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<p>This study examines the removal of copper ion from wastewater using <i>Saccharomyces cerevisiae</i> through biosorption and demonstrates a maximum removal efficiency of 97.3 ± 0.70% under optimal conditions of pH 5.5, biomass concentration of 4&#xa0;g/L, contact time of 60&#xa0;min, and agitation speed of 250&#xa0;rpm. Analysis of variance on signal-to-noise ratios indicated that pH exerted the greatest influence (45.2%), followed by biomass concentration (25.4%), agitation speed (16.7%), and contact time (12.7%). The removal of copper ion from electroplating industry wastewater using <i>Saccharomyces cerevisiae</i> was validated by measuring parameters such as chemical oxygen demand, biological oxygen demand, total dissolved solids, sulphate, nitrate, and chloride levels and all of which met the discharge limits prescribed by the Central Pollution Control Board of India. The biosorption of copper ion was primarily attributed to complexation with functional groups present on the cell wall of <i>Saccharomyces cerevisiae</i>, demonstrating its potential as a cost-effective and environmentally friendly biosorbent for heavy metal removal. The Artificial Neural Network model achieved the highest prediction accuracy (96.2 ± 0.87% (standard deviation), absolute error 1.1%), outperforming Gradient Boosting, Random Forest, Support Vector, and Linear Regression models. Its superior performance underscores the Artificial Neural Network’s potential for optimizing sustainable wastewater treatment and management. Effective copper ion recovery from <i>Saccharomyces cerevisiae</i> biomass was achieved through the desorption process using dilute hydrochloric acid, with recovery efficiency ranging from 85.7 to 88.4%. Future research should expand datasets, explore hybrid models, and integrate real-time process data to enhance performance for other heavy metal contaminants.</p> Graphical Abstract <p></p>

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Eco-smart copper removal with yeast biosorption and machine learning optimization

  • 期刊论文

  • 发表日期：

    2025年11月27日

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  • D. Sivakumar,
  • D. Shankar,
  • T. R. Neelakantan

摘要

This study examines the removal of copper ion from wastewater using Saccharomyces cerevisiae through biosorption and demonstrates a maximum removal efficiency of 97.3 ± 0.70% under optimal conditions of pH 5.5, biomass concentration of 4 g/L, contact time of 60 min, and agitation speed of 250 rpm. Analysis of variance on signal-to-noise ratios indicated that pH exerted the greatest influence (45.2%), followed by biomass concentration (25.4%), agitation speed (16.7%), and contact time (12.7%). The removal of copper ion from electroplating industry wastewater using Saccharomyces cerevisiae was validated by measuring parameters such as chemical oxygen demand, biological oxygen demand, total dissolved solids, sulphate, nitrate, and chloride levels and all of which met the discharge limits prescribed by the Central Pollution Control Board of India. The biosorption of copper ion was primarily attributed to complexation with functional groups present on the cell wall of Saccharomyces cerevisiae, demonstrating its potential as a cost-effective and environmentally friendly biosorbent for heavy metal removal. The Artificial Neural Network model achieved the highest prediction accuracy (96.2 ± 0.87% (standard deviation), absolute error 1.1%), outperforming Gradient Boosting, Random Forest, Support Vector, and Linear Regression models. Its superior performance underscores the Artificial Neural Network’s potential for optimizing sustainable wastewater treatment and management. Effective copper ion recovery from Saccharomyces cerevisiae biomass was achieved through the desorption process using dilute hydrochloric acid, with recovery efficiency ranging from 85.7 to 88.4%. Future research should expand datasets, explore hybrid models, and integrate real-time process data to enhance performance for other heavy metal contaminants.

Graphical Abstract