging from modifications to expansions and design changes. As the asset gets older, there are several challenges that will inevitably arise, such as over or under maintained equipment, difficulties purchasing spare parts or changes in operating conditions that can lead to corrosion, erosion, mechanical damage, and other late life degraded failures.
The University of Salford’s annual HackCamp event, moved to a virtual platform in 2021, seeing 160 undergraduate computing students work together and compete to build industry software systems being led by 11 industry partners, including TOTAL E&P, Add Energy and HR in One.
At Add Energy, we recognize the importance of innovation, technology and digitalization and invest significant effort into the research and development of software solutions, which is why we were so honored to be able to be part of this important event for upskilling up-and-coming developers.
As a consultancy business to the energy sector and beyond, we are uniquely positioned to provide insights to shared challenges plant operators face, enabling us to suggest concepts and guidance for developing solutions that will provide students with experience of solving real-world industry challenges.
Peter Adam our EVP, R&D Manager; Hossein Ghavimi and our Learning Delivery Lead, Mike Meen worked closely with and mentored their assigned group of student developers during a 3 week ‘hackathon’ as they applied research of tailoring agile software development methods and familiarized themselves with techniques for communicating and presenting work to their HackCamp partner clients.
The competition was judged by a panel of industry professionals and we were delighted to see the 1st place prize go to the group who developed Add Energy’s concept into cloud-database-driven dashboard for risk management, impressing the judges with their original use of web scraping and the possibility for the inclusion of machine learning to identify future risks.