Optimisation Studies of Naphthalene Adsorption on Bentonite Clay Impregnated on Chitosan and Surfactant using RSM–CCD, ANN–BP and ANN–PSO Techniques

  • Olaosebikan Abidoye Olafadehan

Abstract

The effects of process variables on naphthalene adsorption on bentonite clay–cetyltrimethylammonium bromide (CTAB)–chitosan matrix were investigated in this study.  The independent variables are initial concentration ( ), adsorbent dosage ( ), contact time ( ), temperature ( ) and pH ( ) while the response variable is the % removal of naphthalene.  The adsorption process was modelled and optimised using the response surface methodology (RSM)–central composite design (CCD), artificial neural network (ANN)–back propagation (BP) and ANN–particle swarm optimization (PSO) algorithms.  The interactive terms of  and ,    and ,  and , and and  give a synergistic effect on the adsorption process.  On account of –value, temperature and adsorbent dosage are the most influential single terms.  Among the square terms, adsorbent dosage was found to be the most controlling factor while adsorbent dosage and contact time were the most significant interactive factors for the adsorption process.  The RSMCCD, ANNBP and ANNPSO aptly modelled and optimised the process variables.  However, the ANN-PSO algorithm excelled over the RSMPSO and ANNBP owing to its highest  value of 0.9803, least value of the error functions considered and percentage error of validation.  Hence, ANN-PSO is projected for the optimisation studies of naphthalene adsorption on the synthesised bentonite clay–cetyltrimethylammonium bromide (CTAB)–chitosan matrix. These approaches enable scalable and data-driven solutions, which minimise costly trial-and-error experiments for treating industrial wastewater contaminated with persistent pollutants of PAHs.

References

ACGIH – American Conference of Governmental Industrial Hygienists (2010) Threshold limit values and biological exposure indices. ACGIH, Cincinnati, OH.
Afroozeh, M., Sohrabi, M. R., Davallo, M., Mirnezami, S. Y., Motiee, F. and Khosravi, M. (2018). Application of artificial neuro-fuzzy inference system to predict the removal of Pb (II) ions from the aqueous solution by using magnetic graphene/nylon 6. Chemical Sciences Journal, 9: 1–7. https://doi.org/10.4172/2150-3494.10001.185
Agarry, S. E., Ogunleye, O. O. and Aworanti, O. A. (2013). Biosorption equilibrium, kinetic and thermodynamic modelling of naphthalene removal from aqueous solution onto modified spent tea leaves. Environmental Technology, 34: 825–839. https://doi.org/10.1080/09593330.2012.720616
Al-Alam, J., Lévy, M., Ba, H., Pham-Huu, C. and Millet, M. (2020). Passive air samplers based on ceramic adsorbent for monitoring of organochlorine pesticides, polycyclic aromatic hydrocarbons and polychlorinated biphenyls in outdoor air. Environmental Technology & Innovation, 20: 101094. https://doi.org/10.1016/j.eti.2020.101094
Al-Alam, J., Millet, M., Khoury, D., Rodrigues, A., Akoury, E., Tokajian, S. and Wazne, M. (2024). Biomonitoring of PAHs and PCBs in industrial, suburban, and rural areas using snails as sentinel organisms. Environmental Science and Pollution Research, 31: 4970–4984. http://doi.org/10.1007/s11356-023-31493-6
Albayati, T. M. and Kalash, K. R. (2020). Polycyclic aromatic hydrocarbons adsorption from waste water using different types of prepared mesoporous materials MCM-41 in batch and fixed bed column. Process Safety and Environmental Protection, 133: 124–136. https://doi.org/10.1016/j.psep.2019.11.007
Alharbi, H. A., Alotaibi, K. D., El-Saeid, M. H. and Giesy, J. P. (2023). Polycyclic aromatic hydrocarbons (PAHs) and metals in diverse biochar and products: effects of feedstock type and pyrolysis temperature. Toxics, 11: 1–15. https://doi.org/10.3390/toxics11020096
Alizamir, M. and Sobhanardakani, S. (2018). An artificial neural network-particle swarm optimization (ANN-PSO) approach to predict heavy metals contamination in ground resources. Jundishapur Journal of Health Sciences, 1–8. https://doi.org/10.5812/jjhs.67544
Al-Salman, A. N., Al-Niaeem, K. S. and A-Ghizzawi, G. J. (2023). Polycyclic aromatic hydrocarbons (PAHs) in sediment and the health risk to fish in the Shatt Al-Arab River, Basrah, Iraq. Research in Veterinary Science, 155: 1–23.
