Chitosan-Based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models

The expanding impact of heavy metals (HMs) on environmental and public health necessitates the development of advanced predictive models that enhance the precision and efficiency of monitoring and remediation strategies. This study aimed to evaluate newly developed machine learning (ML) models for predicting the removal of HMs such as cadmium (Cd2+), copper (Cu2+), nickel (Ni2+), lead (Pb2+), and zinc (Zn2+) using chitosan-based flocculants (CBFs) from wastewater. A gradient boosting regressor (GBR), Hist gradient boosting regressor (HGBR), random forest regressor (RFR), and extreme gradient boosting regressor (XGBR) were developed, with a cluster label generated by K-means clustering included as an additional feature to enhance model learning. The ML models were built using experimental data sets of HM ion removal across 484 sets of flocculation experiments involving various ions of HMs such as Cu2+, Pb2+, Cd2+, Zn2+, and Ni2+. Results indicated that the HGBR model revealed higher performance in combined HM removal scenarios, achieving a determination coefficient (R2 = 0.94/0.97 for the testing/training phases. For individual metals, all models achieved excellent accuracies, especially for nickel (Ni2+), with the GBR model obtaining the lowest error rate in the testing. The results signified a robust capability of the HGBR model for generalization and its capacity as a trustworthy tool in the framework of environmental monitoring. Future research directions required the exploration of the synthesis of these models into real-time predictive monitoring systems and an exploration of the application of integrated ML approaches to boost the predictive accuracy and reliability across wider environmental conditions.