Bio-inspired Adsorption Sheets from Waste Material for Anionic Methyl Orange Dye Removal

Nano zero-valent iron (nZVI), bimetallic nano zero-valent iron-copper (Fe 0 –Cu), and Raw algae (sargassum dentifolium) activated carbon-supported bimetallic nano zero-valent iron-copper (AC-Fe 0 –Cu) are synthesized and characterized using FT-IR, XRD, and SEM. The maximum removal capacity is demonstrated by bimetallic activated carbon AC-Fe 0 –Cu, which is estimated at 946.5 mg/g capacity at the condition pH = 7, 30 min contact time under shaking at 120 rpm at ambient temperature, 200 ppm of M.O, and 1 g/l dose of raw algae-Fe 0 –Cu adsorbent. The elimination capability of the H 3 PO 4 chemical AC-Fe 0 –Cu adsorbent is 991.96 mg/g under the conditions of pH = 3, 120 min contact time under shaking at 120 rpm at room temperature, 200 ppm of M.O, and 2 g/l doses of H 3 PO 4 chemical AC-Fe 0 –Cu adsorbent. The Bagasse activated carbon adsorbent sheet achieves a removal capacity of 71.6 mg/g MO dye solution. Kinetic and isothermal models are used to fit the results of time and concentration experiments. The intra-particle model yields the best fit for bimetallic Fe 0 –Cu, AC-Fe 0 –Cu, H 3 PO 4 chemical AC-Fe 0 –Cu and bagasse activated carbon(CH), with corrected R-Squared values of 0.9656, 0.9926, 0.964, and 0.951respectively. The isothermal results emphasize the significance of physisorption and chemisorption in concentration outcomes. Response surface methodology (RSM) and artificial neural networks (ANN) are employed to optimize the removal efficiency. RSM models the efficiency and facilitates numerical optimization, while the ANN model is optimized using the moth search algorithm (MSA) for optimal results. Highlights The Fe 0 –Cu composite, when combined with activated carbon from Bagasse Pulp (CH), exhibited the most effective decolorization effectiveness for anionic colours present in wastewater. The utilization of composites presents a promising opportunity for efficient dye removal due to its cost-effectiveness and environmentally sustainable nature. The utilization of response surface approach and artificial neural network modelling improves the efficacy of removal processes and treatment techniques.