The Role of Artificial Intelligence in Enhancing Heavy Metal Removal Efficiency: A Bibliometric Perspective
DOI:
https://doi.org/10.30595/rice.v4i2.272Keywords:
bibliometric, artificial intelligence, heavy metal, removalAbstract
The problem of heavy metal pollution in wastewater has prompted the demand for more effective and sustainable treatment systems. In the recent decade, the integration of artificial intelligence (AI) in heavy metal adsorption processes has shown tremendous potential in enhancing efficiency and optimizing operational parameters. This study intends to identify global research trends on the application of AI in optimizing heavy metal adsorption processes by a bibliometric method for the period 2010 to 2024. Data were acquired from Google Scholar and filtered to include indexed papers, then analyzed using VOSviewer and Microsoft Excel software to evaluate annual publishing trends, as well as visualization of keyword co-existence. The findings of the investigation showed an impressive move in publications after 2019. The leading terms detected included “machine learning,” “neural networks,” and “optimization.” Despite demonstrating encouraging trends, research in this subject still confronts hurdles such as inadequate large-scale experimental data, minimal integration of AI with Internet of Things (IoT) systems, and lack of industrial-scale applications. This study shows the need of building hybrid AI-IoT systems, using big data analytics, and adaptive predictive models to increase the effectiveness of heavy metal adsorption systems in the future. These findings are likely to be a key reference for researchers and practitioners in creating smart and sustainable waste processing systems.
References
O. B. Olafisoye, T. Adefioye, and O. A. Osibote, “Heavy Metals Contamination of Water, Soil, and Plants around an Electronic Waste Dumpsite Oladunni,” Polish J. Environ. Stud., vol. 22, no. 5, pp. 1431–1439, 2013.
P. B. Angon et al., “Sources, effects and present perspectives of heavy metals contamination: Soil, plants and human food chain,” Heliyon, vol. 10, no. 7, p. e28357, 2024, doi: 10.1016/j.heliyon.2024.e28357.
H. Kanwal et al., “Transformation of heavy metals from contaminated water to soil, fodder and animals,” Sci. Rep., vol. 14, no. 1, pp. 1–12, 2024, doi: 10.1038/s41598-024-62038-7.
W. Xu, J. Yuan, and G. and Zeng, “Introduction of heavy metals contamination in the water and soil: a review on source, toxicity and remediation methods,” Green Chem. Lett. Rev., vol. 17, no. 1, p. 2404235, Dec. 2024, doi: 10.1080/17518253.2024.2404235.
B. Wang, J. Lan, C. Bo, B. Gong, and J. Ou, “Adsorption of heavy metal onto biomass-derived activated carbon: review,” RSC Adv., vol. 13, no. 7, pp. 4275–4302, 2023, doi: 10.1039/d2ra07911a.
H. Sukmana, N. Bellahsen, F. Pantoja, and C. Hodur, “Adsorption and coagulation in wastewater treatment - Review,” Prog. Agric. Eng. Sci., vol. 17, no. 1, pp. 49–68, 2021, doi: 10.1556/446.2021.00029.
Z. Raji, A. Karim, A. Karam, and S. Khalloufi, “Adsorption of Heavy Metals: Mechanisms, Kinetics, and Applications of Various Adsorbents in Wastewater Remediation—A Review,” Waste, vol. 1, no. 3, pp. 775–805, 2023, doi: 10.3390/waste1030046.
K. F. S. Richard, D. C. S. Azevedo, and M. Bastos-Neto, “Investigation and Improvement of Machine Learning Models Applied to the Optimization of Gas Adsorption Processes,” Ind. Eng. Chem. Res., vol. 62, no. 18, pp. 7093–7102, May 2023, doi: 10.1021/acs.iecr.2c04374.
C. Liu et al., “Preparation of Acid- and Alkali-Modified Biochar for Removal of Methylene Blue Pigment,” ACS Omega, vol. 5, no. 48, pp. 30906–30922, Dec. 2020, doi: 10.1021/acsomega.0c03688.
Z. M. Yaseen, “An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions,” Chemosphere, vol. 277, p. 130126, 2021, doi: https://doi.org/10.1016/j.chemosphere.2021.130126.
