MANTEIA - Early fault detection methodologies, applied to a thermal storage system, based on Artificial Intelligence techniques

MANTEIA - Early fault detection methodologies, applied to a thermal storage system, based on Artificial Intelligence techniques

MANTEIA - Early fault detection methodologies, applied to a thermal storage system, based on Artificial Intelligence techniques

MEDITECH Call No. 3 - Funded by the European Union - Next Generation EU Fund (PNRR) - M4C2 I2.3 
CUP I83D24000130005

Beneficiaries: Magaldi Power Spa

Project duration: 12 months - started on May 6, 2024

Magaldi R&D projects

The project aims to study appropriate predictive control techniques for the development of 'early fault detection' software to be applied to the MGTES fluidized bed thermal storage technology.
The MGTES technology, patented by Magaldi, allows energy produced by renewable sources to be stored and dispatched on demand for the production of green steam, thus supporting the decarbonization of energy-intensive industries.

The implementation of advanced 'early fault detection' algorithms will help to optimize operation and maintain the high reliability of the system.