Automated generative process synthesis via transformer‐based dual‐loop simulation and optimization

Abstract This study presents a novel framework for automated generative process synthesis, addressing the complexity of simultaneously optimizing discrete topologies and continuous operating variables. To overcome conventional superstructure limitations, we propose a dual‐loop architecture integrating generative transformers with rigorous process simulation. The framework utilizes SFILES to encode process structures into textual sequences, which are mapped onto a continuous embedding manifold via a transformer‐based model. The optimization is executed through a dual‐loop approach: the outer loop leverages Particle Swarm Optimization (PSO) to explore the embedding space for candidate topologies, while the inner loop serves as an automated evaluation agent that performs rigorous simulation‐based optimization within Aspen Plus®. Validated on cyclohexane, ethylene glycol, and CO 2 capture systems, the framework successfully rediscovered established industrial configurations and identified novel, superior alternatives. By bridging generative modeling with high‐fidelity physics‐based simulation, this work establishes a robust computational framework for the automated exploration and structural evolution of chemical processes.