ANN-assisted comprehensive screening of silica gel-alunite composite sorbent system for efficient adsorption of toxic nickel ions: Batch and continuous mode water treatment applications

Abstract

Through batch and fixed-bed column operations, nickel ions were extracted from a contaminated aqueous media by adsorption onto silica gel-immobilized alunite (Sg@Aln). A three-layer backward-propagating network with an ideal pattern of 5-10-1 and 4-10-1 was used to train and validate an artificial neural network (ANN) model for process modeling and optimization in batch and continuous systems, respectively. For the test dataset, the model outputs of the model pointed out a satisfactory alignment between the anticipated and experimental response. The Sg@Aln dosage and contact time were recorded as the most relevant parameters in Ni2+ elimination. The Sg@Aln-metal interactions were also characterized using a variety of instrumental approaches. The maximum Ni2+ adsorption was achieved by utilizing 2 g/L of the adsorbent at a solution pH of 5.0 after 10 min of contact time, equating to 89.11%. The data corresponded well with the non-linear shape of the Langmuir isotherm (R2 = 0.99), and the computed maximal adsorption capacity was 96.01 mg/g (1.64 × 10−3 mol/g) at 25 °C. Kinetic analysis reveals that the adsorption process is consistent with the pseudo-second-order model, with R2 = 0.9998. Thermodynamic findings indicated endothermicity, spontaneity, and adsorption favorability. Sg@Aln could remove 41.23 mg/g and 33.20 mg/g of Ni2+ from actual wastewater in batch and continuous processes, respectively. While the Sg@Aln column's breakthrough curve is consistent with Chu's simplistic model, the breakthrough capacity was 69.35 mg/g. Overall, the results might open new possibilities for treating metal pollution in the aquatic environment. © 2025 Elsevier Ltd