Optimizing high-salinity wastewater desalination via deep neural networks and reinforcement learning

作者：Zhichen Wang, Jing Dong, Siyu Zhang, Yuheng Zhang, Xiangjin Li · 发表于：IET conference proceedings. · 年份：2026 · DOI：10.1049/icp.2025.4445 · 研究领域：Membrane Separation Technologies、Hydrological Forecasting Using AI、Membrane-based Ion Separation Techniques

The Yellow River Basin, a critical ecological zone in China, faces escalating challenges from high-salinity wastewater discharge, particularly in industrial regions along its course. The study's computational framework centers on a hybrid deep neural network (DNN) architecture, where Long Short Term Memory (LSTM) networks capture temporal salinity dynamics and reinforcement learning (RL) optimizes operational parameters, achieving a 23% RMSE reduction over conventional methods. The proposed system uniquely integrates spatiotemporal feature extraction through a dual-path DNN-LSTM architecture, enabling real-time adaptation to complex salinity-organic compound interactions in hypersaline wastewater (TDS >5,000 mg/L). The study proposes a DNN model to optimize low-energy desalination processes for high-salinity wastewater. The research integrates real-time salinity monitoring data with a hybrid DNN framework combining LSTM networks and reinforcement learning. The model dynamically adjusts desalination parameters to optimize energy efficiency through adaptive parameter tuning. Field validation at Xiaolangdi Reservoir demonstrated 40% energy savings (2.1 kWh/m³) while maintaining 100% compliance with China's Class IV water standards, significantly outperforming traditional methods in fluctuating industrial conditions. This study advances sustainable wastewater management through AI-driven optimization of desalination processes. Its findings provide actionable insights for optimizi...