Performance Prediction and Analysis of Sweeping Gas Membrane Distillation Through Systematic Optimization of ANN Architectures

Sweeping gas membrane distillation (SGMD) is a thermally driven desalination process that uses a membrane to remove salts from seawater, achieving high salt-rejection rates at relatively low operating temperatures. Considering the coupled nature of heat and mass transfer and the nonlinearities in the process, predicting performance is challenging. However, analytical methodologies do not always accurately predict nonlinear interactions among SGMD operating parameters. On the other hand, artificial neural network (ANN) methodologies have been applied to desalination, but limited architectural configurations and suboptimal hyperparameter tuning lead to poor predictive performance. Therefore, the purpose of this study is to systematically optimize an ANN model to predict the SGMD Performance Index. The study built a total of 108 ANN configurations by varying network depth, neuron distribution, activation functions, optimization methods/learning rates. Fifty-three experimental data points were created from actual SGMD operating conditions to train/validate/test the ANN models. The performance of the ANN models was evaluated using R2, MSE, and MAE. From the results, we found that the ANN architecture and hyperparameter settings significantly affect predictive accuracy. The best-performing ANN model comprises four hidden layers [3:128:64:32:16:1] with ReLU activation, Adamax optimization, and a learning rate of 0.0050, yielding an R2 of 0.9321. These findings indicate that implementing systematic ANN optimization can serve as a data-driven methodology for modeling SGMD performance, aiding process optimization and future scale-up.