Arikan, B., Konakci, C. O., Yildiztugay, E., Turan, M. and Cavusoglu, H. (2022). Polystyrene nanoplastic contamination mixed with polycyclic aromatic hydrocarbons: Alleviation on gas exchange, water management, chlorophyll fluorescence and antioxidant capacity in wheat. Environmental Pollution, 311: 119851. http://doi.org/10.1016./j.envpol.2022.119851
Asadu, C. O., Ekwueme, B. N., Onu, C. E., Onah, T. O., Ike, I. C. and Ezema, C. A. (2022). Modelling and Optimization of crude oil removal from surface water via organic acid functionalized biomass using machine learning approach. Arabian Journal of Chemistry, 15: 1–21. https://doi.org/10.1016/j.arabjc.2022.104025
Asfaram, A., Ghaedi, M., Azqhandi, A. M. H., Goudarzi, A. and Dastkhoon, M. (2016). Statistical experimental design, least square-support vector machines (LS-SVM) and artificial neural network (LS-SVM) and artificial neural network (NN) models for modelling the facilitated adsorption of methylene blue dye. RSC Advances, 6: 40502–40516. https://doi.org/10.1039/cbra01874b
Atemkeng, C. D., Anagho, G. S., Tagne, R. F. T., Amola, L. A., Bopda, A. and Kamgaing, T. (2021). Optimization of 4-nonylphenol adsorption on activated carbons derived from Safou seeds using response surface methodology. Carbon Trend, 4: 100052. https://doi.org/10.1016/y.cartre.2021.100052
Avila, H. E. R., Villarreal, I. A. A., Munoz, L. L. D., Perez, J. M., Pruiz, F. J. S., Mayorga, C. K. R., Castillo, D. I. M. and Petriciolet, A. B. (2022). A review of the modelling of adsorption of organic and inorganic pollutants from water using artificial neural networks. Adsorption Science and Technology, 2022: 1–51. https://doi.org/10.1155/2022/9384871
Azeez, S. O., Jimoh, A. A., Saheed, I. O., Otun, K. O., Mustapha, A. O. and Adekola, F. A. (2022). Optimization by Box Behnken design for Eosin yellow dye removal from aqueous medium using data palm seeds-porous carbon @ TiO2 blend. Journal of Nigerian Society of Physical Sciences, 4: 183–192. http://dx.org/10.46481/jnsps.2022.533
Azhar-ul-Haq, M., Javed, T., Abid, M. M., Masood, H. T. and Muslim, N. (2022). Adsorptive removal of hazardous crystal violet dye onto banana peel powder: equilibrium, kinetic and thermodynamics studies. Journal of Dispersion Science and Technology, 1–16. https://dx.doi.org/10.1080/01932691.2022.2158851
Balati, A., Shahbazi, A., Amini, M. M. and Hashemi, S. H. (2015). Adsorption of polycyclic aromatic hydrocarbons from waste water by using silica-based organic-inorganic nanohybrid material. Journal of Water Reuse and Desalination, 5: 1–14. https://doi.org/10.2166/wrd.2014.013
Batchamen Mougnol, J. B., Waanders, F., Ntwampe, S. K. O., Fosso-Kankeu, E. and Al Alili, A. R. (2022). Synthesis of eco‑friendly ZnO‑based heterophotocatalysts with enhanced properties under visible light in the degradation of organic pollutants. Environmental Systems Research, 11(25): 25. https://doi.org/10.1186/s40068-022-00271-7
Bello, V. E. and Olafadehan, O. A. (2021). Comparative investigation of RSM and ANN for multi-response modeling and optimization studies of derived chitosan from Archachatina marginata shell, Alexandria Engineering Journal, 60(4): 3869–3899. https://doi.org/10.1016/j.aej.2021.02.047
