Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix
DOI:
https://doi.org/10.33736/jaspe.4749.2022Keywords:
chitosan, cetyltrimethylammonium bromide, bentonite clay, RSM, ANFILS.Abstract
The aim of this article was to compare the predictive abilities of the optimization techniques of response surface methodology (RSM), the hybrid of RSM–genetic algorithm (RSM–GA) and adaptive neuro-fuzzy interference logic system (ANFILS) for design responses of % removal of naphthalene and adsorption capacity of the synthesized composite nanoparticles of chitosan–cetyltrimethylammonium bromide (CTAB)–sodium bentonite clay. The process variables considered were surfactant concentration,1X, activation time, 2X, activation temperature, 3X, and chitosan dosage, 4X. The ANFILS models showed better modeling abilities of the adsorption data on the synthesized composite adsorbent for reason of lower % mean absolute deviation, lower % error value, higher coefficient of determination, 2R, amongst others and lower error functions’ values than those obtained using RSM and RSM-GA for both responses. When applied RSM, the hybrid of RSM–genetic algorithm (RSM–GA) and ANFILS 3–D surface plot optimization technique to determine the optimal conditions for both responses, ANFILS was adjudged the best. The ANFILS predicted optimal conditions were 1X= 116.00 mg/L, 2X= 2.06 h, 3X= 81.2oC and 4X= 5.20 g. Excellent agreements were achieved between the predicted responses of 99.055% removal of naphthalene and 248.6375 mg/g adsorption capacity and their corresponding experimental values of 99.020% and 248.86 mg/g with % errors of -0.0353 and 0.0894 respectively. Hence, in this study, ANFILS has been successfully used to model and optimize the conditions for the treatment of industrial wastewater containing polycyclic aromatic compounds, especially naphthalene and is hereby recommended for such and similar studies.
References
Alslaibi, T., Abustan, I., Azmier, M., & Foul, A.A. (2013). Cadmium removal from aqueous solution using microwaved olive stone activated carbon. Journal of Environmental Chemical Engineering, 1(3), 89–599. https://doi.org/10.1016/j.jece.2013.06.028
Satouh, S., Martín, J., del Mar Orta, M., Medina-Carrasco, S., Messikh, N., Bougdah, N., Santos, J.L., Aparicio I., & Alonso E. (2021). Adsorption of polycyclic aromatic hydrocarbons by natural, synthetic and modified clays. Environments, 8(11), 124. https://doi.org/10.3390/environments8110124
Darajeh, N., Alizadeh, H., Farraji, H., Park, J., Barghi, A., & Rezania, S. (2020). Removal of polycyclic aromatic hydrocarbon (PAHs) by different physicochemical methods; a mini-review. Journal of Energy and Environmental Pollution, 1(2), 44–50. https://doi.org/10.47277/JEEP/1(2)50
Patel, A.B., Shaikas, S., Jain, K.R., Desai, C., & Madamwar, D. (2020). Polycyclic aromatic hydrocarbons: sources, toxicity and remediation approaches. Frontiers in Microbiology, 11:1–23. https://doi.org/10.3389/fmicb.2020.562813
Liu, J.J., Wang, X.C., & Fan, B. (2011). Characteristics of PAH adsorption on inorganic particles and activated sludge in domestic wastewater treatment. Bioresource Technology, 102(9), 5305–5311. https://doi.org/10.1016/j.biortech.2010.12.063
Filho, J.L.A., de Moura, L.G.M., & Ramos, A.C. da S. (2010). Polycyclic aromatic hydrocarbons (PAHs) adsorption on solid surfaces applied to waste lubricant oils recovery process. The Canadian Journal of Chemical Engineering, 88, 411–416. https://doi.org/10.1002/cjce.20286
