Alberto Procacci is an Assistant Professor in the Aero-Thermo-Mechanics department at Université Libre de Bruxelles.
Alberto Procacci is also a Principal Investigator of the SWIFFT research collective. His research focuses on machine learning applied to energy systems, with particular emphasis on physics-constrained and probabilistic modelling. These approaches enable the development of digital twins and soft sensors for the real-time monitoring, optimisation, and control of energy-intensive systems.
What is your motivation to join DT-HATS?
The DT-HATS research project addresses critical scientific and technological challenges in the development of next-generation, zero-emission combustion systems, which are essential for enabling a sustainable energy transition.
I joined the DT-HATS consortium to actively contribute to this objective by employing my expertise in digital twins of combustion systems, physics-informed machine learning, and data-driven soft sensors. By integrating physical knowledge with experimental data, my work aims to support the design, optimisation, and control of clean combustion technologies.
- Procacci, A., Amaduzzi, R., Coussement, A., & Parente, A. (2023). Adaptive digital twins of combustion systems using sparse sensing strategies. Proceedings of the Combustion Institute, 39(4), 4257 4266. https://www.sciencedirect.com/science/article/pii/S1540748922000712
- Procacci, A., Donato, L., Amaduzzi, R., Galletti, C., Coussement, A., & Parente, A. (2024). Parameter estimation using a Gaussian process regression-based reduced-order model and sparse sensing: Application to a methane/air lifted jet flame. Flow, Turbulence and Combustion, 112(3), 879-895. https://link.springer.com/article/10.1007/s10494-023-00446-x
- Hafeez, M. A., Procacci, A., Coussement, A., & Parente, A. (2025). Constrained reduced-order modelling using bounded Gaussian processes for physically consistent reacting flow predictions. Energy and AI, 100554. https://www.sciencedirect.com/science/article/pii/S2666546825000862
- Procacci, A., Iavarone, S., Coussement, A., & Parente, A. (2025). Stochastic reduced-order modeling for the forecast of noisy dynamical systems. Proceedings of the Combustion Institute, 41, 105981. https://www.sciencedirect.com/science/article/pii/S1540748925001956