Bieda, A. F., Pereee, A. and Zalas, A. R. (2023). Modelling and optimization of Geraniol ((2E)-3,7-dimethyl-2,6-cctadiene-1-ol) transformation process using response surface methodology (RSM). Catalysts, 13: 1–16. https://dx.doi.org/10.3390/catal13020320
Chittoo, B. S. and Sutherland, C. (2020). Column breakthrough studies for the removal and recovery of phosphate by lime-iron sludge: modeling and optimization using artificial neural network and adaptive neuro-fuzzy inference system. Chinese Journal of Chemical Engineering, 28(7):1847–1859. https://doi.org/10.1016/j.cjche.2020.02.02
da Costa, T. B., da Silva, T. L., Costa, C. S. D., da Silva, M. G. C. and Vieira, M. G. A. (2022). Chromium adsorption using Sargassum filipendula algae waste from alginate extraction: batch and fixed-bed column studies. Chemical Engineering Journal Advances, 11: 100341. http://doi.org/10.1016/j.ceja.2022.100341
Dahlan, I., Azhar, E. E. M., Hassan, S. R., Aziz, H. A. and Hung, Y. T. (2022). Statistical modelling and optimization of process parameters for 2,4-dichlorophenoxyacetic acid removal by using ac/pdmaema hydrogel adsorbent: comparison of different RSM designs and ANN training methods. Water, 14: 1–17. https://doi.org/10.3390/w14193061
Danesh, N., Ghorbabani, M. and Marjani, A. (2021). Separation of copper ions by nano-composites using adsorption process. Scientific Reports, 11: 1–23. https://doi.org/10.1038/s41598-020-80914-w
Darwish, M. S. A., Mostafa, M. H. and Al-Harbi, L. M. (2022). Polymeric nanocomposites for environmental and industrial applications. International Journal of Molecular Sciences, 23: 1023. http://doi.org/10.3390/ijms23031023
Domgas, R., Dauda, A., Arnand, M. A. G., Balike, M., Bagamla, W. and Bosco, T. J. (2023). Optimization of methylene blue adsorption onto activated carbon from Bos Indicus Gudali bones using a Box Behnken Experimental design. Journal of the American Chemical Society, 12: 1–9. https://doi.org/10.5923/j.chemistry.20221201.01
Eberhart, R. C. and Kennedy, J. (1995). A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1: 39–43. http://dx.doi.org/10.1109/MHS.1995.494215
El-Hanandeh, A., Zainab, M. and Imtiaz, M. S. (2021). Modelling of the adsorption of Pb, Cu and Ni ions from single and multi-component aqueous solutions by date seed derived biochar: comparison of six machine learning approaches. Environmental Research, 192: 110338. https://doi.org/10.1016/j.enres.2020.110338
El-Shorbagy, M. A. and Hossaniem, A. E. (2018). Particle swarm optimization from theory to applications. Int J Rough Sets Data Anal 5:1–24. https://doi.org/10.4018/IJRSDA.2018040101
El-Zahhar, A. A. and Idris, A. M. (2022). Synthesis, characterization, and application of TiO2–magnetite/chitosan nanocomposite for adsorptive removal of naphthalene from aqueous solutions. Petroleum Chemistry. 62: 788–799. https://doi.org/10.1134/S0965544122010066
