The application of unsupervised machine learning to optimize water treatment membrane selection

Abstract

Artifi cial intelligence technologies have been extensively used to decipher water quality and characterization. Fewer studies have employed these techniques in the purpose of optimizing a water treatment process. Here, we apply unsupervised machine learning techniques for the optimization of the choice of membranes, following the different constraints and conditions encountered. The adopted data analysis techniques are the Principal Component Analysis (PCA) and the Hierarchical Cluster Analysis (HCA). Both methods showed their capacity to reveal resemblance and discrepancies between different membrane types and based on several properties. PCA is more appreciated than HCA as it removes any intercorrelation between factors and it helps in a better understanding of different trends of the dataset by establishing a Scores-Factors relation.