Prosjekter
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Publikasjoner
- Crude Oil Density Prediction Improved by Multiblock Analysis of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Fourier Transform Infrared, and Near-Infrared Spectroscopy Data
- Exploring the possibilities of a regression model for the prediction of wetting index from crude oils
- Combined Approach to Evaluate Hydrate Slurry Transport Properties through Wetting and Flow Experiments
- Current overview and way forward for the use of machine learning in the field of petroleum gas hydrates Les publikasjonen
- Using machine learning-based variable selection to identify hydrate related components from FT-ICR MS spectra Les publikasjonen
- 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