Multi-Parameter Optimization of Brine Desalination using Machine Learning

Air Gap Membrane Distillation (AGMD) is a promising desalination technology with significant potential for addressing the negative environmental impacts of brine disposal. However, the interplay of operational parameters significantly impacts its performance, making optimization a challenging task. This research focuses on brine desalination as a means to mitigate the negative environmental impacts of brine disposal. By optimizing the AGMD process, the study aims to provide a sustainable solution for handling brine while producing freshwater.

An ANN model is trained and validated using experimental data while varying membrane pore size, feed salinity and feed flow rate to predict two critical performance metrics: permeate flux and specific thermal energy consumption (STEC). Different activation functions and different numbers of neurons were tested. The ReLU activation function was found to be the most effective with 25 neurons resulting in a RMSE of 0.068. The model achieved an R² value of 0.92, 0.9123, and 0.9005 for the training, validation, and test datasets, respectively. For the combined dataset, the model achieved an R² value of 0.9156. While flux predictions yielded a slightly lower R² value of 0.8697, STEC predictions achieved the highest R² value of 0.9316, showcasing higher precision in the prediction of energy consumption metrics.

As for optimization, results for the 0.2 µm membrane reveal that optimal salinity levels depend on feed flow rate. At higher flow rates (> 1.5 lpm), a salinity of 65,000 ppm achieves superior performance, producing higher flux with relatively lower STEC compared to lower salinities. For the 0.45 µm membrane, higher salinity levels of 65,000 ppm generally result in lower STEC for a given flux across all flow rates. As indicated by the pareto front, the 0.2 µm membrane offers a more energy-efficient balance between water production and energy use compared to the 0.45 µm membrane.

Differential evolution is then applied to predict optimal performance metrics by assigning different weights to flux and STEC. This approach allows for the identification of operating conditions that best meet specific application needs, ensuring a tailored balance between water production and energy efficiency. By addressing the challenges of brine desalination through AGMD, this study provides a pathway for reducing the environmental risks associated with brine disposal. It also contributes to sustainable water management strategies by enabling the efficient recovery of freshwater from brine.