Modification of geo-filters (Basic Oxygen Furnace Slag - NaOH/Na2SiO3/FA) for the removal of Fe3+ and SO42− ions from acidic mine effluent in fixed bed column: Artificial intelligence modeling

Abstract

Modified geo-filter Basic Oxygen Furnace Slag (BOFS) was successfully synthesized and applied for the removal of Fe3+ and SO42−ions from Acidic mine effluent in a fixed bed column system (FBS). H2O2 content (1.5 %, 1 %, 0.5 %, and 0 %) as a porosity activator on the geo-filters was evaluated. A column with a 500 mm bed height, a flow rate of 6 ml/min, and an initial ion concentration (Fe3+; 542 mg/L and SO42−; 3400 mg/ L) was used to obtain the experimental breakthrough curves under varied H2O2 content. Herein, the performance of FBS was evaluated based on breakthrough time, the stoichiometric capacity of the bed, the fraction of total capacity used up to breakthrough, and saturation capacity. The results showed that the column packed with BOFS/NaOH prepared with 1.5 % H2O2 had the optimum dynamic response and performance parameters. The column had the highest length of the unused bed, revealing that the column can be reused to adsorb more SO42−, and the saturation capacity is high, which implies that more SO42− and Fe3+ can still be sorbed in this column. It was concluded that BOFS/NaOH is the best-performing column in terms of SO42− and Fe3+ sorption and takes longer to reach the breakpoint time as compared to other columns. Artificial neural networks and artificial neuro-fuzzy inference systems were applied to solve the complex relationship of breakthrough parameters. ANN-log sigmoid showed a heuristic power for the prediction of the breakthrough parameters and thus can be used in making critical decisions about the process.