Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-22T17:40:11.888Z Has data issue: false hasContentIssue false
  • English
  • Français

A deep learning neural network approach for predicting the factors influencing heavy-metal adsorption by clay minerals

Published online by Cambridge University Press:  24 August 2022

Rui Liu ,
Lei Zuo ,
Peng Zhang ,
Jiajia Zhao  and
Dongping Tao
Rui Liu
Affiliation:
School of Resources and Environmental Engineering, Shandong University of Technology, Zibo 255000, China
Lei Zuo
Affiliation:
School of Resources and Environmental Engineering, Shandong University of Technology, Zibo 255000, China
Peng Zhang
Affiliation:
School of Resources and Environmental Engineering, Shandong University of Technology, Zibo 255000, China
Jiajia Zhao
Affiliation:
School of Resources and Environmental Engineering, Shandong University of Technology, Zibo 255000, China
Dongping Tao*
Affiliation:
School of Resources and Environmental Engineering, Shandong University of Technology, Zibo 255000, China
*
*E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The treatment of water containing heavy metals has attracted increasing attention because the ingestion of such water poses risks to human health. Due to their relatively large specific surface areas and surface charges, clay minerals play a significant role in the adsorption of heavy metals in water. However, the major factors that influence the adsorption rates of clay minerals are not well understood, and thus methods to predict the sorption of heavy metals by clay minerals are lacking. A method that can identify the most appropriate clay minerals for removal of a given heavy metal, based on the predicted sorption of the clay minerals, is required. This paper presents a widely applicable deep learning neural network approach that yielded excellent predictions of the influence of the sorption ratio on the adsorption of heavy metals by clay minerals. The neural network model was based on datasets of heavy-metal parameters that are available generally. It yielded highly accurate predictions of the adsorption rate based on training data from the dataset and was able to account for a wide range of input parameters. A Pearson sensitivity analysis was used to determine the contributions of individual input parameters to the adsorption rates predicted by the neural network. This newly developed method can predict the major factors influencing heavy-metal adsorption rates. The model described here could be applied in a wide range of scenarios.

Keywords

adsorptionclay mineralsdeep learningheavy metals
Type
Article
Information
Clay Minerals , Volume 57 , Issue 1 , March 2022 , pp. 70 - 76
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Mineralogical Society of Great Britain and Ireland

