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Artificial Intelligence in Desalination Plants: A New Approach

Strategic Insights

— 2 min read

Researchers from Karpagam Institute of Technology, India, have made significant advancements in the field of artificial intelligence by developing a machine learning model to monitor seawater temperature variations and their effects on desalination efficiency. This breakthrough allows for more efficient and resilient management of desalination plants, especially in climate-sensitive coastal locations. The study, published in the journal Desalination and Water Treatment, combines satellite-derived sea surface temperature data with operational performance parameters of desalination plants, demonstrating the potential for integrating remote sensing and machine learning in desalination management.

Key Takeaways:

  • The research team developed two Random Forest (RF)-based regression models to evaluate and forecast the effects of sea surface temperature (SST) variability on desalination efficiency and energy demand.
  • The models were trained using satellite-derived SST data from MODIS and operational performance parameters of the desalination plant at the Kalpakkam Atomic Power Station in Tamil Nadu, India.
  • The findings demonstrate the opposite relationship between energy consumption and desalination efficiency, with energy consumption changing from 3.5 to 3.9 kWh/m³ and efficiency decreasing from 96% in February to 92% in June.
  • The Research indicates high prospecting ability of the RF model, which scored R-square values with 0.99 proximity and RMSE of only 0.11%.
  • The study establishes a framework for resilient reactions that are scaled up in response to environmental variability, paving the way for water and energy security in climate-sensitive coastal locations.
  • The research has significant implications for the integration of remote sensing and machine learning in desalination management applications.
  • The study was conducted by a team of researchers from Karpagam Institute of Technology, led by A. Christopher Paul, and involved collaboration with Ghalib H. Alshammri, G.R. Hemalakshmi, and Haya Mesfer Alshahrani.

Statistics:

  • Energy consumption changed from 3.5 to 3.9 kWh/m³ in the opposite directions.
  • Efficiency decreased from 96% in February at 27.1°C to 92% in June at 29.6°C.
  • The RF model scored R-square values with 0.99 proximity.
  • The RMSE of the RF model was only 0.11%.

Sources:

  • Alshammri, G. H., Paul, A. C., Hemalakshmi, G. R., & Alshahrani, H. M. (2025). Remote sensing and machine learning-based monitoring of seawater temperature variations and their effects on desalination efficiency. Desalination and Water Treatment, 324, 101459. doi: 10.1016/j.dwt.2025.101459
  • Karpagam Institute of Technology. (n.d.). Remote sensing and machine learning-based monitoring of seawater temperature variations and their effects on desalination efficiency. NewsRx LLC.

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