Vhahangwele, M., Mugera, G.W., & Tholiso, N. (2014). Defluorination of drinking water using Al3+– modified bentonite clay: optimization of fluoride adsorption conditions. Toxicology & Environmental Chemistry, 96(9), 1294–1309. https://doi.org/10.1080/02772248.2014.977289
Cabal, B., Budinova, T., Ania, C.O., Tsyntsarski, B., Parra, J.B., & Petrova, B. (2009). Adsorption of naphthalene from aqueous solution on activated carbons obtained from bean pods. Journal of Hazardous Materials, 161(2– 3) , 1150–1156. https://doi.org/10.1016/j.jhazmat.2008.04.108
Yuan, M.J., Tong, S.T., Zhao, S.Q., & Jia, C.Q. (2010). Adsorption of polycyclic aromatic hydrocar-bons from water using petroleum coke-derived porous carbon. Journal of Hazardous Materials, 181(1– 3), 1115–1120. https://doi.org/10.1016/j.jhazmat.2010.05.130
Iovino, P., Canzano, S., Capasso, S., Di Natale, M., Erto, A., Lama, A., & Musmarrab, D. (2013). Single and competitive adsorption of toluene and naphthalene onto activated carbon. Chemical Engineering Transactions, 32, 67–72. https://doi.org/10.3303/CET1332012
Liu, D., Wu, Z., Ge, X., Cravotto, G., Wu, Z., & Yan, Y. (2016). Comparative study of naphthalene adsorption on activated carbon prepared by microwave-assisted synthesis from different typical coals in Xinjiang. Journal of the Taiwan Institute of Chemical Engineers, 59, 563–568. https://doi.org/10.1016/j.jtice.2015.09
Patiño-Ruiz, D.A., De Ávila, G., Alarcón-Suesca, C., González-Delgado, Á.D., & Herrera, A. (2020). Ionic cross-linking fabrication of chitosan-based beads modified with FeO and TiO2 nanoparticles: adsorption mechanism toward naphthalene removal in seawater from Cartagena bay area. ACS Omega, 5(41), 26463–26475. https://doi.org/10.1021/acsomega.0c02984
Kumari, N., & Mohan, C. (2021). Basis of clay minerals and their characteristics properties. Intech Open, 1–29. http://dx.doi.org/10.5772/intechOpen.97672
Monvisade, P., & Siriphannon, S. (2009). Chitosan intercalated Montmorillonite: preparation, characterization and cationic dye and adsorption. Applied Clay Science, 42(3–4), 427–431. https://doi.org/10.1016/j.clay.2008.04.013
Teofilović, V., Pavličević, J., Bera, O., Jovičić, M., Simendić, J.B., Szécsényi, K.M., & Aroguz, A.A. (2014). The preparation and thermal properties of chitosan/bentonite composite beads. Hemijska Industrija, 68(6), 653–659. https://doi.org/10.2298/HEMIND130905088T
Savitri, E. & Budhyantoro, A. (2017.) The effect of ratio chitosan-bentonite and processing time on the characterization of chitosan-bentonite composite. IOP Conference Series: Material Science and Engineering, 223, 012034. https://doi.org/10.1088/1757-899x/223/1/012034
Jia, J., Liu, Y., & Sun, S. (2021). Preparation and characterization of chitosan/bentonite composites for Cr (VI) removal from aqueous. Adsorption Science & Technology, 6681486, 1–15. https://doi.org/10.1155/2021/6681486
Tanhaei, B., Esfandyari, M., Ayati, A., & Sillanpää, M. (2017). Neuro-fuzzy modelling of adsorptive performance of magnetic chitosan nanocomposite. Journal of Nanostructure in Chemistry, 7, 29–36. https://doi.org/10.1007/s40097-016-0211-4
Suitana, S., Karmaker, B., Saifullah, A.S.M., Uddin, M.G., & Moniruzzaman, M. (2022). Environmental-friendly clay coagulant aid for wastewater treatment. Applied Water Science, 12, 1–10. https://doi.org/10.1007/s13201-021
Damian, G., Damian, F., Szakacs, Z., Lepure, G., & Astefanei, D. (2021). Mineralogical and physico-chemical characterization of the Orasu-Nou (Romania) bentonite resources. Minerals, 11(9), 1–19. https://doi.org/10.3390/min11090938
