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ANN-based modelling and isotherm–kinetic study of dye adsorption on acid-modified manila tamarind seed biomass

S. Karishma, V. C. Deivayanai, P. Thamarai, A. Saravanan, P. R. Yaashikaa, and A. S. Vickram

Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India

 

E-mail: sara.biotech7@gmail.com

Received: 21 December 2024  Accepted: 10 July 2025

Abstract:

Plant biomass has been increasingly utilized as an adsorbent material alternate to chemically synthesized activated carbon. Manila tamarind seed has been acid-modified in the current study in order to improve the biomass’s remediation capabilities. Physical characterization studies revealed a porous surface morphology through scanning electron microscopy analysis, abundant functional groups in adsorption, and point of zero charge of 6.43, indicating favourable surface chemistry for binding of dye molecules. The ideal circumstances for the Eriochrome Black (EB) and Brilliant Orange (BO) dye removal were found through batch investigations. The optimal values are adsorbent dosage–3.5 g/L, temperature–303 K, contact time–50 min and 40 min, and pH–6 and 9 for EB and BO dye, respectively. Adsorption capacity of acid-activated Pithecellobium dulce seed biomass for EB and BO dye was observed to be 97.12 mg/g and 113.43 mg/g, respectively. The best fitting isotherm and kinetic models were Freundlich and pseudo-first order denoting the multilayer adsorption. The artificial neural network model was employed to establish a relationship between the experimental dye removal efficiencies and the predicted values for both dyes. Better correlation was demonstrated by the neural network model, with values more than 0.94 suggesting that the model is appropriate for the current study. The potential of acid-modified manila tamarind seed biomass in the dye removal process was validated by modelling studies.

Graphical abstract

Keywords: Acid modification; Manila tamarind seeds; Freundlich; Pseudo-second order; Artificial neural network

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-025-04247-6

 

Chemical Papers 79 (10) 7079–7097 (2025)

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