arXiv cs.AI· Thomas Pallet (CRAN, LIFIA, Mgib), Mario Lezoche (CRAN, LS2N - {\'e}quipe CPS3, LS2N), Gianpaolo Leone (ENEA), Daniele Pizzichini (ENEA), Diego Torres (LIFIA, UNQ), Herv{\'e} Panetto (CRAN), M{\'e}lanie Glibusic (Mgib)·· 2 小时前AI 评分24
SFE-O:面向超临界 CO2 生物质萃取与分级知识驱动 AI 的本体
SFE-O: An Ontology for Knowledge-Grounded AI in Supercritical CO$_2$ Biomass Extraction and Fractionation
AI 导读
Thomas Pallet 等研究者提出 SFE-O,一个基于 OWL 2 的模块化本体,用于超临界 CO2 生物质萃取(CO2-SFE),按 Biomass、Process、Product 三个维度组织,并显式表征过程拓扑、参数溯源、传质现象及 CER/FER 机制。
正文 · 原文
Authors:Thomas Pallet (CRAN, LIFIA, Mgib), Mario Lezoche (CRAN, LS2N - {é}quipe CPS3, LS2N), Gianpaolo Leone (ENEA), Daniele Pizzichini (ENEA), Diego Torres (LIFIA, UNQ), Herv{é} Panetto (CRAN), M{é}lanie Glibusic (Mgib)
Abstract:Supercritical CO$_2$ extraction (CO$_2$-SFE) offers a sustainable route for recovering high-value compounds from biomass while limiting the use of conventional organic solvents, but its design and interpretation require heterogeneous knowledge spanning biomass properties, operating conditions, mass transfer, and product fractionation, typically fragmented across technical reports, engineering models, external databases, and expert practice. This paper introduces SFE-O, a modular Web Ontology Language (OWL 2) ontology for supercritical CO$_2$ biomass extraction, structured around interconnected Biomass, Process, and Product dimensions and extended with explicit representations of process topology, parameter provenance, external and internal mass-transfer phenomena, and the Constant Extraction Rate (CER) / Falling Extraction Rate (FER) mechanisms. Computational dependencies among process quantities are represented explicitly, without embedding numerical solving in the ontology, so that input, measured, computed, externally retrieved, and fitted parameters remain traceable. SFE-O reuses established semantic resources (ChEBI, QUDT, OWL-Time, PROV-O) and is evaluated through a worked example that instantiates a real pilot-scale extraction batch, a reasoner-based consistency check, and a competency-question SPARQL query. The resulting knowledge model provides a machine-actionable foundation for querying, consistency checking, and mechanistic explanation of SFE processes, and establishes a knowledge layer that could support future ontology-grounded AI reasoning, a direction this paper motivates but does not evaluate.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37123 [cs.AI] |
| (or arXiv:2610.37123v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37123 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | IEEE ICAI-TEMS 2026, International Conference on Application of Information Technologies in Engineering, Management and Science, Nov 2026, Pise (Italie), Italy |
Submission history
From: Thomas Pallet [view email] [via CCSD proxy]
[v1]
Tue, 27 Oct 2026 12:21:50 UTC (108 KB)
来源:arXiv cs.AI · arxiv.org