A MACHINE LEARNING-BASED MODELING APPROACH FOR DYE REMOVAL USING MODIFIED NATURAL ADSORBENTS

This repository contains the dataset and accompanying scripts used in the study titled "A Machine Learning-Based Modeling Approach for Dye Removal Using Modified Natural Adsorbents." The dataset used in this investigation includes maximum adsorption capacity (qe) and removal percentage (%) values obtained by removing MB dye from waste water using different adsorbent types. These adsorbents were modified by incorporating LA into almond, walnut, and apricot kernel powders. The data set under consideration encompasses pH, adsorbent dose, concentration, temperature, and time values. The output variables used for modeling are maximum adsorption capacity (qe) and removal percentage (%). NOTE: This first version contains a labeling error in the last two column headers. Please use the (updated) version v2 for accurate data. This research received no external funding. However, the datasets used in this study were obtained from the peer-reviewed publication by Süheyla Kocaman (2020), titled "Removal of methylene blue dye from aqueous solutions by adsorption on levulinic acid-modified natural shells", published in Environmental Technology (Vol. 22, pp. 885–895). DOI: https://doi.org/10.1080/15226514.2020.1736512. The original data was reused in this work for machine learning-based modeling purposes with proper attribution.