Optimizing Ni (II) adsorption on Carica papaya fiber through experimental and advanced neural network prediction

• Published in: Separation science and technology Vol. 60; no. 6; pp. 583 - 604
• Main Authors: Das, Joydeep, Mondal, Abhijit, Tarafdar, Anirban, Nag, Soma
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 13.04.2025; Taylor & Francis Ltd
• Subjects: Adsorption; Back propagation networks; bayesian regularization predictive network; Carica papaya; Clean technology; Detoxification; Endothermic reactions; feed forward back propagation neural networks; Functional groups; High temperature; Isotherms; Levenberg-Marquardt adaptive network; Neural networks; nickel; Papayas; Reprocessing; Wastewater
• ISSN: 0149-6395; 1520-5754
• DOI: 10.1080/01496395.2025.2452425

Detoxification of Ni (II) ion from the synthetic wastewater by Carica papaya fiber (CPF) has been attempted in batch method. FTIR analysis detected prominent functional groups that helped in binding Ni +2 . The most efficient sorption was observed at pH 6 and with the increase in the ratio of biomass to Ni (II) ion strength, the removal efficiency improved. 2.5 g/L CPF powder removed 90.83% Ni (II) from 10 mg/L solution at a contact time of 120 min and at 30°C. Both linear and non-linear forms of Langmuir, Freundlich, and Temkin isotherms were attempted. Langmuir isotherm in both types matched well (R 2  = 0.999 and 0.994) and the maximum adsorption capacity was 21.84 mg/g. The spontaneity of the reaction was confirmed by ΔG° values (−12.06 to −13.71 kJ/mol) and was more favorable at higher temperatures. The reaction was endothermic (ΔH° 83.03 kJ/mol) and random (ΔS° 83.07 J/mol/K). The desorption study proposed active reprocessing of CPF up to three cycles. The results were scaled up for use by industries. This study reflects sustainable eco-innovation and a cost-effective approach for locally based small-scale industries for environmental preservation. The feed-forward back propagation neural network technique was applied for efficient prediction of the Ni(II) removal efficiency.