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.

翻译：开发管道与仪表流程图（P&ID）是工艺开发中的关键步骤。我们提出了一种数据驱动的控制结构预测方法，其灵感来源于端到端基于Transformer的人类语言翻译模型。我们将控制结构预测视为一项翻译任务：将不含控制结构的工艺流程图（PFD）转化为包含控制结构的工艺流程图。利用SFILES 2.0标记法将工艺流程图拓扑结构表示为字符串。模型先通过生成的流程图进行预训练以学习语法结构，再基于真实流程图通过迁移学习进行微调。该模型在10,000个生成流程图上达到74.8%的top-5准确率，在100,000个生成流程图上达到89.2%的准确率。这些成果展现了人工智能辅助工艺工程的巨大潜力。对312个真实流程图的测试表明，工业应用需要更大规模的流程图数据集及混合人工智能解决方案。