Emezie, N. E., Etuk, B. R., Akpan, O. P. and Chiweoke, O. C. (2022). Cyanide removal from cassava wastewater onto H3PO4 activated periwinkle shell carbon. Applied Water Science, 12: 1–12. https://doi.org/10.1007/s13201-022-01679-3
Emoyan, O. O., Agbaire, P. O., Otobrise, C. and Akporhonor, E. E. (2011). Distribution pattern of polyaromatic hydrocarbon (PAHs) in soils in the vicinity of fuel stations in Abraka, Nigeria. Journal of Applied Sciences and Environmental Management, 15: 513–516
Faisal, A. A., Ramadhan, Z. K., Al-Ansari, N., Sharma, G., Naushad, M. and Bathula, C. (2022). Precipitation of (Mg/Fe-CTAB)-layered double hydroxide nanoparticles onto sewage sludge for producing novel sorbent to remove Congo red and methylene blue dyes from aqueous environment. Chemosphere, 291: 132693. https://doi.org/10.1016/j.chemosphere.2021.132693
Frescura, L. M., de Menezes, B. B., Duarte, R. and da Rosa, M. B. (2020). Application of multivariate analysis on naphthalene adsorption in aqueous solutions. Environmental Science and Pollution Research, 27: 3329–3337. https://doi.org/10.1007/s11356-019-07278-1
Ganesapillai, M., Sinha, A., Mehta, R., Tiwari, A., Chellappa, V. and Drewnowski, J. (2022). Design and analysis of artificial neural network (ANN) models for achieving self-sustainability in sanitation. Applied Sciences, 12: 1–21. https://doi.org/10.3390/app12073384
Garbal, Z. N., Ekwumemgbo, P. A. and Stephen, G. (2022). Optimization of phenol adsorption from synthetic waste water by synthesized BiFeO3 perovskite material using split-plot central composite design. Bulletin of the National Research Centre, 46: 1–14. http://doi.org/10.1186/s42269-022-00866-1
Gil, A., Santamaria, L., Korili, S. A., Vicente, M. A., Barbosa, L. V., de Souza, S. D., Marcel, L., de Faria, E. H. and Ciuffi, K. J. (2021). A review of organic-inorganic hybrid clay-based adsorbents for contaminants removal: synthesis, perspectives and applications. Journal of Environmental Chemical Engineering, 9: 1–18. https://doi.org/10.1016/j.jece.2021.105808
Gong, C., Huang, H., Qian, Y., Zhang, Z. and Wu, H. (2017). Integrated electrocoagulation and membrane filtration for PAHs removal from realistic industrial waste water: effectiveness and mechanisms. The Royal Society of Chemistry Advances, 7: 52366–52374. https://doi.org/10.1039/C7ra09372a
Hussain, M. S., Rehman, R. and Imran, M. (2022). Comparative evaluation of the adsorption performance of citric acid treated peels of Trapa natans and citrullus lanatus for cationic dyes degradation from water. Journal of Chemistry, 2022: 1109376. https://doi.org/10.1155/2022/1109376
Ike, I. S., Asadu, C. O., Ezema, C. A., Onah, T. O., Ogbodo, N. O., Nwakwasi, E. U. G. and Onu, C. E. (2022). ANN-GA, ANFIS-GA and thermodynamics base modelling of crude oil removal from water surface using organic acid grated banana pseudo stem fiber. Applied Surface Science Advances, 9: 1–13. https://doi.org/10.1016/j.apsadv.2022.100259
Ivwurie, W. and Okiriguo, D. (2022). Evaluation of polycyclic aromatic hydrocarbon from selected communities in Udu local government area of Delta state. FUPRE Journal of Scientific and Industrial Research, 6: 1–11.
