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http://dx.doi.org/10.3741/JKWRA.2021.54.S-1.1023

Prediction model for electric power consumption of seawater desalination based on machine learning by seawater quality change in future  

Shim, Kyudae (Environment Solution Team, GS Engineering & Construction)
Ko, Young-Hee (AI Big Data MBA, Seoul School of Integrated Sciences & Technologies)
Publication Information
Journal of Korea Water Resources Association / v.54, no.spc1, 2021 , pp. 1023-1035 More about this Journal
Abstract
The electricity cost of a desalination facility was also predicted and reviewed, which allowed the proposed model to be incorporated into the future design of such facilities. Input data from 2003 to 2014 of the Korea Hydrographic and Oceanographic Agency (KHOA) were used, and the structure of the model was determined using the trial and error method to analyze as well as hyperparameters such as salinity and seawater temperature. The future seawater quality was estimated by optimizing the prediction model based on machine learning. Results indicated that the seawater temperature would be similar to the existing pattern, and salinity showed a gradual decrease in the maximum value from the past measurement data. Therefore, it was reviewed that the electricity cost for seawater desalination decreased by approximately 0.80% and a process configuration was determined to be necessary. This study aimed at establishing a machine-learning-based prediction model to predict future water quality changes, reviewed the impact on the scale of seawater desalination facilities, and suggested alternatives.
Keywords
Machine learning; Seawater temperature; Salinity; Prediction model; Electric power;
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