Abstract
Artificial neural networks (ANNs) have gained popularity in the last years as tools to develop data-driven models of chemical process units. However, representing a system only with such artificial intelligence models may lead to a loss in the comprehension of the occurring phenomena. Hybrid models allow combining the predictive capabilities of ANNs with the foundational knowledge of rigorous models. This study explores the impact of hybridization in designing and optimizing shell-and-tube heat exchangers, comparing a full ANN-based model with a hybrid model. The hybrid model incorporates ANN predictions for highly nonlinear components, such as heat transfer coefficients, while other calculations are performed using the rigorous Bell-Delaware model. To generate the necessary data, the rigorous model is solved under randomly selected conditions. Using Python, one ANN predicts the exchanger's cost, while another predicts the heat transfer coefficients. Both models are optimized using the differential evolution algorithm. The results indicate that the hybrid model produces designs with an approximately 67% lower standard deviation compared to the full ANN-based model when compared to the rigorous model's cost predictions. This highlights the hybrid model’s ability to balance computational efficiency and accuracy, offering a promising approach for process design applications.