DTU
DC-1: Mercedes Belda Ley
Advanced sensitivity analysis and comprehensive validation methodology to improve credibility of QRAs studies in chemical process industries.
Funding: EU
Fellows
DTU
DC-2: Niklas Groll
Hybrid approaches for smart integration of AI/deep learning with model-based algorithms for online risk monitoring.
Funding: EU
NTNU
DC-3: Name of the DC candidate
Safety-aware predictive control methods with robust contingency plans.
Funding: EU
DTU
DC-4: Jingkang Liang
Statistical machine learning and big data analytics for online risk monitoring and fault diagnosis.
Funding: EU
NOVOTEC
DC-5: Lorenza Saturnino
Framework development for benchmark analysis and selection of QRA models.
Funding: EU
NTNU
DC-6: Robson Wilson Silva Pessoa
Hybrid, regressible, and robust machine learning solutions for abnormal event identification.
Funding: EU
KU
DC-7: Mohammadreza Jahanbakhsh
Physics-aware machine learning for control of chemical processes.
Funding: EU
UPC
DC-8: Muchen Zhang
Development of a new methodology using CFD-based tools for a better assessment of accident effects in QRA.
Funding: EU
UPC
DC-9: Jairo Andrés Meneses Gelves
Developing an improved methodology to perform QRA with specific emphasis on natural gas and other renewable and low-carbon fuels facilities.
Funding: EU
NTNU
DC-10: Eden Ngowi
Learning-based health-aware operation and maintenance planning for improved safety.
Funding: EU
IMPERIAL
DC-11: Julien Amblard
Symbolic machine learning solutions for abnormal event identification.
Funding: UKRI
IMPERIAL
DC-12: Christos Ziakas
Closed loop integration of hybrid machine learning and automated adaptation of chemical processes for safe process operation.
Funding: UKRI