Oliveira, C.I.R., Rocha, M.C.G., Silva, A.L.N., & Bertolino, L.C. (2016). Characterization of bentonite clays from Cubati, Paraiba (Northeast of Brazil). Ceramica, 62(363), 272–277. https://dx.doi.org/10.1590/0366-69132016623631970
Shah, L.A., Khattak, N.S., Valenzuela, M.G.S., Manan, A., & Diaz, F.R.V. (2013). Preparation and characterization of purified Na-activated bentonite from Karak (Pakistan) for pharmaceutical use. Clay Minerals, 48(4), 595–603. https://doi.org/10.1180/claymin.2013.048.4.03
Miyoshi, Y., Tsukimura, K., Morimoto, K., Suzuki, M., & Takagi, T. (2018). Comparison of methylene blue adsorption on bentonite measured using the spot and colorimetric methods. Applied Clay Science, 151, 140–147. https://dx.doi.org/10.1016/j.clay.2017.10.023
Samiey, B., Cheng, C.H., & Wu, J. (2014). Organic-inorganic hybrid polymers as adsorbents for removal of heavy metals ions from solution: A review. Materials, 7(2), 673–726. https://doi.org/10.3390/ma7020673
Pandey, P., & De, N. (2018). Surfactant-induced changes in physicochemical characters of bentonite clay. International Research Journal of Pure and Applied Chemistry, 15(4), 1–11. https://doi.org/10.9734/IRJPAC/2017/39374
Rihayat, T., Satriananda, S., Riskina, S., Syahputra, W., & Mawaddah, N. (2019). Formulation of Polyurethane with bentonite-chitosan as filler applied to carbon steel as an antibacterial and environmentally friendly paint. IOP Conference Series: Material Science and Engineering, 536, 1–9, https://doi.org/10.1088/1757-899X/536/1/012093
Rittirong, K., Uasopon, S., Prachayawasin, P., Euaphantasate, N., Aiempanakit, K., & Ummartyotin, S. (2015). CTAB as a soft template for modified clay as filler in active packaging. Data in Brief, 3, 47–50. https://dx.doi.org/10.1016/j.dib.2015.02.002
Zohra, B., Aicha, K., Fatima, S., Nourredine, B., & Zoubir, D. (2008). Adsorption of direct red 2 on bentonite modified by cetyltrimethylammonium bromide. Chemical Engineering Journal, 136(2–3), 295–305. https://doi.org/10.1016/j.cej.2007.03.086
Betiku, E., Odude, V.O., Ishola, N.B., Bamimore, A., & Osunleke, A.S. (2016). Predictive capability evaluation of RSM, ANFIS and ANN: a case of reduction of high free fatty acid of palm kernel oil via esterification process. Energy Conversion and Management, 124, 219–230. http://dx.doi.org/10.1016/j.enconman.2016.07.030
Giannakas, A. & Pissanou, M. (2018). Chitosan/bentonite nanoparticles for waste water treatment: A review. Science Forecast Journal of Nanochemistry and Nanotechnology, 1(1), 1–18.
Liu, Q., Yang, B., Zhang, L., & Huang, R. (2015). Adsorption of an anionic azo dye by cross-linked chitosan/bentonite composite. International Journal of Biological Macromolecules, 72, 1129–1135. https://doi.org/10.1016/j.ijbiomac.2014.10.008
Senol, Z.M. & Simsek, S. (2022). Insights into effective adsorption of lead ions from aqueous solution by using chitosan-bentonite composite beads. Journal of Polymer and Environment. https://doi.org/10.1007/s10924-022-02464-8
Huang, R., Liu, Q., Zhang, L., & Yang, B. (2014). Utilization of cross-linked chitosan/bentonite composite in the removal of methyl orange from aqueous solution. Water Science and Technology, 71(2), 174–182. http://dx.doi.org/10.2166/wst.2014.478
Chen, H., Zheng, K., Zhu, A., Meng, Z., Li, W. & Qin, C. (2020). Preparation of bentonite/chitosan composite for bleaching of deteriorating transformer oil. Polymers, 12 (60). https://doi.org/10.3390/polym12010060
Jia, J., Liu, Y. & Sun, S. (2021). The preparation and characterization of chitosan/bentonite composites for Cr (VI) removal from aqueous solutions, Adsorption Science and Technology, 2021 (6681486), 1–15. https://doi.org/10.1155/2021/6681486
