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

Abstract 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.