Abstract
Chemical process models often involve high non-linearity due to thermodynamic and kinetic relationships, with non-convex bilinear terms adding complexity to process optimization. Recently, data-driven models, particularly artificial neural networks (ANNs), have gained traction for representing chemical processing units. The predictive accuracy of ANNs depends on data quality, variable interactions, and network architecture, the latter being an optimization challenge itself. This study proposes and evaluates two strategies to optimize ANN architecture for modeling a propylene glycol production process from glycerol. The process includes a reactor and two distillation columns, with training data generated through simulation in Aspen Plus by varying design and operating variables. Two approaches are compared: random ANN structure generation and architecture optimization using the ant colony algorithm, a method suitable for discrete problems. Decision variables include the number of hidden layers and neurons per layer, with the objective of minimizing mean squared error. Both methods yield ANN predictions with high accuracy (R² > 99.9%), closely matching simulation data. However, the ant colony algorithm achieves a better fit despite slower convergence, demonstrating its effectiveness in fine-tuning ANN architecture for chemical process modeling.