Huang, R., Zhang, L., Hu, P., & Wang, J. (2016). Adsorptive removal of Congo red from aqueous solutions using crosslinked chitosan and crosslinked chitosan immobilized bentonite. International Journal of Biological Macromolecules, 86, 496–504. http://dx.doi.org/10.1016/j.ijbiomac.2016.01.083
Zhang, L., Hu, P., Wang, J., & Huang, R. (2016). Crosslinked quaternized chitosan/bentonite composite for the removal of Amino black 10B from aqueous solutions.International Journal of Biological Macromolecules, 93, 217–225. http://dx.doi.org/10.1016/j.ijbiomac.2016.08.018
Dotto, G.L, Rodrigues, F.K, Tanabe EH, Frohlich, R., Bertuol, D.A., Martins, T.R. & Foletto, E.L. (2016). Development of chitosan/bentonite hybrid composite to remove hazardous anionic and cationic dyes from colored effluents. Journal of Environmental Chemical Engineering. 4, 3230–3239. http://dx.doi.org/10.1016/j.jece.2016.07.004
Ngah, W. W.S., Ariff, N.F.M. & Hanafiah, M.A.K.M (2010). Preparation, Characterization, and Environmental Application of Crosslinked Chitosan-Coated Bentonite for Tartrazine Adsorption from Aqueous Solutions. Water Air Soil Pollution, 206, 225–236. https://doi.org/10.1007/s11270-009-0098-5
Ngah, W. W. S., Ariff, N. F. M., Hashim, A., & Hanafiah, M. A. K. M. (2010). Malachite Green Adsorption onto Chitosan Coated Bentonite Beads: Isotherms, Kinetics and Mechanism. CLEAN - Soil, Air, Water, 38(4), 394–400. https://doi.org/10.1002/clen.200900251
Hariani, P.L, Fatma, F., Riyanti, F. & Ratnasari, H. (2015). Adsorption of Phenol Pollutants from Aqueous Solution Using Ca-Bentonite/Chitosan Composite. Jurnal Manusia Dan Lingkungan, 22(2), 233–239. https://doi.org/10.22146/jml.18747
De Luna, M. D. G., Futalan, C. M., Jurado, C. A., Colades, J. I., & Wan, M.-W. (2017). Removal of ammonium-nitrogen from aqueous solution using chitosan-coated bentonite: Mechanism and effect of operating parameters. Journal of Applied Polymer Science, 135(9), 45924. https://doi.org/10.1002/app.45924
Guo, J., Chen, S., Liu, L., Li, B., Yang, P., Zhang, L., & Feng, Y. (2012). Adsorption of dye from wastewater using chitosan–CTAB modified bentonites. Journal of Colloid and Interface Science, 382(1), 61–66. https://doi.org/10.1016/j.jcis.2012.05.044
Bulut, Y., & Karaer, H. (2014). Removal of methylene blue from aqueous solution by crosslinked chitosan-g-poly(acrylic acid)/bentonite composite. Chemical Engineering Communications, 202(12), 1635–1644. http://dx.doi.org/10.1080/00986445.2014.968713
Huang, R., Zheng, D., Yang, B., Wang, B., & Zhang, Z. (2011). Preparation and characterization of CTAB- HACC bentonite and its ability to adsorb phenol from aqueous solution. Water Science and Technology, 64(1), 286–292. https://doi.org/10.2166/wst.2011.582
Shakib, F., Dadvand Koohi, A., & Kamran Pirzaman, A. (2017). Adsorption of methylene blue by using novel chitosan-g-itaconic acid/bentonite nanocomposite – equilibrium and kinetic study. Water Science and Technology, 75(8), 1932–1943. https://doi.org/10.2166/wst.2017.077
Wang, J., Liu, Y., Hu, P., & Huang, R. (2017). Adsorption of phosphate from aqueous solution by Zr(IV)-crosslinked quaternized chitosan/bentonite composite. Environmental Progress & Sustainable Energy, 37(1), 267–275. https://doi.org/10.1002/ep.12667
Osman, N.H., Mazu, N.N., Chyi, J.L.Y., Ramli, M.M., Majid, M.A.H.M.A.M. & Mazlan, H.I. (2021). Chitosan-bentonite crosslinked film as indicator for copper (II) ions adsorption. European Physical Journal –Applied Physics, 95(1), 10401–10408. https://doi.org/10.1051/epjap/2021210089
