Reverse osmosis (RO) desalination, a process responsible for producing nearly half of the world’s desalinated water, consumes between 3 to 4 kWh per cubic meter—making it one of the most energy-intensive water treatment methods. Spanish infrastructure group ACCIONA is now targeting that inefficiency through a dual-model optimization system that blends predictive simulation with artificial intelligence, signaling a data-driven shift in how large-scale desalination plants are managed.
At the center of this initiative is ACRRO® (Advanced Control Reverse Osmosis Rack Optimization)—a simulation-based platform developed by ACCIONA’s Innovation Department. Using advanced optimization algorithms over a set of differential equations, ACRRO® models rack behavior to determine the most energy-efficient operating points. Paired with Insight, a real-time AI system trained on live operational data, the combined architecture forms a self-learning feedback loop: the simulation predicts ideal configurations, while the AI dynamically adjusts the process based on observed performance.
The system has already been implemented at one of ACCIONA’s desalination facilities in Qatar. There, it demonstrated measurable improvements in specific energy consumption and throughput consistency, particularly under fluctuating salinity and temperature conditions. The deployment is being remotely monitored by CECOA, ACCIONA’s centralized Water Control Centre in Madrid, providing a proof of concept for scaling across its global portfolio of desalination assets.
According to Guillermo Hijós, O&M Desalination Middle East and Oceania Director at ACCIONA, the approach bridges the predictive precision of simulation models with the adaptability of AI, representing a shift toward “data-centric water operations” rather than traditional fixed-parameter management. This is particularly relevant for energy optimization—where even a 1% efficiency gain in large RO plants can translate to significant cost and carbon savings.
Beyond operational optimization, ACCIONA is embedding predictive analytics deeper into its desalination ecosystem. Its Turbidity Prediction System employs AI and continuous water quality monitoring to forecast variations in feedwater clarity, enabling operators to preempt membrane fouling or mechanical stress. Meanwhile, a satellite-AI model tracks marine hazards—including algae blooms and oil slicks—helping operators mitigate intake contamination risks before they impact plant availability. Both tools are integrated into CECOA’s digital platform, enhancing coordination between regional plants and central operations.
This convergence of AI, simulation, and remote monitoring reflects an emerging trend across the water industry: the digitalization of process control as a means to balance sustainability with cost competitiveness. While the technical success of ACCIONA’s system remains tied to continued data integrity and adaptive calibration, its early field performance suggests a viable path toward lowering desalination’s energy intensity—one of the sector’s most persistent constraints.
By deploying predictive control technologies directly into O&M workflows, ACCIONA demonstrates how incremental digitalization can produce cumulative sustainability gains. As water scarcity intensifies and desalination’s role expands, such innovations may determine not only who leads the market—but who manages to do so efficiently enough to keep it viable.
