Publications
Sort:
Open Access Issue
A shortcut digital twin model for flexible distillation towers based on the adapted Edmister model (AEM)
Journal of Beijing University of Chemical Technology (Natural Science Edition) 2026, 53(4): 41-53
Published: 20 July 2026
Abstract PDF (874.6 KB) Collect
Downloads:0

The strict plate MESH model of the distillation column in the digital twin scenario suffers from problems such as large scale, sensitivity of convergence to initial values, and the need to reconstruct the equation system when switching operation regulations. In light of these problems, a modified AEM full-tower shortcut model (MAEM) including a condenser and a reboiler, along with its corresponding double-layer iterative flexible solution algorithm, is proposed based on the adapted Edmister model (AEM). The MAEM model explicitly incorporates the strict MESH equations for the condenser, reboiler and feed plate, as well as the AEM model for the tower section, into a single equation set, effectively reducing the model size. In our double-layer solution algorithm, the inner layer updates the desorption factor and solves the material balance for the given tower plate temperature. The outer layer adjusts the tower plate temperature to meet any two specified requirements, thereby enhancing the numerical robustness under non-standard conditions. Using the aromatic hydrocarbon separation device, the wide boiling range system, and the gas fractionation process level device as typical examples, our MAEM model was compared with the original AEM model and the RadFrac strict model in Aspen Plus. The results show that the MAEM model can perform internal tower calculations by directly and flexibly setting unconventional operation parameters, while maintaining the same calculation accuracy and efficiency as the AEM model. Compared with the RadFrac model, the relative deviations of the key operational parameters of the MAEM model are all less than 5%, and the calculation time has been reduced by approximately 31.6% to 51%. Our MAEM model provides an efficient and robust framework for the deployment of digital twins in distillation processes, significantly enhancing the application potential of these digital twins.

Total 1