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

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.