Biosorption of methylene blue by bone meal: experimental and modeling with machine learning and full factorial design

Abstract

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Sorption technologies have been proposed for the treatment of water containing methylene blue (MB), a toxic and persistent pollutant. Despite its environmental risks, the role of process variables in MB removal has not been fully explored through experimental design. 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 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 min, 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