CORAL: A Reproducible Hybrid Topology-Search/NLP Architecture for Chemical Process Superstructure Optimization

Chemical process synthesis combines discrete topology choices and continuous operating decisions. This study presents CORAL as a reproducible hybrid topology-search/constrained-NLP architecture and evaluates the contributions of its reef-specific diversity mechanisms. On the classical eight-process benchmark (12 admissible topologies), fixed-topology NLP is decisive for feasibility; a deterministic BARON solution provides a global bound and is preferable for this small algebraic MINLP. A topology-aware stopping rule reduces median CORAL oracle calls by 73.8% without loss of reference-topology recovery in 30 runs. To investigate structural scaling, a separately frozen reactor–separation network benchmark with 64, 256 and 1024 possible topologies is evaluated using 30 matched seeds for each of GA+NLP, CRO-like+NLP and CORAL+NLP under a common 500,000-call oracle budget (270 runs). On the largest network CORAL explores a median 789.5 unique topologies versus 540.5 for GA and 470 for CRO-like and recovers the enumerated reference topology in 30/30 runs versus 28/30 and 29/30. However, GA achieves stricter unpolished feasibility (30/30 versus 22/30 for each reef variant); common fixed-topology SQP polishing restores strict feasibility for all 270 incumbents, with additional evaluations reported separately. The results demonstrate an effect on cumulative structural exploration but do not establish universal superiority in feasible solution quality or computational efficiency.