THE CHALLENGE
ENGIE, a French multinational electricity utility, faced challenges of high energy consumption at a large-scale seawater reverse osmosis (SWRO) desalination plant in the Middle East. Desalinating seawater is energy-intensive for a few key reasons. Desalination typically requires a significant amount of energy to pump seawater, pressurise it, and force it through filtration or membrane systems to remove salt.
The energy cost is a major contributor to the overall expense of desalination. Optimising the performance of multiple reverse osmosis (RO) trains also presents a daunting challenge for operators as it is virtually impossible to determine the best operating parameters and membrane replacement frequency to maximise energy savings.
THE SOLUTION
Turing’s SmartOps technology is a powerful software platform that uses machine learning to optimise plant performance and manage assets efficiently. The technology takes into account various input and output parameters, ensuring that facilities meet operating design standards and mechanical limitations, while reducing energy consumption and maintaining water quality and production targets. It’s particularly effective in optimising the flow of water across reverse osmosis trains with varying efficiencies.
By harnessing the power of machine learning AI algorithms, SmartOps can enhance and predict plant operations, leading to immediate improvements in productivity, cost savings, and reduced carbon and water footprints.
RESULTS
- Up to 5% Energy Savings
- 48% Maximum Recovery Rate
- <5% Differential Pressure Increase
- >200,000 m3 Daily System Capacity
- 99.95% Uptime
- 12M Tonnes Co2 Saved Every Year
– Pascal Renaud, Chief Operating Officer, ENGIE Middle East, South and Central Asia, Turkey and AfricaEngie is accelerating our transition towards a carbon-neutral economy, including sustainable desalination solutions. Leveraging machine learning algorithms for large SWRO to reduce energy use, while meeting KPIs, is an exciting part of this strategy.
