Utilizing artificial neural networks (ANN) for predictive modeling of sulfate removal from water

Year 2024, Volume: 42 Issue: 6, 1866 - 1875, 09.12.2024

Erdal Karadurmuş , Eda Göz , Cankat Keleş Mehmet Yüceer

Abstract

This research centers on developing an artificial neural network (ANN) algorithm to predict the precise removal of sulfate from synthetically prepared water samples. Two distinct resins, sodium-based cationic resin (SBCR) and divinylbenzene styrene (DVBS), were employed to achieve this goal. Additionally, the study investigated the influence of column properties (diameter and height), initial sulfate concentration, and contact time on sulfate removal from synthetically prepared samples. After collecting data from experimental trials, a feed-forward ANN structure was constructed. The selected input parameters for predicting sulfate removal encompassed column properties (diameter and height), contact time, resin type, and initial sulfate concentration. The model’s performance was assessed using several statistical criteria, including the correlation coefficient (R), mean absolute percentage error (MAPE, %), root mean square error (RMSE), and mean square error (MSE). The model’s training and test performance yielded impressive results: the correlation coefficient (R) was exceptionally high at 1.0000 for training and 0.9999 for test, indicating a strong alignment between predicted and actual values. Moreover, the mean absolute percentage error (MAPE, %) was 0.5422 for training and 0.9223 for testing, reflecting low average percentage differences between predictions and actual data and indicating high accuracy. The root mean square error (RMSE) values were also 0.0012 for training and 0.0034 for the test, demonstrating minimal average prediction errors. Lastly, the mean square error (MSE) values were notably low, with 1.42x10-6 for training and 1.14x10-5 for test phase, underscoring the model’s ability to provide accurate predictions with minimal deviations from actual values. Based on these comprehensive evaluation criteria, the ANN exhibited strong predictive performance in estimating sulfate removal.

