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University of Aberdeen and Intelligent Plant launch AI project to support Scottish hydrogen production

Computer scientists at the University of Aberdeen and Intelligent Plant have launched a project using artificial intelligence (AI) to supercharge hydrogen production in Scotland.

The two will use AI to develop a Decision Support System (DSS) to address shortfalls in production and support Scotland in meeting its target of 5GW of installed hydrogen production by 2030, the equivalent to a sixth of the country’s energy needs.

They are working in partnership with the European Marine Energy Centre (EMEC) on the project, which has been funded through the Scottish Government’s Emerging Energy Technologies Fund.

To overcome the shortcomings and impact of hydrogen production, researchers will use explainable AI (XAI) that will allow operators to ask the system questions, receive feedback, and modify their approach if necessary.

Professor Nir Oren, University of Aberdeen, said, “A hydrogen production facility must balance myriad demands, particularly when operating using intermittent renewable energy, and consideration must be given to current and future forecasts for storage, consumption, energy availability and cost.

“In this project we build on ideas from the area of XAI and more particularly formal argumentation theory, to enable users to interrogate the system and understand why it suggested specific courses of action.

“By taking this approach, the DSS will build trust amongst users that we hope will ultimately lead to an increase in the production of green hydrogen – an important factor in helping Scotland meet its Net Zero ambitions.”

Paul Gowans, Engineer at Intelligent Plant, added, “The use of Intelligent Plant’s Industrial App Store as an enabler for XAI will allow operators to better understand its system.

“It will allow for live connectivity to EMEC’s sites and will enable the team to demonstrate how the AI system can be integrated with real systems and data to optimise energy management in a practical and scalable way.

“Our end goal is to create a DSS which can be used to make recommendations around hydrogen logistics, and whose recommendations can be queried and corrected as circumstances change.”

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