BLAST FURNACE SLAG FOR SO2 CAPTURE: OPTIMIZATION AND PREDICTION USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK Original scientific paper

Main Article Content

Itumeleng Kohitlhetse
https://orcid.org/0000-0002-5462-0657
Suter Kiplagat Evans
https://orcid.org/0000-0003-1316-6889
Musamba Banza
https://orcid.org/0000-0001-5858-4190
Robert Makomere
https://orcid.org/0000-0002-0434-1633

Abstract

The main reaction parameters examined were the amount of blast furnace slag, the hydration duration, ammonium acetate concentration, and temperature. The Response surface methodology was employed to quantify their impact on the sorbent's surface area. Using a central composite design, the surface area of the resulting sorbent corresponding to Brunauer- Emmett-Teller (BET) was investigated. The sorbents produced range in surface area from 49.89 to 155.33 m2/g. Additionally, the effectiveness and response prediction capacities of the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling methodologies were investigated. The models were assessed using various statistical metrics, including (MSE) mean squared error, (ARE) average relative errors, the (SSE) sum of squared errors, (HYBRID) Hybrid fractional error function, (SAE) Sum of the absolute errors, (R2)coefficient of determination, and Root means square. According to statistical evidence, the ANN approach surpassed the RSM-CCD model approach. The surface area of the sorbent was shown to be significantly influenced by interactions between variables in addition to all the individual variables examined. The sorbent was made from a material with substantial structural porosity based on SEM. The functional groups were identified using FTIR. The XRF determined the elemental composition of BFS-based sorbents.

Article Details

How to Cite
Kohitlhetse, I. ., Evans, S. K. ., Banza, M. ., & Makomere, R. . (2024). BLAST FURNACE SLAG FOR SO2 CAPTURE: OPTIMIZATION AND PREDICTION USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK: Original scientific paper. Chemical Industry & Chemical Engineering Quarterly, 30(4), 349–357. https://doi.org/10.2298/CICEQ230717006K
Section
Articles

References

B.J. Shokri, F. Shafaei, F.D. Ardejani, S. Entezam, Soil Sediment Contam. 32 (2023) 23—40. https://doi.org/10.1080/15320383.2022.2090895.

L. Lerotholi, R.C. Everson, L. Koech, H.W.J.P. Neomagus, H.L. Rutto, D. Branken, B. B. Hattingh, P. Sukdeo, Clean Technol. Environ. Policy 24 (2022) 2011—2060. https://doi.org/10.1007/s10098-022-02308-y.

C. Zheng, K. Li, C. Zhang, D. Deng, Sep. Sci. Technol. 56 (2021) 2499—2506. https://doi.org/10.1080/01496395.2020.1833218.

R.S. Makomere, H.L. Rutto, L. Koech, Arabian J. Sci. Eng. 48 (2022) 8871—8885. https://doi.org/10.1007/s13369-022-07491-0.

A. López-Olvera, S. Pioquinto-García, J. Antonio Zárate, G. Diaz, E. Martínez-Ahumada, J.L. Obeso, V. Martis, D.R. Williams, H.A. Lara-García, C. Leyva, C.V. Soares, G. Maurin, I.A. Ibarra, N.E. Dávila-Guzmán, Fuel 322 (2022) 124213. https://doi.org/10.1016/j.fuel.2022.124213.

X. Li, T. Huhe, T. Zeng, X. Ling, Z. Wang, H. Huang, Y. Chen, Heliyon 8 (2022) 11463. https://doi.org/10.1016/j.heliyon.2022.e11463.

G. Long, C. Yang, X. Yang, T. Zhao, M. Xu, J. Mol. Liq. 302 (2020) 112538. https://doi.org/10.1016/j.molliq.2020.112538.

R. Makomere, H. Rutto, L. Koech, H. Rutto, L. Koech, J. Environ. Sci. Health, Part A 58 (2023) 191—203. https://doi.org/10.1080/10934529.2023.2174334.

D. Gazioglu Ruzgar, S. Altun Kurtoglu, M.F. Fellah, J. Nat. Fibers 19 (2022) 1366—1375. https://doi.org/10.1080/15440478.2020.1764459.

Y. Yu, R. Zhao, J. Chen, H. Yao, Chem. Eng. J. 431 (2022) 134267. https://doi.org/10.1016/j.cej.2021.134267.

M. Banza, H. Rutto, Int. Nano Lett. 12 (2022) 257—272. https://doi.org/10.1007/s40089-022-00369-x.

S. Chellapan, D. Datta, S. Kumar, H. Uslu, Chem. Data Collect. 37 (2022) 100806. https://doi.org/10.1016/j.cdc.2021.100806.

M. Banza, H. Rutto, T. Seodigeng, Soil Sediment Contam. Int. J. 0 (2023) 1—21. https://doi.org/10.1080/15320383.2023.2178384.

C. Mgbemena, S. O. Onyegu, Qeios (2023) 32388. https://doi.org/10.32388/GEGPL7.

A.A. Ayoola, F.K. Hymore, C.A. Omonhinmin, O.C. Olawole, O.S.I. Fayomi, D. Babatunde, O. Fagbiele, Chem. Data Collect. 22 (2019) 100238. https://doi.org/10.1016/j.cdc.2019.100238.

J. Kabuba, M. Banza, Results Eng. 8 (2020) 100189. https://doi.org/10.1016/j.rineng.2020.100189.

M. Banza, H. Rutto, Can. J. Chem. Eng. 101 (2023) 896—908. https://doi.org/10.1002/cjce.24384.

Z. Zhao, K. Patchigolla, Y. Wu, J. Oakey, E.J. Anthony, H. Chen, Fuel Process. Technol. 221 (2021) 106938. https://doi.org/10.1016/j.fuproc.2021.106938.

J.X. Liu, J. Li, W.Q. Tao, Z. Li, Fluid Phase Equilib. 536 (2021) 112963. https://doi.org/10.1016/j.fluid.2021.112963.

J. Lim, J. Kim, Fuel 327 (2022)124986. https://doi.org/10.1016/j.fuel.2022.124986.

J.R. Hanumanthu, G. Ravindiran, R. Subramanian, P. Saravanan, J. Indian Chem. Soc. 98 (2021)100086. https://doi.org/10.1016/j.jics.2021.100086.

D.S.P. Franco, F.A. Duarte, N.P.G. Salau, G.L. Dotto, Chem. Eng. Commun. 206 (2019) 1452—1462. https://doi.org/10.1080/00986445.2019.1566129.

Similar Articles

You may also start an advanced similarity search for this article.