Digital design and optimization of the integrated synthesis and crystallization process using data-driven approaches

This study presents a data-driven modeling and multi-objective optimization framework for an integrated section of continuous pharmaceutical manufacturing, focusing on flow synthesis and continuous crystallization. To address data scarcity and trade-offs among product quality, efficiency, and environmental impact, the framework combines generative adversarial networks (GANs), artificial neural networks (ANNs), and genetic algorithms (GAs). An integrated dual-GAN (ID-GAN) generates data under physicochemical constraints, which are merged with real data to train an ANN with 15%–20% mean absolute errors for particle size, productivity, and a sustainability throughput index. The ANN is then coupled with a GA to identify Pareto-optimal solutions based on user-defined objectives and constraints. Case studies validate the framework's capability to facilitate process design decisions by systematically exploring trade-offs among competing objectives, underscoring its potential utility in the digitalization of critical units within continuous manufacturing systems.