Karri, R. R. and Sahu, J. N. (2018). Modeling and optimization by particle swarm embedded neural network for adsorption of zinc (II) by palm kernel shell based activated carbon from aqueous environment. J Environ Manage 206:178–191. https://doi.org/10.1016/j.jenvman.2017.10.026
Kim, U. J., Saito, N. and Lee, S. H. (2022). Remediation of water contamination with polycyclic aromatic hydrocarbons using liquid phase plasma: influence of electrical discharge condition. Frontier in Marine Science, 9: 1–12. https://dx.doi.org/10.3389/fmars.2022.1033962
Law, S. K., Fung, Y. H., Chan, H. Y., Han, J. and Lo, C. M. (2022). A mini-review for an adsorption of polycyclic aromatic hydrocarbons (PAHs) by physical gel. Biointerface Research in Applied Chemistry, 12: 8195–8204. https://doi.org/10.33263/BRIAC126.81958204
Li, H., Budarin, V. L., Clark, J. H., North, M. and Wu, X. (2022). Rapid and efficient adsorption of methylene blue dye from aqueous solution by hierarchically porous, activated starbons®: Mechanism and porosity dependence. Journal of Hazardous Materials, 436: 1–16. https://doi.org/10.1016/j.jhazmat.2022.129174
Llyas, M., Ahmad, W. and Khan, H. (2021). Utilization of activated carbon derived from waste plastic for decontamination of polycyclic aromatic hydrocarbons laden waste water. Water Science & Technology, 84: 609–631. https://doi.org/10.2166/Wst.2021.252
Manyangadze, M., Chikuruwo, N. H. M., Narsaiah, T. B., Chakra, C. S., Radhakumari, M. and Danha, G. (2020). Enhancing adsorption capacity of nano-adsorbents via surface modification: A review. South African Journal of Chemical Engineering, 31: 25–32. https://doi.org/10.1016/j.sajce.2019.11.003
Moreno, R. S. and Salazar, Z. (2018). An artificial neural network model to analyze maize price behaviour in Mexico. Applied Mathematics, 9: 473–487. https://doi.org/10.4236/am.2018.95034
Nagababu, A., Reddy, D. S. and Mohan, G. V. K. (2022). Toxic chrome removal from industrial effluents using marine algae: modelling and optimization. Journal of Industrial & Engineering Chemistry, 114: 337–390. https://doi.org/10.1016/j.jiec.2022.07.027
Narayanan, S. L., Kasiselvanathan, M., Gurumoorthy, K. B. and Kiruthika, V. (2023). Particle swarm optimization based artificial neural network (PSO-ANN) model for effective k-barrier count intrusion detection system in WSN. Measurement: Sensors, 29: 1–6. https://doi.org/10.1016/j.measen.2023.100875
Nayagam, J. O. P., Prasanna, K. and Kumar, P. S. (2023). Effective separation of toxic azo dyes from water system using the activated carbon derived from Prosopis juliflora roots. Desalination & Water Treatment, 285: 242–263. https://doi.org/10.5004/dwt.2023.29216
Nguyen, T. T. T., Hoang, D. Q., Nguyen, D. T. C. and Tran, T. V. (2022). Adsorptive optimization of crystal violet dye using central composite rotatable design and response surface methodology: statistical analysis, kinetic and isotherm studies. Arabian Journal for Science & Engineering, 1:1–14. https://doi.org/10.1007/s13369-022-07391-.3
NMAM (NIOSH Manual of Analytical Methods) (2019) Polycyclic aromatic hydrocarbon by GC: methods 5515. 4th ed., U.S. Department of Health, Education, and Welfare.