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Editor: Chun-Hui Zhou

References

Adebowale, K.O., Unuabonah, I.E. & Olu-Owolabi, B.I. (2006) The effect of some operating variables on the adsorption of lead and cadmium ions on kaolinite clay. Journal of Hazardous Materials, 134, 130–139.CrossRefGoogle ScholarPubMed
Barrientos-Velazquez, A.L., Kakani, R., Fowler, J., Haq, A.U., Bailey, C.A. & Deng, Y. (2022) Efficacy of two Texas bentonites in binding aflatoxin B1 and in reducing aflatoxicosis in broilers. Clays and Clay Minerals, https://doi.org/10.1007/s42860-022-00191-8.CrossRefGoogle Scholar
Castro, D., Hickson, S., Bettadapura, V., Thomaz, E., Abowd, G., Christensen, H. & Essa, I. (2015) Predicting daily activities from egocentric images using deep learning. Wearable Computers, 2015, 75–82.Google ScholarPubMed
Chaudhary, K., Poirion, O.B., Lu, L. & Garmire, L.X. (2017) Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clinical Cancer Research, 24, 1248–1259.CrossRefGoogle ScholarPubMed
Cheng, X.B. (2017) Graphene Oxide/Concave–Convex Rod Composite Adsorbent Preparation and Adsorption of Pb(II) in Water Research. MA thesis. Lanzhou Jiaotong University, Lanzhou, China.Google Scholar
Dai, F., Guo, J., He, Y., Song, P. & Wang, R. (2021) Enhanced thermal stability and adsorption performance of MIL-53(Fe)@montmorillonite. Clay Minerals, 56, 99–107.CrossRefGoogle Scholar
Daikh, S., Ouis, D., Benyoucef, A. & Mouffok, B. (2022) Equilibrium, kinetic and thermodynamic studies for evaluation of adsorption capacity of a new potential hybrid adsorbent based on polyaniline and chitosan for Acetaminophen. Chemical Physics Letters, 798, 139565.CrossRefGoogle Scholar
David, M.K., Okoro, U.C., Akpomie, K.G., Okey, C. & Oluwasola, H.O. (2020) Thermal and hydrothermal alkaline modification of kaolin for the adsorptive removal of lead (II) ions from aqueous solution. SN Applied Sciences, 2, 1–13.CrossRefGoogle Scholar
Erdoğan, B., Ergürhan, O. & Anter, A. (2021) Influence of acid activation on the NH3-adsorption properties of a Turkish bentonite. Clay Minerals, 56, 178–184.CrossRefGoogle Scholar
Glänzel, W., Teles, A. & Schubert, A. (1984) Correction to characterization by truncated moments and its application to Pearson-type distributions. Probability Theory and Related Fields, 66, 173–183.Google Scholar
Gomes, C., Rautureau, M., Poustis, J. & Gomes, J. (2021) Benefits and risks of clays and clay minerals to human health from ancestral to current times: a synoptic overview. Clays and Clay Minerals, 69, 612–632.CrossRefGoogle Scholar
He, H., Guo, J., Xie, X. & Peng, J. (2000) Experimental study of the selective adsorption of heavy metals onto clay minerals. Chinese Journal of Geochemistry, 19, 105–109.CrossRefGoogle Scholar
Hinton, G.E., Osindero, S. & Teh, Y.W. (2006) A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554.CrossRefGoogle ScholarPubMed
Hu, Z., Zhang, Z., Yang, H., Chen, Q. & Zuo, D. (2017) A deep learning approach for predicting the quality of online health expert question-answering services. Journal of Biomedical Informatics, 71, 241–253.CrossRefGoogle ScholarPubMed
Huang, H., Wu, X. & Cheng, X. (2020) The analysis of the urban sprawl measurement system of the Yangtze River Economic Belt, based on deep learning and neural network algorithm. International Journal of Environmental Research and Public Health, 17, 4194.CrossRefGoogle ScholarPubMed
Koppelman, M.H. & Dillard, J.G. (1977) A study of the adsorption of Ni(II) and Cu(II) by clay minerals. Clays and Clay Minerals, 25, 457–462.CrossRefGoogle Scholar
Lahreche, S., Moulefera, I., El Kebir, A., Lilia, S., M'hamed, K. & Abdelghani, B. (2022) Application of activated carbon adsorbents prepared from prickly pear fruit seeds and a conductive polymer matrix to remove Congo red from aqueous solutions. Fibers, 10, 7.CrossRefGoogle Scholar
Lai, J. (2013) Adsorption of Pb by Clay Minerals and Its CD-MUSIC Fitting. MA thesis. Huazhong Agricultural University, Wuhan, China.Google Scholar