Huang, C., Huang, Y., Xie, T., Yu, W., & Ai, S. (2021). Adsorption mechanism of bentonite with dispersed chitosan for cadmium ions. Chemical Engineering & Technology, 44(3), 441–448. https://doi.org/10.1002/ceat.202000505
Xu, X., Cheng, Y., Wu, X., Fan, P. & Scry, R. (2020). Lab (III)-bentonite/chitosan composite: A new type of adsorbent for rapid removal of phosphate from water bodies. Applied Clay Science, 190(105547), 1–9. https://doi.org/10.1016/j.clay.2020.105547
Lin, Z., Yang, Y., Liang, Z., Zeng, L., & Zhang, A. (2021). Preparation of chitosan/calcium alginate/bentonite composite hydrogel and its heavy metal adsorption properties, Polymers, 13(1891), 1–19. https://doi.org/10.3390/polym13111891
Aydar, A.Y. (2018). Utilization of response surface methodology, optimization of extraction of plant materials. Intech Open, 10, 2–15. https://doi.org/10.5772/intechopen.73690
Biswas, S., Bal, M., Behera, S.K., Sen, T.K., & Meikap, B.C. (2019). Process optimization study of Zn+2 adsorption on biochar-alginate composite adsorbent by response surface methodology (RSM). Water, 11(2), 325. https://doi.org/10.3390/w11020325
Rao, J.H., King, P., & Kumar, Y.P. (2018). Application of response surface methodology for optimization of cadmium adsorption in an aqueous solution by activated carbon prepared from Bauhinia Purpurea leaves. Rasayan Journal of Chemistry, 11(4), 1577–1586. http://dx.doi.org/10.31788/RJC.2018.1144024
Ani, J.U., Okoro, U.C., Aneke, L.E., Onukwuli, O.D., Obi, I.O., Akpomie, K.G., & Ofomatah, A.C. (2019). Application of response surface methodology for optimization of dissolved solids adsorption by activated coal. Applied Water Science, 9, 1–11. https://doi.org/10.1007/s13201-0.19-0943-7
Yu, A., Liu, Y., Li, X., Yang, Y., Zhou, Z., & Liu, H. (2021). Modeling and optimization of NH4+ removal from storm water by coal-based granular activated carbon using RSM and ANN coupled with GA. Water, 13(5), 2–22. https://doi.org/10.3390/w13050608
Sarkar, J., Prottoy, Z.H., Bari, M.T., & Al Faraque, M.A. (2021). Comparison of ANFIS and ANN modelling for predicting the water absorption behaviour of polyurethane treated polyester fabric. Heliyon, 7(9), 1–9. https://doi.org/10.1016/j.heliyon.2021.e08000
Okwu, M.O., & Adetunji, O. (2018). A comparative study of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution systems with deterministic inputs. International Journal of Engineering Business Management, 10(12), 1–17. https://doi.org/10.1177/1847979018768421
Taher, A.S., Zhou, J., Shen, X., Yin, Y., & Ji, X. (2019). Comparison of RSM with ANFIS in predicting tensile strength of dissimilar friction stir welded AA2024-AA5083 aluminum alloys. Procedia Manufacturing, 37, 555–562. https://doi.org/10.1016/j.promfg.2019.12.088
Shivakoti, I., Rodrigues, L.L.R., Cep, R., Pradhan, P.M., & Sharma, A. (2020). Experimental Investigation and ANFIS-based modelling during machining of EN31 alloy steel. Materials, 13(14), 1–15. https://doi.org/10.3390/ma13143137
Mishra, R., Prasad, S.R., & Kumar, S. (2021). ANFIS model to predict effect of tool pin length and position on tensile strength of friction stir welded joint. Welding International, 1-10. https://doi.org/10.1080/09507116.2021.1917972
Chainarong, S., Srichok, T., Pitakaso, R., Sirirak, W., Khonjun, S., & Akararungruangku, R. (2021). Variable neighborhood strategy adaptive search for optimal parameters of SSM-ADC 12 aluminum friction stir welding. Processes, 9(10), 1–24.
