Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence
arXiv:2412.00508v2 Announce Type: replace Abstract: 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 topolog
The increasing maturity of generative AI models and the demand for efficiency in complex engineering tasks are driving innovation in this area.
This research signifies a step towards automating complex, tedious engineering design processes, potentially accelerating industrial development and innovation.
Traditional manual control structure design in process engineering could be significantly augmented or potentially replaced by AI-driven generative methods.
- · Chemical Process Engineering firms
- · Generative AI developers
- · Automation software providers
- · Manufacturing sectors
- · Junior process engineers (routine tasks)
- · Legacy CAD software vendors
Reduced P&ID development times and associated costs in chemical process design.
Increased complexity and scale of processes that can be designed efficiently, leading to new industrial capabilities.
The development of fully autonomous design-to-production pipelines for various industrial processes.
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Read at arXiv cs.LG