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Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures — arXiv:2608.11255

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.11255
License (as archived): CC0-1.0
Archived: 2026-10-19T05:15:17.282Z
SHA-256 of the archived text: 6b103181a0ef97b80e872c5a6ce3c2128b0421c4c8fe3642e3a95e74e3a38a8b
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How to cite

Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures — arXiv:2608.11255 — arXiv (en), https://arxiv.org/abs/2608.11255 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d9acab9dfd04 (Metraipolis archive dc_d9acab9dfd04, sha256 6b103181a0ef97b80e872c5a6ce3c2128b0421c4c8fe3642e3a95e74e3a38a8b)

BibTeX

@misc{dcd9acab9dfd04,
  title = {Symbolic Machine Learning for Vapor-Liquid Equilibrium Prediction in Cx-N2 Binary Mixtures — arXiv:2608.11255},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.11255},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d9acab9dfd04; sha256 6b103181a0ef97b80e872c5a6ce3c2128b0421c4c8fe3642e3a95e74e3a38a8b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.11255. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Accurate prediction of vapor--liquid equilibrium (VLE) for hydrocarbon-nitrogen mixtures remains challenging for cubic equations of state, particularly across broad ranges of composition and hydrocarbon chain length. While deep learning models can provide accurate predictions, they often lack interpretability and explicit analytical expressions. In this work, we propose a symbolic machine learning approach to discover interpretable symbolic corrections to Peng-Robinson equation-of-state (PR-EOS) predictions from experimental data. The proposed approach adopts a two-level strategy: symbolic expressions are first identified for individual hydrocarbon systems, after which their coefficients are represented as functions of carbon number to enable accurate prediction across different hydrocarbon systems. The results demonstrate significantly improved prediction accuracy over the original PR-EOS across all hydrocarbon-nitrogen systems. Overall, the proposed approach provides an interpretable symbolic correction framework for improving PR-EOS predictions of hydrocarbon-nitrogen VLE.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-13 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG); Computational Physics (physics.comp-ph).
Pagina del lavoro: https://arxiv.org/abs/2608.11255 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:08:46.616Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).