Control structure design is an important but tedious step in P&ID development. Generative artificial intelligence (AI) promises to reduce P&ID development time by supporting engineers. Previous research on generative AI in chemical process design mainly represented processes by sequences. However, graphs offer a promising alternative because of their permutation invariance. We propose the Graph-to-SFILES model, a generative AI method to predict control structures from flowsheet topologies. The Graph-to-SFILES model takes the flowsheet topology as a graph input and returns a control-extended flowsheet as a sequence in the SFILES 2.0 notation. We compare four different graph encoder architectures, one of them being a graph neural network (GNN) proposed in this work. The Graph-to-SFILES model achieves a top-5 accuracy of 73.2% when trained on 10,000 flowsheet topologies. In addition, the proposed GNN performs best among the encoder architectures. Compared to a purely sequence-based approach, the Graph-to-SFILES model improves the top-5 accuracy for a relatively small training dataset of 1,000 flowsheets from 0.9% to 28.4%. However, the sequence-based approach performs better on a large-scale dataset of 100,000 flowsheets. These results highlight the potential of graph-based AI models to accelerate P&ID development in small-data regimes but their effectiveness on industry relevant case studies still needs to be investigated.

翻译：控制结构设计是管道及仪表流程图开发中重要但繁琐的步骤。生成式人工智能有望通过辅助工程师来缩短管道及仪表流程图的开发时间。先前关于化学过程设计中生成式人工智能的研究主要采用序列表示过程。然而，图因其排列不变性而成为一种有前景的替代方案。我们提出了Graph-to-SFILES模型，这是一种基于生成式人工智能的方法，用于从流程拓扑预测控制结构。该模型以流程拓扑作为图输入，并返回以SFILES 2.0符号表示的控制扩展流程序列。我们比较了四种不同的图编码器架构，其中一种为本工作提出的图神经网络。当在10,000个流程拓扑上进行训练时，Graph-to-SFILES模型实现了73.2%的top-5准确率。此外，所提出的图神经网络在编码器架构中表现最佳。与纯序列方法相比，在相对较小的1,000个流程训练数据集上，Graph-to-SFILES模型将top-5准确率从0.9%提升至28.4%。然而，序列方法在100,000个流程的大规模数据集上表现更优。这些结果突显了基于图的人工智能模型在小数据场景下加速管道及仪表流程图开发的潜力，但其在工业相关案例研究中的有效性仍需进一步探究。