Expandable neural networks for efficient modeling of various amine scrubbing configurations for CO2 capture

• Published in: Chemical engineering science 2023-11, Vol.281, p.119191, Article 119191
• Main Authors: Hsiao, Yu-Da, Chang, Chuei-Tin
• Format: Article
• Language: English
• Subjects: Amine scrubbing configurations; Expandable neural networks; Incremental learning; Model interpretability; Model reusability; Post-combustion CO2 capture
• ISSN: 0009-2509
• DOI: 10.1016/j.ces.2023.119191

•An innovative modeling strategy for various amine scrubbing processes.•Expandable ANN-fabrication method for efficient process model building.•Existing models can be expanded laterally to form better new ones.•Parameter capacities can be enlarged in a step-by-step fashion. Modeling of improved amine scrubbers using artificial neural networks (ANNs) were carried out in this study. Instead of training models from scratch with the case-by-case method, the expandable neural networks were utilized to progressively increase the number of model parameters and change the model input/output structures in a step-wise fashion. Efficient and rapid transformation of any existing model to a new one can be made realizable. This proposed strategy has been successfully validated in several process modification scenarios. From the experimental results, the required sampling sizes to achieve the similar prediction accuracy of the corresponding baseline model were considerably smaller, and, furthermore, over 47% of total data acquisition time can be saved. Finally, the corresponding sensitivity analyses showed that the proposed models were physically interpretable and able to extract the correct process mechanisms in the sense that the gain scales and signs were consistent with those of their rigorous counterparts.