Auto sensor and machine learning driven design of electrochemical desalination units for high efficiency brine management

Electrochemical desalination offers an environmentally sustainable solution to water scarcity, yet brine management remains a significant efficiency challenge. This study implements an automated sensor and machine learning framework to optimize desalination units in Kozhikode, a coastal region facing acute saline intrusion and freshwater depletion. The research integrates real-time sensors with predictive algorithms to enhance brine management efficiency and evaluate machine learning performance in coastal environments. Sensors monitored brine dynamics, levels, and salinity, while Extreme Gradient Boosting (XGB) and Random Forest (RF) models generated predictions. Results revealed model-specific strengths. Random Forest excelled at brine level prediction, achieving 0.88 training accuracy and 0.903 test accuracy, while XGB performed poorly (0.511 and 0.23 respectively). Conversely, for salinity prediction, XGB achieved reasonable accuracy (0.869 training, 0.792 testing), though RF achieved near-perfect predictive accuracy (R² ≈ 1.0) in both phases. The findings demonstrate that Random Forest is superior for predicting brine levels and salinity, enabling efficient system design and operation. Combined sensor and machine learning technologies offer promising sustainable desalination solutions for water-stressed coastal communities.