AI-Based Prediction and Optimization of Ammonium Removal Efficiency and Capacity of Natural Zeolites Using LSBoost (XGBoost) for Sustainable Ecosystems

Preserving clean water resources and efficiently treating wastewater is critical for ensuring human survival on Earth and in extraterrestrial environments. Major pollutants, including ammonium, heavy metals, industrial dyes, and chemicals, threaten limited clean water supplies and soil. Among the renowned absorbent materials, natural zeolite minerals have demonstrated their effectiveness for pollutant removal compared to clay minerals and synthetic equivalents like biochar, activated carbon, and MOFs, owing to their relatively extensive reserves and eco-friendly nature. Recently, investigating and optimizing pollutant removal rates from water without conducting laboratory experiments is getting more crucial, considering the time-consuming, expensive, and error-prone nature of laboratory testing due to human factors and potential calibration issues among the chosen analytical techniques.

This study aims to forecast the ammonium removal efficiency (% adsorption) and capacity (mg/g) of natural and modified zeolites from aqueous solutions using the regression ensemble LSBoost (MATLAB R2024b) machine learning (ML) algorithm, which is equivalent to XGBoost open-source library. A total of 527 experiments on 15 different zeolite compositions were gathered from a combination of 14 suitable moderately recent (≥ 2005) and highly referred studies to assess the performance of zeolite minerals on ammonium removal rates from aqueous solutions. The LSBoost algorithm achieved over 0.99 R² fitting for training and overall, 0.95 R² for prediction on the quarterly partitioned testing data for both efficiency and capacity. Throughout the improvement of the ML models using different random forest ML approaches, the number of predictors was successfully reduced to 8 based on importance rates among 31 different features in the initial dataset, with a negligible accuracy loss (<0.1 R²) on both training and testing. This research provides a valuable contribution to optimizing applicable experimental parameters in water treatment processes by effectively identifying the significance of predictors within a comprehensive data set. In addition to this, our model not only provides a robust predictive tool for optimizing zeolite performance in water treatment but also represents the first open-sourced web application in the literature to estimate the water treatment performance of zeolites.