Desalination Develops An Intelligent Model To Predict The Concentration Of Iron Chloride

Desalination develops an intelligent model to predict the concentration of iron chloride needed to dissolve suspended impurities in water

The Saline Water Conversion Corporation has succeeded in achieving a qualitative achievement in the field of the desalination industry by devising a new method to improve the processes of raising the quality of desalinated water through the development of a predictive model using one of the artificial intelligence algorithms to determine the concentration of iron chloride used to dissolve suspended impurities in water within reverse osmosis systems.

Through its effective ability to control and monitor the doses of iron chloride used in pre-treatment processes with these systems, the new innovation contributes to improving the efficiency of operations and reducing costs, in addition to contributing to reducing the consumption of chemicals.

This achievement gains its qualitative importance in light of the widespread use of a number of modern uses of machine learning and artificial intelligence solutions, with the aim of providing predictive models for various salt water desalination processes, as CNN-LSTM represents the strongest framework among the other solutions that were tested with an error rate estimated at less than 5%, which makes it an unprecedented scientific addition.

This comes in the context of the Corporation’s relentless innovative endeavors to develop technical solutions and overcome the most pressing obstacles in the field of the desalination industry, by adopting innovative models to improve the work of water desalination plants and ensuring proactive treatments using advanced technologies based on predicting the levels of turbidity of the flow resulting from the coagulation process, and then improving the quality of the water fed to the membranes of the reverse osmosis units, preventing rapid sedimentation or calcification and enhancing maximum safety and reliability.