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Toward autocorrection of chemical process flowsheets using large language models
The model achieves a top-1 accuracy of 80% and a top-5 accuracy of 84% on an independent test dataset of synthetically generated flowsheets.
Data augmentation for machine learning of chemical process flowsheets
Artificial intelligence has great potential for accelerating the design and engineering of chemical processes.
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence
Thereafter, the model is fine-tuned leveraging transfer learning on real P&IDs.
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
We pre-train our model on synthetically generated flowsheets to learn the flowsheet language grammar.
SFILES 2.0: An extended text-based flowsheet representation
SFILES is a text-based notation for chemical process flowsheets.
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