SFILES 2.0: An extended text-based flowsheet representation
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Abstract (arXiv)
SFILES are a text-based notation for chemical process flowsheets. They were originally proposed by d'Anterroches (Process flow sheet generation & design through a group contribution approach) who was inspired by the text-based SMILES notation for molecules. The text-based format has several advantages compared to flowsheet images regarding the storage format, computational accessibility, and eventually for data analysis and processing. However, the original SFILES version cannot describe essential flowsheet configurations unambiguously, such as the distinction between top and bottom products. Neither is it capable of describing the control structure required for the safe and reliable operation of chemical processes. Also, there is no publicly available software for decoding or encoding chemical process topologies to SFILES. We propose the SFILES 2.0 with a complete description of the extended notation and naming conventions. Additionally, we provide open-source software for the automated conversion between flowsheet graphs and SFILES 2.0 strings. This way, we hope to encourage researchers and engineers to publish their flowsheet topologies as SFILES 2.0 strings. The ultimate goal is to set the standards for creating a FAIR database of chemical process flowsheets, which would be of great value for future data analysis and processing.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 0 of its 26 references that are in the map. First posted on arXiv 25 Jul 2022.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 2; 2025: 0; 2026: 3.
10 papers.
- 2022Toward automatic generation of control structures for process flow diagrams with large language modelscited by 12
- 2022Learning from flowsheets: A generative transformer model for autocompletion of flowsheetscited by 10
- 2023Deep reinforcement learning for process design: Review and perspectivecited by 10
- 2024Generative AI and Process Systems Engineering: The Next Frontiercited by 9
- 2023Toward autocorrection of chemical process flowsheets using large language modelscited by 6
- 2024Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligencecited by 5
- 2026Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulationscited by 2
- 2023Data augmentation for machine learning of chemical process flowsheetscited by 0
- 2026CRAFTS: Collaborative Role-Adaptive Fine-Tuning of LLM Agents for Chemical Process Simulationcited by 0
- 2026LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDscited by 0
- 2026CRAFTS: Collaborative Role-Adaptive Fine-Tuning of LLM Agents for Chemical Process Simulation2 Aug 2026
- 2026LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDs21 Jul 2026
- 2026Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations13 Mar 2026
- 2024Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence30 Nov 2024
- 2024Generative AI and Process Systems Engineering: The Next Frontier15 Feb 2024
- 2023Toward autocorrection of chemical process flowsheets using large language models5 Dec 2023
- 2023Deep reinforcement learning for process design: Review and perspective15 Aug 2023
- 2023Data augmentation for machine learning of chemical process flowsheets7 Feb 2023
- 2022Toward automatic generation of control structures for process flow diagrams with large language models26 Oct 2022
- 2022Learning from flowsheets: A generative transformer model for autocompletion of flowsheets1 Aug 2022