论文标题
使用人工智能自动生成管道和仪器图（P＆ID）
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence
论文作者
论文摘要
开发管道和仪器图（P＆ID）是化学过程开发过程中的关键步骤。目前，这是一项乏味，手动和耗时的任务。我们为预测控制结构提出了一种新颖的，完全数据驱动的方法。我们的方法受到端到端变压器的人类语言翻译模型的启发。我们将控制结构预测作为翻译任务，其中过程流程图（PFD）转换为P＆ID。要使用既定的基于变压器的语言翻译模型，我们使用我们最近提出的SFILE 2.0表示法代表P＆IDS和PFD作为字符串。模型培训是以转移学习方法进行的。首先，我们使用生成的P＆ID预先培训模型来学习过程图的语法结构。此后，该模型是对实际P＆ID上的转移学习的微调。该模型在10,000个生成的P＆ID上实现了74.8％的前5个精度，在100,000个生成的P＆ID上获得了89.2％。这些有希望的结果显示了AI辅助工程工程的巨大潜力。 312个实际P＆ID的数据集上的测试表明需要更大的P＆ID数据集来用于行业应用程序。
Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during the development of chemical processes. Currently, this is a tedious, manual, and time-consuming task. We propose a novel, completely 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) are translated to P&IDs. To use established transformer-based language translation models, we represent the P&IDs and PFDs as strings using our recently proposed SFILES 2.0 notation. Model training is performed in a transfer learning approach. Firstly, we pre-train our model using generated P&IDs to learn the grammatical structure of the process diagrams. Thereafter, the model is fine-tuned leveraging transfer learning on real P&IDs. The model achieved a top-5 accuracy of 74.8% on 10,000 generated P&IDs and 89.2% on 100,000 generated P&IDs. These promising results show great potential for AI-assisted process engineering. The tests on a dataset of 312 real P&IDs indicate the need of a larger P&IDs dataset for industry applications.