Mokarram, M., Amin, H., & Khosravi, M.R. (2019). Using adaptive neuro‐fuzzy inference system and multiple linear regression to estimate orange taste. Food Science & Nutrition, 7(10), 3176–3184. https://doi.org/10.1002/fsn3.1149
Ghaedi, M., Hosaininia, R., Ghaedi, A.M., Vafaei, A., & Taghizadeh F. (2014). Adaptive neuro-fuzzy inference system model for adsorption of 1,3,4-thiadiazole-2,5-dithiol onto gold nanoparticles-activated carbon. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 131, 606–614. https://doi.org/10.1016/j.saa.2014.03.055
Mandal, S., Mahapatra, S., & Patel, R.K. (2015). Neuro fuzzy approach for arsenic (III) and chromium (VI) removal from water. Journal of Water Process Engineering, 5, 58–75. https://doi.org/10.1016/j.jwpe.2015.01.002
Rebouh, S., Bouhedda, M., & Hanini, S. (2015). Neuro-fuzzy modeling of Cu(II) and Cr(VI) adsorption from aqueous solution by wheat straw. Desalination and Water Treatment, 57, 6515–6530. https://doi.org/10.1080/19443994.2015.1009171
Chittoo, B.S., & Sutherland, C. (2019). Adsorption using lime-iron sludge–encapsulated calcium alginate beads for phosphate recovery with ANN- and RSM-optimized encapsulation. Journal of Environmental Engineering, 145(5), 04019019. https://doi.org/10.1061/(asce)ee.1943-7870.0001519
Olafadehan, O.A., Bello, V.E., & Amoo, K.O. (2022). Production and characterization of composite nanoparticles derived from chitosan, CTAB and bentonite clay. Chemical Papers. https://doi.org/10.1007/s11696-022-02228-7
Saini, S., Chawla, J., Kumar, R., & Kaur, I. (2019). Response surface methodology (RSM) for optimization of cadmium ions adsorption using C16-6-16 incorporated mesoporous MCM-41. SN Applied Sciences, 1(8), 894. https://doi.org/10.1007/s42452-019-0922-5
Mourabet, M., El Rhilassi, A., Ziatni, M.B., & Taitai, A. (2014). Comparative study of artificial neural network and response surface methodology for modelling and optimization the adsorption capacity of fluoride onto apatitic tricalcium phosphate. Universal Journal of Applied Mathematics, 2(2), 84–91. https://doi.org/10.13189/ujam.2014.020202
Bayuo, J., Abukari, M.A., & Pelig-Ba, K.B. (2020). Optimization using central composite design (CCD) of response surface methodology (RSM) for biosorption of hexavalent chromium from aqueous media. Applied Water Science, 10(135), 1–12. https://doi.org/10.1007/s13201-020-01213-3
Gaitonde, V.N., Karnik, S.R., Achyutha, B.T. & Siddeswarappa, B. (2005). GA applications to RSM based models for burr size reduction in drilling, Journal of Scientific and Industrial Research, 64(1), 347–353.
Bello, V.E., & 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
Baghban, A., & Ebadi, T. (2019). GA-ANFIS modeling of higher heating value wastes: application to fuel upgrading. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 41(1), 7–13. https://doi.org/10.1080/15567036.2017.1344746
Al-hmouz, A., Shen, J., Al-hmouz, R., & Yang, J. (2012). Modeling and simulation of an adaptive neuro fuzzy inference system (ANFIS) for mobile learning. IEEE Transactions on Learning Technologies, 5(3), 226–237. https://doi.org/10.1109/TLT.2011.36
Calp, M.H. (2019). A hybrid ANFIS-GA approach for estimation of regional rainfall amount. Gazi University Journal of Science, 32, 142–162. e-ISSN: 2147-1762
Buragohain, M. (2009). Adaptive network based fuzzy inference system (ANFIS) as a tool for system identification with special emphasis on training data minimization, PhD Thesis, Department of Electronics and Communication Engineering, Indian Institute of Technology, Guwahati.