Liu, Y., Wu, P.X., Tang, J.W. & Zeng, S.Y. (2005) Experimental study on the adsorption of heavy metal ions by polyhydroxy aluminum pillar-supported montmorillonite. Journal of Mineralogy and Petrology, 3, 122–126.Google Scholar
Liu, J., Chen, R., Li, Y., Chen, J., Chen, L., Gao, J. & Li, G. (2018) Microstructure-related Pb2+ adsorption capability of Ti-pillared montmorillonite in aqueous solution. Clays and Clay Minerals, 66, 466–473.CrossRefGoogle Scholar
Liu, J., Li, J., Wang, H. & Yan, J. (2020) Application of deep learning in genomics. Science China Life Sciences, 63, 1860–1878.CrossRefGoogle ScholarPubMed
Ma, Q., Wang, W., Ge, W., Xia, L. & Song, S. (2021) Synthesis of a composite aerogel of reduced graphene oxide supported by two-dimensional montmorillonite nanolayers for methylene blue removal. Clays and Clay Minerals, 69, 746–758.CrossRefGoogle Scholar
Orucoglu, E., Grangeon, S., Gloter, A., Robinet, J.C., Madé, B. & Tournassat, C. (2022) Competitive adsorption processes at clay mineral surfaces: a coupled experimental and modeling approach. ACS Earth and Space Chemistry, 1, 144–159.CrossRefGoogle Scholar
Ouis, D., El Kebir, A., Moulefera, I., Lilia, S. & Benyoucef, A. (2022) Synthesis, characterization and adsorption of bisphenol A using novel hybrid materiel produced from PANI matrix reinforced by kieselguhr. Journal of Inorganic and Organometallic Polymers and Materials, 32, 1092–1102.CrossRefGoogle Scholar
Sarabadan, M., Bashiri, H. & Mousavi, S.M. (2021) Modelling, kinetics and equilibrium studies of crystal violet adsorption on modified montmorillonite by sodium dodecyl sulfate and hyamine surfactants. Clay Minerals, 56, 16–27.CrossRefGoogle Scholar
SenGupta, S. & Bhattacharyya, K. (2012) Adsorption of heavy metals on kaolinite and montmorillonite: a review. Physical Chemistry Chemical Physics, 14, 6698–6723.CrossRefGoogle Scholar
Sigmund, G., Gharasoo, M., Hüffer, T. & Hofmann, T. (2020) Deep learning neural network approach for predicting the sorption of ionizable and polar organic pollutants to a wide range of carbonaceous materials. Environmental Science & Technology, 54, 4583–4591.CrossRefGoogle ScholarPubMed
Silva, R.P., Gois, A.G., Ramme, M.O., Dantas, T.N.C., Barillas, J.L. & Santanna, V.C. (2021) Adsorption of cetyltrimethyl ammonium bromide surfactant for organophilization of palygorskite clay. Clay Minerals, 56, 140–147.CrossRefGoogle Scholar
Wang, H.K. & Gong, W.Q. (2006) Application of clay mineral materials to the treatment of heavy metal wastewater. Industrial Water Treatment, 26, 4–7.Google Scholar
Wang, Y., Zhuang, Y., Wang, S., Liu, Y., Kong, L., Li, J. & Chen, H. (2022) Preparation and characterization of porous palygorskite/carbon composites through zinc chloride activation for wastewater treatment. Clays and Clay Minerals, https://doi.org/10.1007/s42860-022-00187-4.CrossRefGoogle Scholar
Werling, N., Kaltenbach, J., Weidler, P.G., Schuhmann, R., Dehn, F. & Emmerich, K. (2022) Solubility of calcined kaolinite, montmorillonite, and illite in high molar NaOH and suitability as precursors for geopolymers. Clays and Clay Minerals, 70, 270–289.CrossRefGoogle Scholar
Xie, J.Y., Wu, Z.Z. & Zheng, Q.Q. (2020) 2D adaptive feature selection algorithm based on information gain and Pearson correlation coefficient. Journal of Shaanxi Normal University (Natural Science Edition), 48, 69–81.Google Scholar
Xu, L., Dai, H., Skuza, L., Xu, J., Shi, J., Wang, Y. et al. (2022) Integrated survey on the heavy metal distribution, sources and risk assessment of soil in a commonly developed industrial area. Ecotoxicology and Environmental Safety, 236, 113462.CrossRefGoogle Scholar
Yu, C., Lv, J., Zhou, M., Cai, X. & Yu, X. (2019) Remediation of heavy metal Pb (II) in aqueous solution using kaolin-supported nano iron. Materials Research Express, 6, 11.CrossRefGoogle Scholar
Zhang, L.L. (2012) Adsorption Characteristics of Pb(II) by Clay Minerals. MA thesis. Dalian University of Technology, Dalian, China.Google Scholar
Supplementary material: File

Liu et al. supplementary material

Liu et al. supplementary material

Download Liu et al. supplementary material(File)
File 2.8 MB