Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources

Ahmet Nur

doi:10.17798/bitlisfen.1921603

Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources

Abstract

Organic dyes used in industrial processes, particularly in textile, leather, paper, and cosmetic applications, are among the most persistent and environmentally harmful pollutants due to their stable aromatic structures and resistance to natural degradation. Methylene blue (MB) and methyl orange (MO) are representative model dyes commonly used to investigate photocatalytic degradation mechanisms in aqueous systems. In this study, the photocatalytic degradation of MB and MO was systematically investigated under two artificial light sources (LED and mercury-vapor lamp) in the presence of NiO nanoparticles. A physics-consistent, data-driven modeling framework was developed to predict degradation kinetics using the logarithm of absorbance ratios (ln(At/A0). Multiple machine learning models, including Decision Tree, LSBoost, Support Vector Regression, Gaussian Process Regression, Ridge Regression, and a shallow neural network, were evaluated using a Leave-One-Group-Out (LOGO) cross-validation strategy to assess generalization across heterogeneous dye–light regimes. Model outputs were subsequently transformed back into physically measurable domains (At and degradation efficiency, D%) to ensure interpretability. Among the evaluated approaches, the Decision Tree model demonstrated superior predictive performance, stable residual behavior, and kinetic consistency. The results highlight the importance of integrating physical insight with data-driven modeling to reliably describe complex photocatalytic degradation processes.

Keywords

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Details

Primary Language

English

Subjects

Lighting

Journal Section

Research Article

Authors

Ahmet Nur ^*
0009-0006-5671-6923
Türkiye

Publication Date

September 30, 2026

Submission Date

April 2, 2026

Acceptance Date

June 30, 2026

Published in Issue

Year 2026 Volume: 15 Number: 3

DOI

https://doi.org/10.17798/bitlisfen.1921603

IZ

https://izlik.org/JA49LH35CJ

Cite

RIS / Bibtex

APA

Nur, A. (2026). Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 15(3), 1484-1496. https://doi.org/10.17798/bitlisfen.1921603

AMA

1.Nur A. Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15(3):1484-1496. doi:10.17798/bitlisfen.1921603

Chicago

Nur, Ahmet. 2026. “Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 (3): 1484-96. https://doi.org/10.17798/bitlisfen.1921603.

EndNote

Nur A (September 1, 2026) Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15 3 1484–1496.

IEEE

[1]A. Nur, “Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 3, pp. 1484–1496, Sept. 2026, doi: 10.17798/bitlisfen.1921603.

ISNAD

Nur, Ahmet. “Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 15/3 (September 1, 2026): 1484-1496. https://doi.org/10.17798/bitlisfen.1921603.

JAMA

1.Nur A. Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026;15:1484–1496.

MLA

Nur, Ahmet. “Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources”. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 15, no. 3, Sept. 2026, pp. 1484-96, doi:10.17798/bitlisfen.1921603.

Vancouver

1.Ahmet Nur. Machine Learning-Based Prediction of Photocatalytic Degradation of Organic Dyes Under Different Artificial Light Sources. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2026 Sep. 1;15(3):1484-96. doi:10.17798/bitlisfen.1921603