论文标题
从流程表中学习：用于自动完成流程的生成变压器模型
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
论文作者
论文摘要
我们提出了一种新的方法，可以使化学流程图自动完成。这个想法的灵感来自文本的自动完成。我们使用基于文本的SFILE 2.0表示法表示流程图为字符串，并使用基于变压器的语言模型在流程图中学习SFILE 2.0语言和常见模式的语法结构。我们将模型预先培训，以了解合成生成的流程表，以学习流语言语法。然后，我们将模型在真实流程图拓扑的转移学习步骤中微调。最后，我们使用训练有素的因果语言建模模型来自动完成流程表。最终，所提出的方法可以在交互式流动表合成过程中为化学工程师提供建议。结果表明，这种方法对于未来的AI辅助过程合成具有很高的潜力。
We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowsheets as strings using the text-based SFILES 2.0 notation and learn the grammatical structure of the SFILES 2.0 language and common patterns in flowsheets using a transformer-based language model. We pre-train our model on synthetically generated flowsheets to learn the flowsheet language grammar. Then, we fine-tune our model in a transfer learning step on real flowsheet topologies. Finally, we use the trained model for causal language modeling to autocomplete flowsheets. Eventually, the proposed method can provide chemical engineers with recommendations during interactive flowsheet synthesis. The results demonstrate a high potential of this approach for future AI-assisted process synthesis.
