Harnessing Avicennia Marina Leaves for Dye Contaminant Removal: An RSM and ANN-Based Study

Authors

  • Nageswara Rao Lakkimsetty

    Department of Chemical and Petroleum Engineering, American University of Ras Al Khaimah (AURAK), Ras Al Khaimah 72603, United Arab Emirates
  • Rohini Kumar Palavalasa

    Department of Chemical Engineering, RVR & JC College of Engineering, Chowdavaram, Guntur, Andhra Pradesh 522019, India
  • Noor Mohammed Said Qahoor

    Department of Chemical Engineering, College of Engineering and Technology, University of Technology and Applied Sciences-Salalah, Salalah 211, Sultanate of Oman
  • Babu Rao Gaddala

    Department of Chemical Engineering, School of Studies in Engineering and Technology, Guru Ghasidas Vishwavidyalaya (A Central University), Koni, Bilaspur, Chhattisgarh 495009, India
  • China Subbarao Chikkam

    Department of Petroleum Technology, Aditya University, Surampalem, East Godavari, Andhra Pradesh 533437, India
  • G. Kavitha

    Department of Chemical Engineering, RVR & JC College of Engineering, Chowdavaram, Guntur, Andhra Pradesh 522019, India
  • Dadapeer Doddamani

    Mechanical and Industrial Engineering Section, College of Engineering and Technology, University of Technology and Applied Sciences, Muscat 133, Sultanate of Oman
  • Rakesh Namdeti

    Department of Chemical Engineering, College of Engineering and Technology, University of Technology and Applied Sciences-Salalah, Salalah 211, Sultanate of Oman

DOI:

https://doi.org/10.30564/jees.v7i5.8590
Received: 28 January 2025 | Revised: 20 February 2025 | Accepted: 26 February 2025 | Published Online: 9 May 2025

Abstract

This study examines the efficacy of Avicennia marina (AM) leaves as an environmentally sustainable biosorbent for the extraction of methylene blue (MB) dye from wastewater. A hybrid approach of Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) was implemented to assess, optimize, and forecast biosorption effectiveness across different operating parameters. The experimental design employed a Central Composite Design (CCD) methodology, focusing on critical parameters including pH, initial dye concentration, temperature, and biosorbent dosage. The ideal biosorption parameters were identified as a temperature of 44.3°C, pH 7.1, a biosorbent dosage of 0.3 grams, and an initial dye concentration of 48.4 mg/L, resulting in a maximum removal efficiency of 84.26%. The ANN model exhibited significant prediction accuracy, so confirming its appropriateness for predicting and enhancing intricate biosorption processes. The findings underscore that AM leaves constitute a cost-efficient, plentiful, and ecologically sustainable resource for wastewater treatment purposes. Furthermore, the amalgamation of RSM and ANN shown significant efficacy in process optimization and forecasting. These findings provide significant insights into the advancement of eco-friendly solutions for the treatment of dye-contaminated water. Subsequent study must prioritize the amplification of the procedure for industrial applications, the execution of ongoing system assessments, and the evaluation of the enduring environmental and economic ramifications of utilizing AM leaves as a biosorbent.

Keywords:

Synthetic Dye Removal; Experimental Design Using Central Composite Method; Avicennia Marina as a Biosorbent; ANN-Based Performance

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How to Cite

Nageswara Rao Lakkimsetty, Rohini Kumar Palavalasa, Noor Mohammed Said Qahoor, Babu Rao Gaddala, China Subbarao Chikkam, G. Kavitha, Dadapeer Doddamani, & Namdeti, R. (2025). Harnessing Avicennia Marina Leaves for Dye Contaminant Removal: An RSM and ANN-Based Study. Journal of Environmental & Earth Sciences, 7(5), 372–380. https://doi.org/10.30564/jees.v7i5.8590

Issue

Vol. 7 , Iss. 5 (May 2025)

Article Type

Article

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Copyright © 2025 Nageswara Rao Lakkimsetty, Rohini Kumar Palavalasa, Noor Mohammed Said Qahoor, Babu Rao Gaddala, China Subbarao Chikkam, G. Kavitha, Dadapeer Doddamani, Rakesh Namdeti

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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