Understanding Contributions of Divalent Cations in Mineral Carbonation Using Artificial Neural Network

Abidoye, L.K. (Process Engineering Department, National University of Science and Technology, Oman; Chemical Engineering Department, Osun State University, Nigeria)
Oladipo, H.B. (Process Engineering Department, National University of Science and Technology, Oman)

Article ID: 4465


The roles played by divalent cations (calcium, magnesium and iron) of rock minerals in the efficiency of mineral carbonation have been investigated. Statistical modeling with Artificial Neural Network (ANN) having configuration ANN[17-4-1] shows that carbonation efficiency largely increases as the quantity of calcium content increases. Averagely,there is approximately 5% rise in the original efficiency for 10% increase in the quantity of calcium. This changes to 3.4% and 1.6% increases in efficiency, relative to the original efficiency for 20% and 30% increases in calcium content, respectively. Iron content of minerals offers clear positive correlation to the carbonation efficiency. From the global average, there is approximately 17% rise in the original efficiency for 10% increase in the quantity of iron. This increases to 29% and 41% over the original efficiency for 20% and 30% increases in iron content, respectively.. The influence of magnesium was found to be mainly negatively correlated to carbonation efficiency, after exceeding an unknown threshold. The global average of the efficiency changes with magnesium content results in original efficiency rising by 2% at 10% quantity increase and then reduces by 3% and 9% for 20% and 30% increase in magnesium quantity, respectively, relative to the original efficiency. Thus, iron compounds are found to be most potent of the divalent cations in carbonation reaction while calcium and magnesium content should maintain a threshold ratio with silica content for improved efficiency.


Carbonation; ANN; Calcium; Magnesium; Iron

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[1] Wang, F., Dreisinger, D., Jarvis, M., et al., 2019.Kinetics and mechanism of mineral carbonation of olivine for CO2 sequestration. Minerals Engineering.131, 185-197.DOI: https://doi.org/10.1016/j.mineng.2018.11.024

[2] Pasquier, L.C., Mercier, G., Blais, J.F., et al.,2014. Reaction mechanism for the aqueous-phase mineral carbonation of heat-activated serpentine at low temperatures and pressures in flue gas conditions.Environmental Science & Technology. 48, 5163-5170. DOI: https://doi.org/10.1021/es405449v

[3] Xi, F., Davis, S.J., Ciais, P., et al., 2016. Substantial global carbon uptake by cement carbonation. Nature Geoscience. 9, 880-883.DOI: https://doi.org/10.1038/ngeo2840

[4] Syed Hasan, S.N.M., Mohd Kusin, F., Jusop, S., et al., 2018. Potential of soil, sludge and sediment for mineral carbonation process in Selinsing gold mine, Malaysia. Minerals. 8(6), 257.

[5] Huijgen, W.J.J., Witkamp, G., Comans, R.N.J., 2006. Mechanisms of aqueous wollastonite carbonation as a possible CO2 sequestration process.Chemical Engineering Science. 61, 4242-4251.DOI: https://doi.org/10.1016/j.ces.2006.01.048

[6] Sanna, A., Uibu, M., Caramanna, G., et al., 2014.A review of mineral carbonation technologies to sequester CO2. Chemical Society Reviews. 43,8049-8080.

[7] Ramli, N.A.A., Kusin, F.M., Molahid, V.L.M., 2021.Influencing Factors of the Mineral Carbonation Process of Iron Ore Mining Waste in Sequestering Atmospheric Carbon Dioxide. Sustainability. 13, 1866.DOI: https://doi.org/10.3390/su13041866

[8] Ukwattage, N.L., Ranjith, P.G., Li, X., 2017. Steel-making slag for mineral sequestration of carbon dioxide by accelerated carbonation. Measurement Journal of the International Measurement Confederation. 97, 15-22. DOI: https://doi.org/10.1039/c4cs00035h

[9] Hanspal, N.S., Allison, B.A., Deka, L., et al.,2013. Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media. Journal of Hydroinformatics. 15(2), 540-554. DOI: https://doi.org/10.2166/hydro.2012.119

[10] Abidoye, L.K., Das, D.B., 2015. Artificial Neural Network (ANN) Modelling of Scale Dependent Dynamic Capillary Pressure Effects in Two-Phase Flow in Porous Media. Journal of Hydroinformatics. 17(3), 446-461. DOI: https://doi.org/10.2166/hydro.2014.079

[11] Abidoye, L.K., Mahdi, F.M., Idris, M.O., et al.,2018. ANN-Derived Equation and ITS Application in the Prediction of Dielectric Properties of Pure and Impure CO2. Journal of Cleaner Production. DOI: https://doi.org/10.1016/j.jclepro.2017.12.013

[12] Li, J., Hitch, M., Power, I.M., et al., 2018. Integrated Mineral Carbonation of Ultramafic Mine Deposits—A Review. Minerals. 8, 147.DOI: https://doi.org/10.3390/min804014

[13] Lackner, K.S., 2003. A Guide to CO2 Sequestration. Science. 300, 1677-1678. [CrossRef] [PubMed]

[14] Lackner, K., Wendt, C., Butt, D., et al., 1995. Carbon dioxide disposal in carbonate minerals. Energy. 20, 1153-1170. [CrossRef]

DOI: https://doi.org/10.30564/agger.v4i2.4465


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