952 50 693
- Unit:
- SINTEF Industry
- Department:
- Process Technology
- Office:
- Tiller
Publications
- Utilization of machine learning on FT-ICR MS spectra for improved understanding and prediction of theproperties of hydrate-active components
- A new high pressure method for successive accumulation of hydrate active components
- Developing machine learning models for identifying chemical components from wide and short FT-ICR mass spectrometry data
- Identifying components related to hydrate formation by machine learning-based variable selection
- Towards a machine learning based produced for interpretation of mass spectra for better understanding of hydrate phenomena in oil systems
- Successive accumulation of naturally occurring hydrate active components and the effect on the wetting properties
- Machine learning as a basis for better understanding of flow assurance through FT-ICR-MS analysis of gas hydrates
- Phase Transitions and Separation Time Scales of CO2-Crude Oil Fluid Systems: Wheel Flow Loop Experiments and Modelling
- Study on how oil type and weathering of crude oils affect interaction with sea ice and polyethylene skimmer material
- SINTEF Webinar "Bridging the gap between the lab and the field- Session2: Hydrate Management"