X. Zhu, X. Wang, and Y. S. Ok, “The application of machine learning methods for prediction of metal sorption onto biochars,” J. Hazard. Mater., vol. 378, p. 120727, 2019, doi: https://doi.org/10.1016/j.jhazmat.2019.06.004.
H. Yang, K. Huang, K. Zhang, Q. Weng, H. Zhang, and F. Wang, “Predicting Heavy Metal Adsorption on Soil with Machine Learning and Mapping Global Distribution of Soil Adsorption Capacities,” Environ. Sci. Technol., vol. 55, no. 20, pp. 14316–14328, Oct. 2021, doi: 10.1021/acs.est.1c02479.
N. Hafsa, S. Rushd, M. Al-Yaari, and M. Rahman, “A generalized method for modeling the adsorption of heavy metals with machine learning algorithms,” Water (Switzerland), vol. 12, no. 12, pp. 1–22, 2020, doi: 10.3390/w12123490.
S. Yarragunta, M. A. Nabi, P. J., and S. R., “Prediction of Air Pollutants Using Supervised Machine Learning,” in 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021, pp. 1633–1640. doi: 10.1109/ICICCS51141.2021.9432078.
I. Gulshin and O. Kuzina, “Optimization of Wastewater Treatment Through Machine Learning-Enhanced Supervisory Control and Data Acquisition: A Case Study of Granular Sludge Process Stability and Predictive Control,” Automation, vol. 6, no. 1. 2025. doi: 10.3390/automation6010002.
A. K.G., S. R., and M. Changmai, “Optimizing wastewater treatment plant operational efficiency through integrating machine learning predictive models and advanced control strategies,” Process Saf. Environ. Prot., vol. 188, pp. 995–1008, 2024, doi: https://doi.org/10.1016/j.psep.2024.05.148.
O. Keremer, F. C. Malay, B. Deveci, P. Kirci, and P. Unal, “Optimisation of a Chemical Process by Using Machine Learning Algorithms with Surrogate Modeling BT - Mobile Web and Intelligent Information Systems,” 2023, pp. 187–201.
A. Shokry, S. Medina-González, P. Baraldi, E. Zio, E. Moulines, and A. Espuña, “A machine learning-based methodology for multi-parametric solution of chemical processes operation optimization under uncertainty,” Chem. Eng. J., vol. 425, p. 131632, 2021, doi: https://doi.org/10.1016/j.cej.2021.131632.
A. Kiraz, O. Canpolat, E. F. Erkan, and Ç. Özer, “Artificial neural networks modeling for the prediction of Pb(II) adsorption,” Int. J. Environ. Sci. Technol., vol. 16, no. 9, pp. 5079–5086, 2019, doi: 10.1007/s13762-018-1798-4.
I. Maamoun, M. A. Rushdi, O. Falyouna, R. Eljamal, and O. Eljamal, “Insights into machine-learning modeling for Cr(VI) removal from contaminated water using nano-nickel hydroxide,” Sep. Purif. Technol., vol. 308, p. 122863, 2023, doi: https://doi.org/10.1016/j.seppur.2022.122863.
Z. Wang, Q. Wang, F. Yang, C. Wang, M. Yang, and J. Yu, “How machine learning boosts the understanding of organic pollutant adsorption on carbonaceous materials: A comprehensive review with statistical insights,” Sep. Purif. Technol., vol. 350, p. 127790, 2024, doi: https://doi.org/10.1016/j.seppur.2024.127790.
R. Ahmad Aftab et al., “Removal of congo red from water by adsorption onto activated carbon derived from waste black cardamom peels and machine learning modeling,” Alexandria Eng. J., vol. 71, pp. 355–369, 2023, doi: 10.1016/j.aej.2023.03.055.
Z. Haider Jaffari et al., “Machine-learning-based prediction and optimization of emerging contaminants’ adsorption capacity on biochar materials,” Chem. Eng. J., vol. 466, p. 143073, 2023, doi: https://doi.org/10.1016/j.cej.2023.143073.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Moh Azhar Afandy, Fikrah Dian Indrawati Sawali
This work is licensed under a Creative Commons Attribution 4.0 International License.
