AI Desalination Energy Management
The Problem
“Cut desalination energy costs amid volatile grids”
Organizations face these key challenges:
High and volatile electricity costs (real-time pricing, demand charges) with limited ability to shift load without risking water supply commitments
Non-linear process performance changes (membrane fouling, intake salinity/temperature shifts, pump/ERD degradation) that increase kWh/m3 and are hard to capture in static models
Fragmented data across SCADA, meters, maintenance systems, and market feeds, preventing timely, plant-wide optimization and predictive action
Impact When Solved
Real-World Use Cases
AI in Energy Industry: Smart Grid Optimization and Energy Management
This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.
Artificial Intelligence for Energy Systems
Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.