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Multi-output machine learning models for kinetic data evaluation : a Fischer–Tropsch synthesis case study
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Efficient promoters and reaction paths in the CO2 hydrogenation to light olefins over zirconia-supported iron catalysts
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Bridging kinetic modelling and clustering machine learning approaches for catalyst design
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- Conference Paper
- C3
- open access
Recognition of efficient promoters for light olefin production from CO2 hydrogenation reaction over zirconia supported iron catalysts using high throughput experimentation
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- Journal Article
- A1
- open access
Machine learning based interpretation of microkinetic data : a Fischer-Tropsch synthesis case study
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- Journal Article
- A1
- open access
Unravelling the influence of catalyst properties on light olefin production via Fischer-Tropsch synthesis : a descriptor space investigation using Single-Event MicroKinetics
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Une solution pour valoriser de déchets plastiques en eléfines légères
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Active phases for high temperature Fischer-Tropsch synthesis in the silica supported iron catalysts promoted with antimony and tin
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- Journal Article
- A1
- open access
Bismuth mobile promoter and cobalt-bismuth nanoparticles in carbon nanotube supported Fischer-Tropsch catalysts with enhanced stability
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Effect of Rh in Ni-based catalysts on sulfur impurities during methane reforming