Okoji, A., Ambrose, A., James, O., Abiola, T. and Osuolale, F. (2021). Energetic assessment of a precalcining rotary kiln in a cement plant using simulator and neural networks. Alexandria Engineering Journal, 61(7): 5097–5109. https://doi.org/10.1016/j.aej.2021.010
Okoro, H. K., Tella, A. C., Ajibola, O. A., Zvinowanda, C. and Ngila, J. C. (2019). Adsorptive removal of naphthalene and anthracene from aqueous solution with zinc and copper-terephthalate metal-organic frame works. Bulletin of the Chemical Society of Ethiopia, 33: 229–241. https://doi.org/10.4314/bese.v3312.4
Olafadehan, O. A., Aminu, F. U., Adewunmi, O. V., Kabiawu, A. I., Akinyanju, A. S., Amokun, M. K., Bello, A. M. and Olafadehan, Q. O. (2025). Isothermic, kinetic and thermodynamic studies of chromium (VI) ions adsorption on composite adsorbent of chitosan-eggshell activated carbon. Petroleum & Petrochemical Engineering Journal, 9: 1–34. https://doi.org/10.23880/ppej-16000408
Olafadehan, O. A. and Bello, V. E. (2022). Comparative Studies of RSM, RSM-GA and ANFILS for modelling and optimization of naphthalene adsorption on chitosan-CTAB-sodium bentonite clay matrix. Journal of Applied Science & Process Engineering, 9(1): 1242–1280. http://doi.org/10.33736/jaspe.4749.2022
Olafadehan, O. A., Bello, V. E. and Adesina, A. J. (2023). ANN optimization of adsorption of naphthalene on composite nanoparticles of chitosan-CTAB-sodium bentonite clay. Petroleum & Petrochemical Engineering Journal, 7(2): 000354. http://doi.org/10.23880/ppej-16000354
Olafadehan, O. A., Bello, V. E. and Amoo, K. O. (2022). Production and characterization of composite nanoparticles derived from chitosan, CTAB and bentonite clay. Chemical Papers, 76:5063–5086. https://doi.org/10.1007/s11696-022-02228-7
Onukwuli, O. D. and Nnanwube, I. A. (2022). Optimization of zinc recovery from sphalerite using response surface and particle swarm optimization. Periodica Polytechnica Chemical Engineering, 66: 20–29. https://doi.org/10.3311/PPch.17897
Peng, X., Du, P. F., Tang, H., Meng, Y., Yuan, L. and Sheng, L. P. (2018). Degradation of polycyclic aromatic hydrocarbons: a review. Applied Ecology and Environmental Research, 16: 6419–6440. http://dx.org/10.15666/acer/1605_64196440
Prabhahar, M., Gomathi, K., Venkatesh, R., Stalany, V. M., Vijayan, D. S., Sassykova, L. R., Sendilvalan, S., Priya, V. S., Jijina, G. O. and Selvaraj, R. (2022). Isothermic and kinetic study on removal of methylene blue dye using anisomeles malabarics silver nanoparticles: an efficient adsorbent to purify dye-contaminated waste water. Adsorption Science & Technology, 2022: 1–7. https://doi.org/10.1155/2022/9878987
Puszkarewicz, A. and Kaleta, J. (2020). The efficiency of the removal of naphthalene from aqueous solutions by different adsorbents. Int J Environ Res Public Health 17:1–16. https://doi.org/10.3390/ijerph17165969
Queiroz, R. N., da Silva, M. G. C., Mastelaro, V. R., Prediger, P. and Vieira, M. G. A. (2023). Adsorption of naphthalene polycyclic aromatic hydrocarbon from waste water by a green magnetic composite based on chitosan and grapheme oxide. Environmental Science and Pollution Research International, 30: 27603–27621. https://doi.org/10.1007/s11356-022-24198-9
Raaj, E. P., Bhuvaneshwari, K., Lakshmipathy, R., Devi, V. V. and Rico, I. L. R. (2022). Garlic peel surface modification and fixed column investigations towards crystal violet dye. Adsorption Science and Technology, 2022:1–9. http://dx.doi.org/10.1155/2022/6904842
Radoor, S., Karayil, J., Jayakumar, A., Parameswaranpillai, J., Lee, J. and Siengchin, S. (2022). Ecofriendly and low-cost bio adsorbent for efficient removal of methylene blue from aqueous solution. Scientific Report, 12:20580. https://doi.org/10.1038/s41598-022-22936-0
Rosinska, A. and Dabrowska, L. (2018). Selection of coagulants for the removal of chosen PAHs from drinking water. Water, 10: 1–14. https://doi.org/10.3390/w10070886
Rossides, G., Metculfe, B. and Hunter, A. (2021). Particle swarm optimization – An adaptation for the control of robotic swarms. Robotics, 10: 58. https://doi.org/10.3390/robotics10020058
Sabah, E. and Ouki, S. (2017). Adsorption of pyrene from aqueous solutions onto sepiolite. Clays and Clay Minerals, 65: 14–26. https://doi.org/10.1346/CCMN.2016.064046
Satouh, S., Martín, J., del Mar Orta, M., Medina-Carrasco, S., Messikh, N., Bougdah, N., Santos, J. L., Aparicio, I. and Alonso, E. (2021). Adsorption of polycyclic aromatic hydrocarbons by natural, synthetic and modified clays. Environments, 8: 124. https://doi.org/10.3390/environments8110124
Satyobroto, T. (2011). Mathematical modelling and applications of particle swarm optimization. MA Thesis. Blekinge Institute of Technology.
