| dc.description.abstract | This study investigated the adsorptive removal of three heavy metal ions (Cu²⁺, Ni²⁺, Fe²⁺) from
wastewater using fayalite slag (FS), a low-cost industrial by-product. The media was
characterized using XRD, XRF, SEM, batch adsorption studies were conducted, and the
adsorption process was further optimized and predicted adsorption efficiency by developing
Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS).
Comprehensive characterization revealed that FS was suitable as an adsorbent, with a particle
size distribution (d10 = 0.3mm, Cu = 5.36), high porosity (50%), and significant iron oxide
content (Fe₂O₃ = 45.44%). Batch adsorption experiments demonstrated optimal removal
efficiencies at a dosage of 2.0 g/100mL, near-neutral pH (6-8), and a contact time of 40 minutes,
achieving approximately 3.5 mg/ g adsorption capacity (35% removal efficiency) for each
metal. The adsorption kinetics aligned with the pseudo-second-order model (R² ≥ 0.994),
indicating chemisorption as the rate-limiting step. Langmuir and Freundlich isotherm models
effectively described the adsorption behavior. Thermodynamic studies confirmed that the
process was endothermic (ΔH° > 0) and spontaneous (ΔG° = -9.023 to -10.294 kJ/mol).
Reusability studies showed a gradual decline in recovery efficiency, from approximately 30%
in the first cycle to less than 20% by the third cycle. The ANFIS model, with six fuzzy rules,
exhibited superior performance (R² = 0.823, RMSE = 11.87), demonstrating a high correlation
between predicted and experimental data. Thus, FS proved to be an effective, sustainable
adsorbent, warranting further research into regeneration optimization, continuous flow
applications, and real wastewater treatment, alongside cost-benefit and pilot-scale assessments
for industrial implementation. | en_US |
