Improving hydrothermal carbonization prediction by machine learning: towards a more accurate and less expensive process modelling through data augmentation

Journal:
Energy
Published:
DOI:
10.1016/j.energy.2025.137373
Affiliations:
2
Authors:
4
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Institutions Authors Share
Kore University of Enna, Italy
3.000000
 0.75
University of Pisa (UNIPI), Italy
1.000000
 0.25
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