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Machine learning

Machine Learning Advances Dye Removal from Wastewater

Strategic Insights

— 1 min read

A team of researchers from the Polytechnic University of Valencia has made significant breakthroughs in using machine learning to efficiently remove dyes from wastewater. The study utilized Cedarlea odorata L. as a bioadsorbent material to treat industrial-scale wastewater contaminated with methylene blue and safranin, which can have detrimental effects on aquatic ecosystems and human health. The researchers employed Aspen Adsorption software to simulate an industrial-scale operational adsorption column and applied machine learning algorithms to predict the results.

Key Takeaways:

  • The research achieved adsorption efficiencies of 96.1% for both methylene blue and safranin using the Langmuir-LDF model.
  • The Freundlich-LDF model showed efficiencies of 94.8% for methylene blue and 96% for safranin.
  • The Langmuir-Freundlich-LDF model achieved up to 96.1% for methylene blue and 94.8% for safranin.
  • The study demonstrated the feasibility of simulating the competitive adsorption of dyes in solution at an industrial scale using Cedarlea odorata L. as a bioadsorbent.
  • Machine learning algorithms were implemented in this research, obtaining R2 higher than 0.996 for validation and testing stages for the responses of the model.
  • The application of LDF kinetic models and adsorption isotherms (Langmuir, Freundlich, and Langmuir-Freundlich) resulted in high adsorption efficiencies, highlighting the potential of this approach for the remediation of dye-contaminated effluents.
  • The research concluded that machine learning can be a viable method for predicting the performance of full-scale packed columns.

Statistics:

  • Adsorption efficiencies:

+ Langmuir-LDF model: 96.1% for both methylene blue and safranin.

+ Freundlich-LDF model: 94.8% for methylene blue and 96% for safranin.

+ Langmuir-Freundlich-LDF model: up to 96.1% for methylene blue and 94.8% for safranin.

  • R2 values for validation and testing stages: higher than 0.996.

Sources:

  • Use of cedrela Odorata L. As a Biomaterial for Dye Adsorption In Wastewater: Simulation and Machine Learning Approaches for Scale-up Analysis. Processes, 2025;13(9):2907.
  • Mdpi, St Alban-Anlage 66, Ch-4052 Basel, Switzerland.
  • Modesto Perez-Sanchez, Polytechnic University of Valencia, Hydraul & Environm Engn Dept, Valencia 46022, Spain.
  • Candelaria Tejada-Tovar, Angel Villabona-Ortiz, Maria Hueto-Polo, and Oscar E. Coronado-Hernandez.
  • NewsRx LLC. New Findings in Machine Learning Described from Polytechnic University of Valencia (Use of cedrela Odorata L. As a Biomaterial for Dye Adsorption In Wastewater: Simulation and Machine Learning Approaches for Scale-up Analysis). Journal of Engineering. October 20, 2025; p 2227.

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