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<p>In adsorption technology, understanding the factors that influence adsorption efficiency and optimizing the process parameters are essential for achieving optimal system performance. In this line, the present work focused on optimization of inevitable process parameters, such as initial metal concentration, pH, contact time, and adsorbent dosage to enhance the sorption of hexavalent chromium [Cr (VI)] using <i>Purpureocillium lilacinum</i> (<i>P. lilacinum</i>) fungal biomass. Response surface methodology (RSM) and artificial neural networks (ANN) were employed for optimizing the process parameters. Besides, adsorption equilibrium, kinetics, thermodynamics, and adsorbent regeneration studies were also investigated. The maximum sorption efficiency, 97.71%, for Cr (VI) removal was achieved at an initial metal concentration of 20 mg/L, adsorbent dosage of 0.5 g/L, pH value of 4, and contact time of 40 min. The results of the ANN model demonstrated that ANN has superior predictive power than the RSM model, with R<sup>2</sup> of 0.9936 for ANN and 0.9788 for RSM. The adsorption process was well-fit to Langmuir and three-parameter isotherms with <i>R</i><sup>2</sup> &gt; 0.995. The kinetic investigation indicated that the biosorption was a chemisorption process and the data were observed to be fitted best with Elovich’s kinetic model. The results of thermodynamic analysis revealed that biosorption was favorable and exothermic. Further, the treatment with NaOH showed a superior performance in the regeneration of adsorbent material. Different characterizations on the adsorbent revealed the presence of active sites, cavities, and various functional groups were responsible for biosorption.</p>

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Enhanced Cr (VI) biosorption using dead fungal biomass of Purpureocillium lilacinum: RSM and ANN based optimization, kinetics, thermodynamics, and adsorbent regeneration

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Gizachew Assefa Kerga,
  • Nurelegne Tefera Shibeshi,
  • Venkatesa Prabhu Sundramurthy,
  • Alazar Yeshitla

摘要

In adsorption technology, understanding the factors that influence adsorption efficiency and optimizing the process parameters are essential for achieving optimal system performance. In this line, the present work focused on optimization of inevitable process parameters, such as initial metal concentration, pH, contact time, and adsorbent dosage to enhance the sorption of hexavalent chromium [Cr (VI)] using Purpureocillium lilacinum (P. lilacinum) fungal biomass. Response surface methodology (RSM) and artificial neural networks (ANN) were employed for optimizing the process parameters. Besides, adsorption equilibrium, kinetics, thermodynamics, and adsorbent regeneration studies were also investigated. The maximum sorption efficiency, 97.71%, for Cr (VI) removal was achieved at an initial metal concentration of 20 mg/L, adsorbent dosage of 0.5 g/L, pH value of 4, and contact time of 40 min. The results of the ANN model demonstrated that ANN has superior predictive power than the RSM model, with R2 of 0.9936 for ANN and 0.9788 for RSM. The adsorption process was well-fit to Langmuir and three-parameter isotherms with R2 > 0.995. The kinetic investigation indicated that the biosorption was a chemisorption process and the data were observed to be fitted best with Elovich’s kinetic model. The results of thermodynamic analysis revealed that biosorption was favorable and exothermic. Further, the treatment with NaOH showed a superior performance in the regeneration of adsorbent material. Different characterizations on the adsorbent revealed the presence of active sites, cavities, and various functional groups were responsible for biosorption.