Clustered Polynomial Regression Modeling Based on Machine Learning for Optimizing Seawater Desalination Plant System

As global demand for freshwater rises, efficient desalination processes are becoming increasingly critical, particularly in regions facing water scarcity. RO(reverse osmosis) is widely utilized in seawater desalination due to its effectiveness in producing potable water. However, RO is highly energy-intensive, necessitating the optimization of control variables to reduce energy consumption while maintaining high water quality. This study optimizes the desalination process by applying a clustered second-order polynomial regression model to predict seawater conductivity, a key performance indicator, before and after pretreatment. Key control variables, such as high pressure pump duty and downstream valve opening, are identified as influential factors and incorporated into the modeling process. A dataset containing 34,224 samples from desalination operations is categorized based on valve opening and supply water temperature to develop specific models tailored to various operating conditions. The developed model effectively captures the nonlinear relationships between control variables and conductivity, improving the accuracy of system behavior predictions under different scenarios. This approach enables dynamic adaptation to changing conditions, enhancing overall system efficiency, and reducing energy consumption. The results demonstrate that polynomial regression is a robust method for optimizing desalination processes, ensuring more precise control and improved performance in energy-intensive systems like RO.