نوع مقاله : مقاله پژوهشی
نویسندگان
مهندسی شیمی، دانشگاه علم و صنعت ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
In this study, the modeling of carbon dioxide (CO2) absorption in a wet wall column were conducted using response surface methodology (RSM) and artificial neural networks (ANN). The input variables included the molality of the absorbent, inlet CO2 pressure, solvent loading (%), total system pressure, and gas flow rate, while the output variable was the outlet pressure of the wet wall column. The RSM approach was first applied to evaluate and optimize the system response, followed by prediction using two neural network architectures: Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. Based on the outlet pressure, the CO2 flux through the column could be calculated. In the MLP model, a mean squared error (MSE) of 3.2244 × 10⁻6 was achieved using two hidden layers with 3 and 7 neurons over 219 epochs. The R² value obtained from the RSM model using a quadratic function was 0.9998. For the neural networks, the R² values were 0.99999 and 0.99997 for the MLP (with Trainbr training function) and RBF models, respectively. The results demonstrate that both ANN and RSM approaches can effectively predict the outlet pressure of the wet wall column, offering reliable tools for modeling reactive CO2 absorption processes.
کلیدواژهها [English]
)