Sayed, M. A., Aly, H. F., Mahmoud, H. H., Abdelwahab, S. M., Heiai, A. F. I. and Wilson, I. D. (2022). Synthesis and characterization of hausmannite-activated carbon nanocomposites for removal of lead from aqueous solutions. Chemical Engineering & Technology, 45: 717–726. http://dx.doi.org/10.1002/ceat.202100365
Shariati, M., Mafipour, M. S., Mehrabi, P., Bahadori, A., Zandi, Y., Salih, M. N. A., Nguyen, H., Dou, J., Song, X. and Ngian, S. P. (2019). Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal and high-strength concrete. Applied Sciences, 9: 1–22. http://doi.org/10.3390/app9245534
Soltani, R., Marjani, A. and Shirazian, S. (2019). Facile one-pot synthesis of thiol-functionalized mesoporous silica submicrospheres for Tl(I) adsorption: isotherm, kinetic and thermodynamic studies. Journal of Hazardous Materials, 371: 146–155. https://doi.org/10.1016/j.jhazmat.2019.02.076
Suresh, S., Kuman, P., Jha, J. M., Verma, S., Arisutha, S. and Lens, P. N. (2022). Sonocatalytic removal of naphthalene from an aqueous solution using ZnO nanoparticles. AQUA Water Infrastructure, Ecosystems and Society, 71: 1002–1015. https://doi.org/10.2166/aqua.2022.042
Tai, Y. K., Sim, L. C., Leong, K. H. and Saravanan, P. (2021). Optimization study of adsorption parameters for removal of dye pollutant using candle soot coated egg carton. IOP Conference Series: Earth and Environmental Science, 945: 1–8. https://doi.org/10.1088/1755-1315/945/1/012012
Taiwo, O. C., Afolabi, T. J., Osuolale, F. N., Ajani, A. O., Aworanti, O. A., Ogunleye, O. R. and Alade, A. O. (2021). Recycling of waste expanded polystyrene as an effective adsorbent of naphthalene from aqueous solution. Kemija u industriji/J Chemists and Chemical Engineers, 70: 519–534. https://doi.org/10.15255/KUI.2020.084
Tentori, E. F., Ashenafi, E. L., Urshel, M. R. and Nyman, M. C. (2021). Peroxy-acid treatment of polycyclic aromatic hydrocarbons: degradation kinetics, thermodynamics and predictive modelling. Journal of Environmental Engineering, 147: 0421053, https://doi.org/10.1061/(ASCE)EE.1943-7870.0001924
Urbano, L. G., Guzman, M. V., Recuerda, R. P. and Rodriguez, J. M. (2021). Removal of polycyclic aromatic hydrocarbons (PAHs) in convectional drinking water treatment processes. Journal of Contaminant Hydrology, 243: 1–7. https://doi.org/10.1016/j.jconhyd.2021.103888
Wang, R., Zhong, M., Li, W., Chen, Y., Tan, Z., Li, X. and Zhang, J. (2022). Isotherm and kinetic studies of biosorption of low concentration Cr (III) from aqueous solution by 4 microbial biosorbents. Polish Journal of Environmental Studies, 31: 1363–1376. https://doi.org/10.15244/pjoes/141903
Xu, Q., Liu, T., Li, L., Liu, L., Liu, B., Wang, X., Zhang, S., Li, L., Wang, B., Zimmerman, A. R. and Gao, B. (2021). Hydrothermal carbonization of distillers grains with clay minerals for enhanced adsorption of phosphate and methylene blue. Bioresource Technology, 340: 125725. https://doi.org/10.1016/j.biortech.2021.125725
