Comparison of tree-based model with deep learning model in predicting effluent pH and concentration by capacitive deionization

Abstract

Capacitive deionization (CDI) is an emerging technique for water treatment and electroadsorption processes (i.e., brackish water desalination). Various numerical modeling methods have been developed to predict and optimize the performance of CDI, and artificial intelligence techniques have recently been applied to overcome the limitations of numerical modelings, such as the difficulty in handling all complexities in the environment. However, such a complex neural network (i.e., deep learning (DL)) has limitations in that it is difficult to design a structure, takes a long time to train, and requires massive computer resources. Therefore, in this study, a tree-based model that is more effective than a neural network model for processing tabular data was developed to predict effluent pH and concentration in the CDI process. The tree-based ensemble models had a remarkably lower computational cost (100 times less than the DL model) with almost the same prediction accuracy (R2 = 0.998 for the steady random forest model and R2 = 0.986 for the DL model) using a binary feature concept. These findings will contribute to further examining the use of tree-based models for predicting and optimizing the CDI process to reduce computing capacity and minimize modeling complexity.