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P&ID开发中的重要但繁琐步骤。生成式人工智能有望通过支持工程师来缩短P&ID开发时间。以往关于化学过程设计中生成式AI的研究主要采用序列表示过程。然而，图由于其排列不变性而成为一种有前景的替代方案。我们提出Graph-to-SFILES模型，一种从流程图拓扑预测控制结构的生成式AI方法。该模型以流程图拓扑作为图输入，并以SFILES 2.0符号序列形式返回控制扩展流程图。我们比较了四种不同的图编码器架构，其中一种是本文提出的图神经网络。Graph-to-SFILES模型在10000个流程图拓扑上训练时达到73.2%的前5准确率。此外，本文提出的GNN在编码器架构中表现最佳。与纯序列方法相比，在仅1000个流程图的较小训练数据集上，Graph-to-SFILES模型将前5准确率从0.9%提升至28.4%。然而，在100000个流程图的大规模数据集上，序列方法表现更优。这些结果凸显了基于图的AI模型在数据稀缺场景下加速P&ID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。