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This study frames control structure prediction for process flow diagrams as a translation task using transformer-based large language models, achieving strong top-5 accuracy on synthetic data but highlighting a significant gap when tested on limited real-world industrial datasets. The approach relies on pretraining with generated data followed by fine-tuning via transfer learning, suggesting that hybrid AI solutions and larger domain-specific datasets are necessary before practical deployment. While the methodology offers a promising direction for automating engineering workflows, its current reliance on synthetic training data underscores the need for robust, industry-scale benchmarks to validate generalizability.
Toward automatic generation of control structures for process flow diagrams with large language models
arXiv:2211.05583v2 Announce Type: replace Abstract: Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during process development. We propose a data-driven method for the prediction of control structures. Our methodology is inspired by end-to-end transformer-based human language translation models. We cast the control structure prediction as a translation task where Process Flow Diagrams (PFDs) without control structures are translated to PFDs with control structures. We represent the topology of PFDs as strings using the SFILES 2.0 notation. We pretrain our model using generated PFDs to learn the grammatical structure. Thereafter, the model is fine-tuned leveraging transfer learning on real PFDs. The model achieved a top-5 accuracy of 74.8% on 10,000 generated PFDs and 89.2% on 100,000 generated PFDs. These promising results show great potential for AI-assisted process engineering. The tests on a dataset of 312 real PFDs indicate the need for a larger PFD dataset for industry applications and hybrid artificial intelligence solutions.
Abstract:Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during process development. We propose a data-driven method for the prediction of control structures. Our methodology is inspired by end-to-end transformer-based human language translation models. We cast the control structure prediction as a translation task where Process Flow Diagrams (PFDs) without control structures are translated to PFDs with control structures. We represent the topology of PFDs as strings using the SFILES 2.0 notation. We pretrain our model using generated PFDs to learn the grammatical structure. Thereafter, the model is fine-tuned leveraging transfer learning on real PFDs. The model achieved a top-5 accuracy of 74.8% on 10,000 generated PFDs and 89.2% on 100,000 generated PFDs. These promising results show great potential for AI-assisted process engineering. The tests on a dataset of 312 real PFDs indicate the need for a larger PFD dataset for industry applications and hybrid artificial intelligence solutions.
Computation and Language (cs.CL); Optimization and Control (math.OC)
arXiv:2211.05583 [cs.CL]
(or arXiv:2211.05583v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2211.05583 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1002/aic.18259 Focus to learn more DOI(s) linking to related resources
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