The AI Digital Platform for SWRO by SAWACO (EGDC)

From Fixed Operations to AI-Driven Efficiency: SAWACO’s Smart Desalination Solution

What if desalination plants could cut energy use not by upgrading hardware, but by making existing systems smarter? That’s the vision behind SAWACO’s AI Digital Platform for SWRO Optimisation. Developed in partnership with a technology provider and deployed at SAWACO’s CFRO + BWRO seawater desalination unit in Jeddah, Saudi Arabia, the solution uses Artificial Intelligence and predictive analytics to fine-tune operational setpoints—like pump pressure, brine flow, and recirculation rates—reducing electricity consumption while maintaining stable performance.

For SAWACO, this isn’t just theory. The platform was assessed using real operational data from 2024 (annualised from measured plant data between January 1 and November 20, 2024), proving how digital intelligence can deliver measurable energy savings and carbon reductions in one of the most energy-intensive industrial processes: seawater desalination.

The Challenge: Desalination Plants Often Run on Fixed, Inefficient Settings

Many seawater reverse osmosis (SWRO) plants still rely on manual monitoring, operator experience, and static setpoints. While functional, this approach limits energy efficiency, increases operational costs, and inflates the carbon footprint of water production.

The core issue? Lack of real-time adaptability. Without dynamic adjustments, plants may operate at suboptimal conditions—over-pumping, over-pressurizing, or running equipment at inefficient levels. This not only wastes electricity but also accelerates wear and tear on critical components like high-pressure pumps, energy recovery devices, and membranes.

SAWACO’s AI Digital Platform addresses this gap by replacing guesswork with data-driven, real-time optimisation.

The Solution: AI-Optimised Control Through Existing SCADA Systems

SAWACO’s solution integrates a cloud-based AI tool with the plant’s existing SCADA system. The platform continuously monitors, stores, and analyses operational data from key instruments, acting as a digital assistant for operators. By detecting patterns in live and historical data, the AI predicts system behaviour and recommends optimal setpoints for controllable parameters—such as pump pressure, brine flow, and recirculation rates.

Crucially, the solution works with the plant’s existing infrastructure. No hardware upgrades are required. Instead, the AI model—trained on SAWACO’s historical operational data—uses predictive analytics and optimisation algorithms to reduce specific energy consumption (kWh per m³ of water produced) without compromising performance.

This approach reflects a broader shift in industrial sustainability: leveraging digital intelligence to squeeze more efficiency out of existing assets, rather than relying on costly replacements.

The Impact: Quantified Savings in Energy and Emissions

The assessment, conducted as an ex-post analysis for 2024, compared electricity consumption before and after implementing the AI-recommended setpoints. The results, expressed per cubic metre of potable water produced, are striking:

• Net carbon impact: -157.6 tCO₂e/year
• Net carbon impact range: -141.6 to -175.4 tCO₂e/year (accounting for uncertainty)
• Net impact per m³ of potable water: -0.2585 kgCO₂e/m³/year
• Reduced electricity consumption: Lower operational energy demand (kWh) and associated Scope 2 GHG emissions from grid electricity.
• Operational benefits: More stable operation reduces mechanical and hydraulic stress on equipment, potentially improving reliability and extending component lifetimes (e.g., reduced membrane fouling/damage).

The assessment also includes first-order solution emissions (e.g., from AI model training), ensuring the net effect is transparently measured.

Beyond emissions, the solution delivers cost savings through reduced electricity purchases—savings that could be reinvested in further sustainability initiatives.

Why It Matters: Proving the Digital Handprint in Water Production

SAWACO’s AI Digital Platform is a prime example of enabling impact—how digital technologies can reduce emissions beyond the ICT sector by optimising real-world industrial processes. In this case, the impact comes from applying AI, connectivity, and real-time data to desalination, a critical sector for both water security and climate action.

For plant operators, the lesson is clear: smarter management of existing infrastructure can deliver measurable sustainability gains. For the digital sector, the takeaway is broader: sustainability value must be backed by credible, quantified data.

SAWACO’s next steps include scaling the solution across its operations and exploring further optimisations. As desalination plants become more connected, this approach points to a scalable pathway for industrial sustainability: use real-time intelligence to reduce waste, improve efficiency, and turn existing infrastructure into a lower-emission, higher-performance system.