Keywords

Adsorption, Artificial Intelligence, DVBS, SBCR, Sulfate Removal

References

• REFERENCES
• [1] Quintana-Baquedano AA, Sanchez-Salas JL, Flores-Cervantes DX. A review of technologies for the removal of sulfate from drinking water. Water Environ J 2023;37:718–728. [CrossRef]
• [2] Gupta A, Yunus M, Sankararamakrishnan N. Zerovalent iron encapsulated chitosan nanospheres - A novel adsorbent for the removal of total inorganic Arsenic from aqueous systems. Chemosphere 2012;86:150–155. [CrossRef]
• [3] Darbi, Viraraghavan T, Jin YC, Braul L, Darrell C. Sulfate removal from water. Water Qual Res J Canada 2003;38:169–182. [CrossRef]
• [4] Hong S, Cannon FS, Hou P, Byrne T, Nieto-Delgado C. Sulfate removal from acid mine drainage using polypyrrole-grafted granular activated carbon. Carbon 2014;73:51–60. [CrossRef]
• [5] Runtti H, Luukkonen T, Niskanen M, Tuomikoskia S, Kangasa T, Tynjäläc P, Emma-Tuulia Tolonena ET Sarkkinen M, Kemppainen K, Rämö J, Lassi U. Sulphate removal over barium-modified blast- furnace-slag geopolymer. J Hazard Mater 2016;317:373–384. [CrossRef]
• [6] Fernando WAM, Ilankoon IMSK, Syed TH, Yellishetty M. Challenges and opportunities in the removal of sulphate ions in contaminated mine water: A review. Miner Eng 2018;117:74–90. [CrossRef]
• [7] Salman MS. Removal of Sulfate from Waste Water by Activated Carbon. Khwarizmi Eng J 2009;5:72–76.
• [8] Ma H, Wang M, Zhang J, Sun S. Preparation mechanism of spherical amorphous ZrO(OH)2/AlOOH hybrid composite beads for adsorption removal of sulfate radical from water. Mater Lett 2019;247:56–59. [CrossRef]
• [9] Rahmati M, Yeganeh G, Esmaeili H. Sulfate ıon removal from water using activated carbon powder prepared by ziziphus spina-christi lotus leaf. Acta Chim Slov 2019;66:888–898. [CrossRef]
• [10] Salimi AH, Mousavi SF, Farzin S. Removal of sulfate from Gamasiab river water samples by using natural nano-Clinoptilolite. J Appl Res Water Wastewater 2019;6:39–44. [CrossRef]
• [11] Ao H, Cao W, Hong Y, Wu J, Wei L. Adsorption of sulfate ion from water by zirconium oxide-modified biochar derived from pomelo peel. Sci Total Environ 2020;708:135092. [CrossRef]
• [12] Sukamto YK, Rusdiarso B, Nuryono. Highly effective magnetic silica-chitosan hybrid for sulfate ion adsorption. In: H.-Y. Jeon (Ed.) Sustainable development of water and environment. Cham: Springer International Publishing; 2021. p. 203–216. [CrossRef]
• [13] Tejada-Tovar C, Villabona-Ortíz Á, Gonzalez-Delgado AD, Herrera A, Viera De la Voz A. Efficient Sulfate Adsorption on Modified Adsorbents Prepared from Zea mays Stems. Appl Sci 2021;11:1596. [CrossRef]
• [14] Obeid AF, Nile BK, and Al Juboury MF. Adsorption Sulfate from Wastewater by Using New Material E3S Web of Conferences 2023;427:04003. [CrossRef]
• [15] Shahzadi T, Anwaar A, Riaz T, Zaib M. Sulfate and phosphate ions removal using novel nano-adsorbents: modeling and optimization, kinetics, isotherm and thermodynamic studies. Int J Phytoremediation 2022;24:1518–1532. [CrossRef]
• [16] Haykin, S. Neural Networks, A Comprehensive Foundation. Upper Daddle river, New Jersey, USA: Prentice Hall; 1999.
• [17] Göz E, Yüceer M, Karadurmuş E. Total Organic Carbon Prediction with Artificial Intelligence Techniques. 29th European Symposium on Computer-Aided Process Engineering 2019;46;889–894. [CrossRef]
• [18] Karakaplan N, Göz E, Tosun E, Yüceer M. Kinetic and artificial neural network modeling techniques to predict the drying kinetics of Mentha spicata L. J Food Process Preserv 2019;43:e14142. [CrossRef]
• [19] Boztepe C, Künkül A, Yüceer M. Application of artificial intelligence in modeling of the doxorubicin release behavior of pH and temperature responsive poly(NIPAAm-co-AAc)-PEG IPN hydrogel. J Drug Deliv Sci Technol 2020;57:101603. [CrossRef]
• [20] Jawad J, Hawari AH, Zaidi SJ. Artificial neural network modeling of wastewater treatment and desalination using membrane processes: A review. J Chem Eng 2021;419:129540. [CrossRef]
• [21] Yu F, Bobashev G, Bienkowski PR, Sayler GS. Artificial neural network modeling on trichloroethylene biodegradation in a packed-bed biofilm reactor and its comparison with response surface modeling approach. Biochem Eng J 2023;191:108801. [CrossRef]
• [22] Moret A, Rubio J. Sulphate and molybdate ions uptake by chitin-based shrimp shells. Miner Eng 2003;16:715–722. [CrossRef]
• [23] Namasivayam C, Sureshkumar MV. Removal of sulfate from water and wastewater by surfactant modified coir pith, an agricultural solid 'waste' by adsorption methodology. J Environ Eng Manag 2007;17:129.
• [24] Namasivayam C, Sangeetha D. Application of coconut coir pith for the removal of sulfate and other anions from water. Desalination 2008;219:1–13. [CrossRef]
• [25] Sadeghalvad B, Khorshidi N, Azadmehr A, Sillanpaa M. Sorption, mechanism, and behavior of sulfate on various adsorbents: A critical review. Chemosphere 2021;263:128064. [CrossRef]
• [26] Salman MS. Removal of sulfate from wastewater by activated carbon. Al-Khwarizmi Eng J 2009;5:72–76.
• [27] Alimohammadi V, Sedighi M, Jabbari E. Optimization of sulfate removal from wastewater using magnetic multi-walled carbon nanotubes by response surface methodology. Water Sci Technol 2017;76:2593–2602. [CrossRef]
• [28] Hassan W, Faisal A, Abed E, Al-Ansari N, Saleh B. New composite sorbent for removal of sulfate ions from simulated and real groundwater in the batch and continuous tests. Molecules 2021;26:4356. [CrossRef]
• [29] Farahani SD, Zolgarnein J. Sulfate removal by barium-terephthalate MOF synthesized from recycled PET-waste using Doehlert design optimization. Inorg Chem Commun 2022;140:109388. [CrossRef]