Batch crystallization processes are prone to batch-to-batch inconsistencies arising from operational uncertainties and equipment-induced noise. This study presents a Robustness-Aware Genetic Algorithm (RAGA) integrated with a Long Short-Term Memory (LSTM) digital twin for the design of robust crystallization procedures. The RAGA employs a hierarchical fitness function that strictly enforces a target median crystal size D50 as the primary constraint while maximizing process yield as a secondary objective. Robustness is incorporated directly into the optimization by requiring candidate trajectories to satisfy the D50 specification across five independent stochastic realizations with perturbed operating conditions. A candidate is promoted in the evolutionary search only if all five evaluations produce a predicted D50 within ±2 µm of the target. The framework was applied to seeded cooling crystallization of creatine monohydrate across three target crystal sizes of 115, 125, and 135 µm. Robustness of optimal crystallization procedures was independently verified through 100-run Monte Carlo simulations under ±10% parameter perturbations with success defined as D50 within ±5 µm of target. Experimental validation at laboratory scale confirmed that optimized procedures translate to practice, with two of three target sizes achieved within the ±5 µm specification and the third deviating due to the combined effect of LSTM prediction uncertainty and thermal lag. Despite having no embedded mechanistic knowledge, the optimizer successfully converged on physically coherent crystallization strategies. Its variations in seed loading, batch time, and cooling trajectory parameters remained entirely consistent with established principles of supersaturation management. The results demonstrate that embedding robustness directly within the evolutionary optimization loop enables consistent crystal size control using data-driven models.
Vrban et al. (Mon,) studied this question.