Abdulshahed, A.M., Longstaff, A.P., & Fletcher, S. (2015). The application of ANFIS prediction models for thermal error compensation on CNC machine tools. Applied Soft Computing, 27, 158–168. http://dx.doi.org/10.1016/j.asoc.2014.11.012
Mohadesi, M., & Aghel, B. (2020). Use of ANFIS/genetic algorithm and neural network to predict inorganic indicators of water quality. Journal of Chemical and Petroleum Engineering, 54, 155–164. https://doi.org/10.22059/jchpe.2020.264471.1244
Kumar, R., & Hynes, N.R.J. (2020). Prediction and optimization of surface roughness in thermal drilling using integrated ANFIS and GA approach. Engineering Science and Technology, An International Journal, 23(1), 30–41. https://doi.org/10.1016/j.jestch.2019.04.011
Biu, D.T., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V.P., Chapi, K., Shirzadi, A., Panahi, S., Chen, W., & Ahmad, B.B. (2018). New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling. Water, 10(9), 1210. https://doi.org/10.3390/w10091210
El-Hasnony, I.M., Barakat, S.I., & Mostafa, R.R. (2020). Optimized ANFIS model using hybrid metaheuristic algorithm for Parkinson’s disease prediction in IoT environment. IEEE Access, 8, 119252–119270. https://doi.org/10.1109/access.2020.3005614
Safihulla, M.A. (2019). Modeling of optimized neuro-fuzzy logic based active vibration control method for automotive suspension, M. Sc. Thesis, Grand Valley State University, Michigan, USA.
Khoshnevisan, B., Rafiee, S., Omid, M., & Mousazadeh, H. (2014). Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Information Processing in Agriculture, 1(1), 1–9. http://dx.doi.org/10.1016/j.inpa.2014.04.001
Bello, E.V., & Olafadehan, O.A. (2022). Evaluation of heterocyclic aromatic compound dye (methylene blue) on chitosan adsorbent sourced from African snail shell: Modelling and optimization studies. Journal of Applied Science and Process Engineering, 9 (1), 1054-1090. https://doi.org/10.33736/jaspe.4464.2022
Onu, C.E., Igbokwe, K.P., Nwabame, T.J., Nwajinka, C.O., & Ohale, P.E. (2020). Evaluation of optimization techniques in predicting optimum moisture content reduction in drying potatoes slices. Artificial Intelligence in Agriculture, 4, 39–47. https://doi.org/10.1016/j.aiia.2020.04.001
Bello, V.E., & 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
Olafadehan, O.A. (2021). Fundamentals of Adsorption Processes, LAP Lambert Academic Publishing, OmniScriptum DUE GmbH.
Silva, G.N., Tomaz, R.S., Sant’anna, I.C., Carneiro, V.Q., Cruz, C.D., & Nascimento, M. (2016). Evaluation of the efficiency of artificial neural networks for genetic value prediction. Genetic Molecular Research, 15(1):1–11. https://doi.org/10.4328/gmr.15017676
Yaseen, Z.M., Ebtehaj, I., Kim, S., Sanikham, H., Asadi, H., Ghareb, M.I., Bonakdari, H., Montar, W.H.M.W., Al-Ansari, N., & Shahid, S. (2019). Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water, 11(3), 1–23. https://doi.org/10.3390/w11030502
Shahbeig, H., Bagheri, N., Ghorbanian, S.A., Hallajisani, A., & Poorkarimi, S. (2013). A new adsorption isotherm model of aqueous solutions on granular activated carbon. World Journal of Modelling and Simulation, 9(4), 243–254.
Sabreena, A.H.N., Azma, Y.N., & Mohamad, O. (2016). Response surface methodology for optimization of parameters for extraction of stevia rebaudiana using water, H2O. IIOABJ, 7(1), 459–466.