Yadav, A. and Roy, S. M. (2023). An artificial neural network-particle swarm optimization (ANN-PSO) approach to predict the aeration efficiency of venturi aeration system. Smart Agricultural Technology, 4: 1–9. https://doi.org/10.1016/j.atech.2023.100230
Yadav, D. K., Kumar, A. R., Jayaraman, S., Lenka, S., Gurjar, S., Sarkar, A., Saha, J. K. and Patra, A. K. (2022). Polycyclic aromatic hydrocarbons in diverse agricultural soils of central India: occurrences, sources, and potential risks. International Journal of Environmental Analytical Chemistry, 1–15. http://doi.org/10.1080/03067319.2022.2125307
Yakout, S. M., Daifullah, A. A. M. and Reefy, S. A. (2013). Adsorption of naphthalene, phenanthrene and pyrene from aqueous solution using low cost activated carbon derived from agricultural wastes. Adsorption Science & Technology, 31: 293–302.
Yemele, O. M., Zhao, Z., Nkoh, J. N., Ymele, E. and Usman, M. (2024). A systematic review of polycyclic aromatic hydrocarbon pollution: A combined bibliometric and mechanistic analysis of research trend toward an environmentally friendly solution. Science of The Total Environment, 926: 171577. https://doi.org/10.1016/j.scitotenv.2024.171577
Yeo, J. Y. J., Khaerudini, D. S., Soetaredjo, F. E., Waworuntu, G. L., Ismadji, S., Putranto, A. and Sunarso, J. (2023). Experimental and modelling study of adsorption isotherms of amoxicillin, ampicillin and doripenem on bentonite-chitosan composite. South African Journal of Chemical Engineering, 43: 38-45. https://doi.org/10.1016/j.sajce.2022.09.013
Zango, Z. U., Jumbri, K., Sambudi, N. S., Bakar, N. H. H. A., Adullah, N. A. F., Basheer, C. and Saad, B. (2019). Removal of anthracene in water by MIL-88 (Fe), NH2-MIL-88(Fe), and mixed-MIL-88(Fe) metal-organic frame works. The Royal Society of Chemistry Advances, 9: 41490–41501. https://doi.org/10.1039/c9ra0860a
Zhao, W., Hao, C., Guo, Y., Shao, W., Tian, Y. and Zhao, P. (2023). Optimization of adsorption conditions using response surface methodology for tetracycline removal by MnFe2O4/multi-wall carbon nanotubes. Water, 15:2392. https://doi.org/10.3390/w15132392
Zulaihah, L., Marasabessy, A. and Sari, S. (2020). The experiment test of absorbents for controlling poly aromatics hydrocarbon on sea water around gravity dock and hhip loading terminal. IOP Conference Series: Materials Science and Engineering, 1125: 1–8. https://dx.doi.org/10.1088/1757-899X/1125/1/012110
Published
2026-05-26
How to Cite
Olafadehan, O. A. (2026). Optimisation Studies of Naphthalene Adsorption on Bentonite Clay Impregnated on Chitosan and Surfactant using RSM–CCD, ANN–BP and ANN–PSO Techniques. Journal of Engineering Research, 31(1), 111-133. Retrieved from https://jer.unilag.edu.ng/article/view/3144