Rocznik Ochrona Środowiska 2025, vol. 27, pp. 582-598
Malarvizhi Thangaraj1, Saveeth Ramanathan2 
, Angeline Kiruba Dunston1
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1. Government College of Technology, India 2. Coimbatore Institute of Technology, India |
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https://doi.org/10.54740/ros.2025.047 |
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This study presents a data-driven hybrid framework that combines experimental adsorption trials with machine learning and evolutionary optimization to enhance the removal of Turquoise Blue Dye from aqueous media. Three bio-adsorbents, Azadirachta indica, Phyllanthus emblica, and Saraca asoca, and their equi-mass mixture were examined for synergistic adsorption performance. Batch experiments were conducted by varying the pH (3-11), contact time (20-120 minutes), dye concentration (20-120 ppm), and adsorbent dosage (0.02-0.1 g/L). The composite mixture achieved the highest removal efficiency at 77% exceeding Phyllanthus emblica at 74%, Saraca asoca at 70%, and Azadirachta indica at 63.8%. FTIR analysis confirmed chemical interactions via hydroxyl, carboxyl and carbonyl groups. Supervised models, including Random Forest with a coefficient of determination of 0.92 and mean squared error of 0.0021, were optimized using Differential Evolution. This integrative strategy supports scalable and intelligent solutions for industrial dye effluent remediation.
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bio-adsorbents, turquoise blue dye removal, machine learning regression models
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AMA Style
Thangaraj M, Ramanathan S, Dunston A. Biotechnological Evaluation and Predictive Modeling of Bio-Based Adsorbents for Turquoise Blue Dye Detoxification: Integrating Experimental Validation and Machine Learning. Rocznik Ochrona Środowiska. 2025; 27. https://doi.org/10.54740/ros.2025.047
ACM Style
Thangaraj, M., Ramanathan, S., Dunston, A. 2025. Biotechnological Evaluation and Predictive Modeling of Bio-Based Adsorbents for Turquoise Blue Dye Detoxification: Integrating Experimental Validation and Machine Learning. Rocznik Ochrona Środowiska. 27. DOI:https://doi.org/10.54740/ros.2025.047
ACS Style
Thangaraj, M.; Ramanathan, S.; Dunston, A. Biotechnological Evaluation and Predictive Modeling of Bio-Based Adsorbents for Turquoise Blue Dye Detoxification: Integrating Experimental Validation and Machine Learning Rocznik Ochrona Środowiska 2025, 27, 582-598. https://doi.org/10.54740/ros.2025.047
APA Style
Thangaraj, M., Ramanathan, S., Dunston, A. (2025). Biotechnological Evaluation and Predictive Modeling of Bio-Based Adsorbents for Turquoise Blue Dye Detoxification: Integrating Experimental Validation and Machine Learning. Rocznik Ochrona Środowiska, 27, 582-598. https://doi.org/10.54740/ros.2025.047
ABNT Style
THANGARAJ, M.; RAMANATHAN, S.; DUNSTON, A. Biotechnological Evaluation and Predictive Modeling of Bio-Based Adsorbents for Turquoise Blue Dye Detoxification: Integrating Experimental Validation and Machine Learning. Rocznik Ochrona Środowiska, v. 27, p. 582-598, 2025. https://doi.org/10.54740/ros.2025.047
Chicago Style
Thangaraj, Malarvizhi, Ramanathan, Saveeth, Dunston, Angeline Kiruba. 2025. "Biotechnological Evaluation and Predictive Modeling of Bio-Based Adsorbents for Turquoise Blue Dye Detoxification: Integrating Experimental Validation and Machine Learning". Rocznik Ochrona Środowiska 27, 582-598. https://doi.org/10.54740/ros.2025.047
Harvard Style
Thangaraj, M., Ramanathan, S., Dunston, A. (2025) "Biotechnological Evaluation and Predictive Modeling of Bio-Based Adsorbents for Turquoise Blue Dye Detoxification: Integrating Experimental Validation and Machine Learning", Rocznik Ochrona Środowiska, 27, pp. 582-598. doi:https://doi.org/10.54740/ros.2025.047
IEEE Style
M. Thangaraj, S. Ramanathan, A. Dunston, "Biotechnological Evaluation and Predictive Modeling of Bio-Based Adsorbents for Turquoise Blue Dye Detoxification: Integrating Experimental Validation and Machine Learning", RoczOchrSrod, vol 27, pp. 582-598. https://doi.org/10.54740/ros.2025.047