Zhou, Q., Ding, L., Zhu, Y., Zhong, M., & Yang, C. (2020). Process parameters optimization of gallic acid removal from water by MIEX resin based on response surface methodology. Processes, 8(3), 1–11. https://doi.org/10.3390/pr8030273
Kalavathy, M.H., Regupathi, I., Pillai, M.G., & Miranda, L.R. (2009). Modelling, analysis and optimization of adsorption parameters for H3PO4 activated rubber and saw dust using response surface methodology (RSM). Colloids and Surfaces B: Biointerfaces, 70(1), 35–45. https://doi.org/10.1016/j.colsurfb.2008.12.007
Chittoo, B.S., & Sutherland, C. (2019). Column breakthrough studies for the removal and recovery of phosphate by lime-iron sludge: Modelling 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.2020.02.022
Trinh, T.K., & Kang, L.S. (2010). Application of response surface method as an experimental design to optimize coagulation tests. Environmental Engineering Research, 15(2), 063–070. https://doi.org/10.4491/eer.2010.15.2.063
Supeni, E.E., Epaarachchi, J.A., Islam, M.M., & Lau, K.T. (2014). Development of artificial neural network model in predicting performance of the smart wind turbine blade. Journal of Mechanical Engineering and Sciences, 6, 734–745. http://dx.doi.org/10.15282/jmes.6.2014.1.0071
Igwe, J.E., & Agu, C.S. (2016). Improvement of waste plastic plant from crude oil recovery. American Journal of Engineering Research, 5(8), 98–104. e-ISSN: 2320-0847
Elbaz, K., Shen, S.L., Zhou, A., Yuan, D.J., and Xu, Y. (2019). Optimization of EPB shield performance with adaptive neuro-fuzzy inference system and genetic algorithm. Applied Science, 9(4), 2–17. https://doi.org/10.3390/app9040780
Liu, P., Leng, W., & Fang, W. (2013). Training ANFIS model with an improved quantum-behaved particle swarm optimization algorithm. Mathematical Problem in Engineering, 2013(1), 1–7. https://doi.org/10.1155/2013/595639
Kumar, R.V., Moorthy, I.G., & Pugazhenthi, G. (2015). Modeling and optimization of critical parameters by hybrid RSM-GA for the separation of BSA using tubular configured MFI-type zeolite microfiltration membrane. RSC Advances, 5(106), 1–44. https://doi.org/10.1039/C5RA20114D
Kalathinga, M.S.H., Basak, S., & Mitra, J. (2019). Artificial neural network modelling and genetic algorithm optimization of process parameters in fluidized bed drying of green tea leaves. Journal of Food Process Engineering, 43(1), 1–7. https://doi.org/10.1111/jfpe.13128w11030502
Downloads
Published
How to Cite
Issue
Section
License
Copyright Transfer Statement for Journal
1) In signing this statement, the author(s) grant UNIMAS Publisher an exclusive license to publish their original research papers. The author(s) also grant UNIMAS Publisher permission to reproduce, recreate, translate, extract or summarize, and to distribute and display in any forms, formats, and media. The author(s) can reuse their papers in their future printed work without first requiring permission from UNIMAS Publisher, provided that the author(s) acknowledge and reference publication in the Journal.
2) For open access articles, the author(s) agree that their articles published under UNIMAS Publisher are distributed under the terms of the CC-BY-NC-SA (Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License) which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original work of the author(s) is properly cited.
3) For subscription articles, the author(s) agree that UNIMAS Publisher holds copyright, or an exclusive license to publish. Readers or users may view, download, print, and copy the content, for academic purposes, subject to the following conditions of use: (a) any reuse of materials is subject to permission from UNIMAS Publisher; (b) archived materials may only be used for academic research; (c) archived materials may not be used for commercial purposes, which include but not limited to monetary compensation by means of sale, resale, license, transfer of copyright, loan, etc.; and (d) archived materials may not be re-published in any part, either in print or online.
4) The author(s) is/are responsible to ensure his or her or their submitted work is original and does not infringe any existing copyright, trademark, patent, statutory right, or propriety right of others. Corresponding author(s) has (have) obtained permission from all co-authors prior to submission to the journal. Upon submission of the manuscript, the author(s) agree that no similar work has been or will be submitted or published elsewhere in any language. If submitted manuscript includes materials from others, the authors have obtained the permission from the copyright owners.
5) In signing this statement, the author(s) declare(s) that the researches in which they have conducted are in compliance with the current laws of the respective country and UNIMAS Journal Publication Ethics Policy. Any experimentation or research involving human or the use of animal samples must obtain approval from Human or Animal Ethics Committee in their respective institutions. The author(s) agree and understand that UNIMAS Publisher is not responsible for any compensational claims or failure caused by the author(s) in fulfilling the above-mentioned requirements. The author(s) must accept the responsibility for releasing their materials upon request by Chief Editor or UNIMAS Publisher.
6) The author(s) should have participated sufficiently in the work and ensured the appropriateness of the content of the article. The author(s) should also agree that he or she has no commercial attachments (e.g. patent or license arrangement, equity interest, consultancies, etc.) that might pose any conflict of interest with the submitted manuscript. The author(s) also agree to make any relevant materials and data available upon request by the editor or UNIMAS Publisher.
To download Copyright Transfer Statement for Journal, click here
