Application of nano remediation of mine polluted in acid mine drainage water using machine learning model

Abstract

Acid mine drainage (AMD) is the term used to describe drainage from coal mines with high sulfur-bearing rocks. The oxidative weathering of metal sulfides leads to AMD. The acidic environment corrodes more harmful compounds in the soil, which is spread throughout the working area. One such significant metal is copper, which is extracted in massive quantities from ores rich in sulfide. A copper-extraction resin might be created by combining diatomaceous earth (DE) particles with polyethyleneimine (PEI), which is shown to have great selectivity and affinity for copper. In this effort, PEI-DE particles' copper absorption level was examined by using synthetic and actual acid mine drainage samples at varied pH values. The findings of the copper uptake particles have been examined through the Support Vector Machine (SVM) model. Using the n-fold 14 cross-validation approach, the quantities of parameters and C are estimated to be 0.001 and 0.01, respectively. The SVM analysis was correct, and the findings indicated that copper could bind to the material efficiently and preferentially at pH 4. Subsequent water elution studies at a pH value of 1 confirmed the pH-reliant interaction between dissolved Cu and PEI by demonstrating full release of the adsorbed Cu. In this research, the copper absorption of PEI-DE particles from synthetic and genuine AMD specimens was studied based on several pH conditions. The findings suggest that copper may attach to the material effectively and preferentially at pH 4. Studies of filtering water at pH1 later confirmed that all of the adsorbed Cu was released. This shows that the interaction between PEI and dissolved Cu depends on PH.

Keywords: Acid mine drainage (AND); Artificial intelligence; Coal mines; Copper; Diatomaceous earth (DE); Polyethyleneimine particles (PEI); Support vector machine (SVM); Water.