AI for Control of Hybrid Desalination Systems (RO + RED/ED)

Hybrid desalination systems combining Reverse Osmosis (RO) with Reverse Electrodialysis (RED) or Electrodialysis (ED) offer high water recovery and improved energy efficiency. However, determining the optimal flow distribution and operating mode in real time is complex due to fluctuating feedwater conditions, brine salinity, and energy prices. The goal is to automate control decisions to maximize performance and sustainability.

SensQuant uses reinforcement learning and predictive models to continuously monitor sensor data, electricity rates, and water quality. The AI model dynamically selects the most efficient operation strategy — deciding when to route brine to RED or ED modules, adjust pressures, or shift flow balance. The system evolves over time, learning from each operational cycle to fine-tune performance.

With AI-assisted control, hybrid systems achieve superior energy efficiency and reduced CO₂ emissions. Operators benefit from real-time optimization, lower operating costs, and automated adjustments that respond to changing external and internal conditions without manual intervention.