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ORIGINAL RESEARCH article

Front. Environ. Chem.
Sec. Sorption Technologies
Volume 5 - 2024 | doi: 10.3389/fenvc.2024.1451051
This article is part of the Research Topic Emerging trends in adsorption process for environmental applications View all articles

Biosorption of Methylene Blue by Bone Meal: Experimental and Modeling with Machine Learning and Full Factorial Design

Provisionally accepted
Leandro G. de Araujo Leandro G. de Araujo *Gabriel F. Martins Gabriel F. Martins Alexssandra A. Campera Alexssandra A. Campera Júlio T. Marumo Júlio T. Marumo Sabine N. Guilhen Sabine N. Guilhen
  • Independent researcher, São Paulo, Brazil

The final, formatted version of the article will be published soon.

    The objective of this study is to assess the potential of bone meal powder (BMP), an underexplored agricultural byproduct, as an affordable adsorbent for the removal of methylene blue (MB) from water. BMP was subjected to a series of analytical characterization techniques, and its adsorption capacity was evaluated through a comprehensive factorial design, which investigated the effects of biosorbent dosage, solution pH, and initial MB concentration. The study revealed that the highest adsorption level was 14.49 mg g-1, attained under the following conditions: 1 g L-1 BMP, pH 11, and 100 mg L-1 MB. The adsorption equilibrium was reached within 60 minutes, with a measured capacity (qexp) of 18 mg g-1. Theoretical adsorption isotherms indicated a capacity of 63 mg g-1, which aligned well with the Langmuir model. To predict adsorption outcomes, machine learning models were applied, with multiple linear regression performing best. Optimization of decision trees and neural networks improved accuracy but risked overfitting. FT-IR, XRD, and ICP analyses indicated ion exchange as a significant mechanism of adsorption. In desorption studies, H2SO4 was the most effective agent, achieving 68.72% desorption efficiency. BMP exhibited optimal recyclability for up to four cycles before efficiency declined.

    Keywords: Adsorption, dyes, experimental design, Low-cost biosorbents, machine learning

    Received: 18 Jun 2024; Accepted: 29 Aug 2024.

    Copyright: © 2024 de Araujo, Martins, Campera, Marumo and Guilhen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Leandro G. de Araujo, Independent researcher, São Paulo, Brazil

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.