Research Article Published: August 18, 2025 Volume 1, Issue 6

Predictive Optimization of Natural Coagulants Using Artificial Neural Networks: A Case Study with Moringa Oleifera Seed Extract

Authors

Ayodeji Oladejo1

Affiliation: College of Medicine, University of Ibadan, Oyo State Nigeria

Temiope Atoyebi2

Affiliation: Department of Information Technology and Systems, Nile University, Abuja, Nigeria

Elias Ocheme3

Affiliation: Department of Civil and Environmental Engineering, Federal University of Technology, Akure, Nigeria

Edwin Agbeze4

Affiliation: Department of Pharmacy, University of Uyo, Akwa Ibom, Nigeria

Toheeb Kazeem5

Affiliation: Department of Food Science and Technology, Federal University of Technology, Akure, Nigeria

Prayer Atumah6

Affiliation: Department of Geology, University of Benin, Edo State, Nigeria

Sidney Igidi7

Affiliation: Independent Researcher

Abstract

This study explores the application of Artificial Neural Networks (ANNs) for optimizing Moringa oleifera seed extract as a coagulant in wastewater treatment. Moringa oleifera is recognized for its eco-friendly and sustainable properties, with proteins that effectively aggregate and remove colloidal impurities. Traditional optimization techniques, such as Response Surface Methodology (RSM), have been applied to this natural coagulant but fail to model the complex, nonlinear interactions influencing coagulation efficacy. By leveraging the predictive capabilities of ANNs, this study aims to address the limitations of traditional methods, offering a more precise approach to optimizing coagulant dosage.

Experimental results reveal that an optimal dosage of 0.02 g/100 mL achieves a turbidity reduction efficiency of 70.59%, with diminishing returns at higher concentrations. The ANN model demonstrates strong predictive performance, achieving a Root Mean Squared Error (RMSE) of 0.384 and Mean Absolute Error (MAE) of 0.312. This highlights the model's capability to capture nonlinear relationships between coagulant dosage and turbidity reduction.

The findings underscore the potential of integrating ANNs with natural coagulants to develop scalable, sustainable, and cost-effective solutions for water treatment, particularly in resource-constrained settings.

Keywords

Artificial Neural Networks (ANNs) Moringa oleifera Wastewater treatment Coagulant dosage optimization Natural coagulants Water purification Turbidity reduction Sustainable water management

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How to Cite

APA Style:

Oladejo, A., Atoyebi, T., Ocheme, E., Agbeze, E., Kazeem, T., Atumah, P., & Igidi, S. (2025). Predictive Optimization of Natural Coagulants Using Artificial Neural Networks: A Case Study with Moringa Oleifera Seed Extract. International Journal of Advanced Research in Engineering and Related Sciences, 1(6), 1-15.

IEEE Style:

A. Oladejo, T. Atoyebi, E. Ocheme, E. Agbeze, T. Kazeem, P. Atumah, and S. Igidi, "Predictive Optimization of Natural Coagulants Using Artificial Neural Networks: A Case Study with Moringa Oleifera Seed Extract," International Journal of Advanced Research in Engineering and Related Sciences, vol. 1, no. 6, pp. 1-15, 2025, doi: 10.5281/zenodo.17013812.

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DOI

DOI: https://doi.org/10.5281/zenodo.17013812