Matches in Ghent University Academic Bibliography for { <https://biblio.ugent.be/publication/01JJ1V7Y9JS012VBKEHJ3N6J3T> ?p ?o. }
Showing items 1 to 25 of
25
with 100 items per page.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T classification A1.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T date "2025".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T language "eng".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T type journalArticle.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T hasPart 01JJ1VFRM3D427TP71DAPXP7S6.pdf.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T hasPart 01JJRMHF3ZYME8PJBB4MAX2H68.pdf.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T subject "Mathematics and Statistics".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T subject "Technology and Engineering".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T doi "10.1016/j.fuel.2024.133218".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T issn "0016-2361".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T issn "1873-7153".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T issue "B".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T volume "381".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T abstract "Renewable fuels offer a sustainable option for engine applications where electrification is more challenging, not possible. To evaluate the potential of novel fuels it is crucial to first determine their combustion and spray related properties. This can be done experimentally, but during screening of multiple fuel candidates this can be cost and time expensive. Machine learning can be used for rapid, inexpensive, and accurate predictions of fuel mixture properties. To this end a novel function for pooling molecular representations called MolPool has been developed, which was combined with graph neural networks. The new approach processes the input permutation invariant, allowing for application to a varying number of components in the mixture. In this article, three different compression ignition engine related properties were investigated: derived cetane number (DCN), flashpoint, and viscosity. The results show that this novel neural network approach is able to increase the prediction accuracy and the generalizibility compared to traditional blending laws for all investigated properties. MolPool improves the prediction if oxygenated species are present in the mixture resulting non-linear mixture behavior, which is common for renewable fuels. Thus, MolPool shows great potential for prediction of various properties and fuel mixtures.".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T author 403f775a-4be8-11ec-838f-dde4dd96b4d8.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T author F5C38C8E-F0ED-11E1-A9DE-61C894A0A6B4.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T author urn:uuid:10762332-883d-4a08-b23f-f39b4385962d.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T author urn:uuid:a86c993d-3609-4337-9114-a87466ece524.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T dateCreated "2025-01-20T12:33:17Z".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T dateModified "2025-02-10T08:48:18Z".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T name "Property prediction of fuel mixtures using pooled graph neural networks".
- 01JJ1V7Y9JS012VBKEHJ3N6J3T pagination urn:uuid:ea978692-d944-4454-8f15-5793e5d7532b.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T sameAs LU-01JJ1V7Y9JS012VBKEHJ3N6J3T.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T sourceOrganization urn:uuid:ed79acb3-f46c-4fc9-a607-51ba02097482.
- 01JJ1V7Y9JS012VBKEHJ3N6J3T type A1.