Analysis of hydraulic performance in a structured packing column for air/water system: RSM and ANN modeling

Abstract

The study investigated the hydraulic characteristics of a structured packing column for an air/water system using two modeling approaches: artificial neural network (ANN) and response surface methodology (RSM). Seven parameters were identified as influential factors for pressure drop and liquid hold-up. Two correlations for the responses were proposed using the two-factor interaction (2FI) model based on the RSM-central composite design approach. In the ANN modeling, two algorithms were applied: multi-layer perceptron (MLP) and radial basis function (RBF). The optimal MLP structure using the Bayesian Regularization (trainbr) algorithm had two hidden layers, 16 and 8 neurons, and the optimal RBF structure had 42 neurons. The best MSE performance of the MLP network was 0.0938 and 0.0083 for pressure drop and hold-up, respectively, at 16 epochs. The best MSE performance of the RBF network was 0.0685 and 0.0140 for pressure drop and hold-up, respectively, at 42 epochs. Both the ANN and RSM models provided reliable predictions of the experimental data. In addition, ANN's prediction performance (R2 = 0.993) was somewhat better than RSM's (R2 = 0.926). The results showed that gas factor and liquid load had the greatest effect on pressure drop and liquid hold-up, respectively.