Toward automatic generation of control structures for process flow diagrams with large language models

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.

References in corpus (7)

• Sequence to Sequence Learning with Neural Networks
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
• SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
• Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
• Toward automatic generation of control structures for process flow diagrams with large language models
• SFILES 2.0: An extended text-based flowsheet representation

Cited by in corpus (5)

• El Agente: An Autonomous Agent for Quantum Chemistry
• Toward automatic generation of control structures for process flow diagrams with large language models
• Toward autocorrection of chemical process flowsheets using large language models
• Autonomous Industrial Control using an Agentic Framework with Large Language Models
• Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence