Central composite design (CCD) and artificial neural network-based Levenberg-Marquardt algorithm (ANN-LMA) for the extraction of lanasyn black by cloud point extraction
Article Sidebar
Main Article Content
Abstract
The Lanasyn Black is among the most often used in manufacturing and is challenging to take out during wastewater treatment was acquired in the textile industry. Cloud point extraction was used for their elimination in an aqueous solution. The multivariable process parameters have been independently optimized using Central composite design and Levenberg-Marquardt algorithm-based artificial neural network for the highest yield of the extraction of Lanasyn Black via cloud point extraction. The CCD forecasts the output maximum of 97.01% under slightly altered process parameters. Still, the ANN-LMA model predicts the extraction yield (99.98%) using an amount of KNO3 =1.04 g, beginning pH of solution=8.99, initial of Lanasyn Black 24.57 ppm, and 0.34 W/W of Triton X-100. With coefficients of determination of 0.997 and 0.9777, the most recent empirical verification of the model mentioned above's predictions using CCD and ANN-LMA is determined to be satisfactory.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution license 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
References
D. A. Yaseen, M. Scholz, Int. J. Environ. Sci. Technol. 16 (2019) 1193 (https://doi.org/10.1007/s13762-018-2130-z)
B. K. Nandi, A. Goswami, M. K. Purkait, Appl. Clay. Sci. 42 (2009) 583 (https://doi.org/10.1016/j.clay.2008.03.015)
Krasnoborodko I.G. Destructive wastewater treatment from dyes. L: Chemistry, 1988.
M. Ghaedi, H. Hossainian, M. Montazerozohori, A. Shokrollahi, F. Shojaipour, M. Soylak, M. K. Purkait, Desalination. 281 (2011) 226 (https://doi.org/10.1016/j.desal.2011.07.068)
V. Geissen, H. Mol, E. Klumpp, G. Umlauf, M. Nadal, M. D. Ploeg, S. E. A. T. M. Van de Zee, C. J. Ritsema, Int. Soil Water Conserv. Res. 3 (2015) 57 (https://doi.org/10.1016/j.iswcr.2015.03.002)
N. E.Djebbari, A. Amara; A. Didi; M. A. Didi,. Scientific Study & Research Chemistry & Chemical Engineering, Biotechnology, Food Industry. 23 (2022) 333 (https://pubs.ub.ro/dwnl.php?id=CSCC6202204V04S01A0005)
H. Chandarana, P. Senthil Kumar, M. Srinivasan, M. Anil Kumar, Chemosphere 285 (2021) 131480 (https://doi.org/10.1016/j.chemosphere.2021.131480)
M. K. Dahri, M. R. R. Kooh, L. B.L. Lim, Alex. Eng. J. 54 (2015) 1253 (https://doi.org/10.1016/j.aej.2015.07.005)
E. Brillas, E.Mur, R. Sauleda, L. Sanchez, J. Peral, X. Domenech, J. Casado, Appl. Catal. B: Environ. 16 (1998) 31 (https://doi.org/10.1016/S0926-3373(97)00059-3)
P. Liang, J. Li, X. Yang, Microchim Acta. 152 (2005) 47-51 (https://doi.org/10.1007/s00604-005-0415-7)
D. Snigur, E. A. Azooz, O. Zhukovetska, O. Guzenko, W. Mortada, TrAC, Trends Anal. Chem. 164 (2023) 117113 (https://doi.org/10.1016/j.trac.2023.117113)
R. Halko, I. Hagarová, V. Andruch, J. Chromatogr. A. 1701(2023) 464053 (https://doi.org/10.1016/j.chroma.2023.464053)
H. S. Ferreira, M. A. Bezerra, S.L.C. Ferreira, Microchim Acta. 154 (2006) 163 (https://doi.org/10.1007/s00604-005-0475-8)
W. R. Melchert, F. R. P. Rocha, Rev. Anal. Chem. 35 (2016) 41 (https://doi.org/10.1515/revac-2015-0022)
J. Yongsheng, W. Le, L. Ruihong, W. Haohao, S. Shuhui, C. Mingzhuo, ACS Omega 6 (2021) 13508 (https://doi.org/10.1021/acsomega.1c01768)
M.N. Jones, Int. J. Pharm. 177 (1999) 137 (https://doi.org/10.1016/s0378-5173(98)00345-7)
A. Amara-Rekkab, M.A. Didi, Desalin. Water Treat. 281 (2022) 186 (https://doi.org/10.5004/dwt.2023.29147)
N. Sato, M. Morin, H. Itabashi, Talanta 117 (2013) 376-381 (https://doi.org/10.1016/j.talanta.2013.08.025)
A. Asfaram, M. Ghaedi, A. Goudarzi, M. Rajabi, Dalton Trans.44 (2015) 14707 (https://doi.org/10.1039/C5DT01504A)
G. Hanrahan, K. Lu, Crit. Rev. Anal. Chem. 36 (2006) 141 (https://doi.org/10.1080/10408340600969478)
M. Boulahbal, M. A. Malouki, M. Canle, Z. Redouane-Salah, S. Devanesan, M. S. AlSalhi, M. Berkani, Chemosphere 306 (2022) 135516 (https://doi.org/10.1016/j.chemosphere.2022.135516)
H. Ucbeyiay, Fuel. Process. Technol. 106 (2013) 1 (https://doi.org/10.1016/j.fuproc.2012.09.020)
G. E. B. Box, W.G. Hunter, J.S. Hunter, Statistics for Experimenters, 2 nd edition, Wiley-Interscience, Hoboken, NJ, 1978.
T. Mehmood, A. Ahmed, A. Asif, A. M. Sheeraz, M. A. Sandhu, Food Chem. 253 (2018) 179 (https://doi.org/10.1016/j.foodchem.2018.01.136)
S.I.S. Al-Hawary, K. Azhar, S. A. Sherzod, A.K. Kareem, K. A. Alkhuzai, Romero- R.M. Parra, A. H. Amini, T. Alawsi, M. Abosaooda, M. Dejaverdi, Alex. Eng. J. 74 (2023) 737 (https://doi.org/10.1016/j.aej.2023.05.066)
K. Behera, H. Meena, S. Chakraborty, B.C. Meikap, Int. J. Min. Sci. Technol. 28 (2018) 621 (https://doi.org/10.1016/j.ijmst.2018.04.014)
M. Maleki‑Kakelar A., Aghaeinejad‑Meybodi, S. Sanjideh, M. J. Azarhoosh, Environ. Proc. 9 (2022) 7 (https://doi.org/10.1007/s40710-022-00564-0)
P. Mondal, A. K. Sadhukhan, A. Ganguly, P. Gupta, 3 Biotech 11 (2021) 28 (https://doi.org/10.1007/s13205-020-02553-2)
N. Teslić, N. Bojanić, D. Rakić, A. Takači, Z. Zeković, A. Fišteš, M. Bodroža-Solarov, B. Pavlić, Chem. Eng. Process. 143 (2019) 107634 (https://doi.org/10.1016/j.cep.2019.107634)
B. Jiang, F. Zhang, Y. Sun, X. Zhou, J. Dong, L. Zhang, J. Taiwan Inst. Chem. Eng. 45 (2014) 2217 (https://doi.org/10.1016/j.jtice.2014.03.020)
A. Çelekli, H. Bozkurt, F. Geyik, Bioresour. Technol. 129 (2013) 396 (https://doi.org/10.1016/j.biortech.2012.11.085)
A. Smaali, M. Berkani, F. Merouane, V.T. Le, Y. Vasseghian, N. Rahim, M. Kouachi, Chemosphere 266 (2021) 129158 (https://doi.org/10.1016/j.chemosphere.2020.129158)
K. Oukebdane, R. Semmoud, M.A. Didi, Desalin. Water Treat. 247 (2022) 272 (https://doi.org/10.5004/dwt.2022.28039)
M. Berkani, M. Bouhelassa, M. K. Bouchareb, Arab. J. Chem. 12 (2019) 3054 (https://doi.org/10.1016/j.arabjc.2015.07.004)
D. Bas, I. Boya, J. Food Eng. 78 (2007) 836 (https://doi.org/10.1016/j.jfoodeng.2005.11.024)
M. Pravitha, M. R. Manikantan, V. Ajesh Kumar, S. Beegum, R. Pandiselvam, LWT. 146 (2021) 111441 (https://doi.org/10.1016/j.lwt.2021.111441)
J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, 3rd Ed., Morgan Kaufmann, 2012.
E. Bello, T. Ogedengbe, M. Khumbulani, I. Daniyan, Procedia CIRP 89 (2020) 59 (https://doi.org/10.1016/j.procir.2020.05.119)
R. Pandiselvam, M. R. Manikantan, S. Sunoj, S. Sreejith, S. Beegum, J. Food Process Eng. 42 (2019) e12981 (https://doi.org/10.1111/jfpe.12981)
S. Youssefi, Z. Emam-Djomeh, S. M. Mousavi, Drying Technology 27 (2009) 910 (https://doi.org/10.1080/07373930902988247)
V. Ajesh Kumar, S. Prem Prakash, M. Pravitha, H. Muzaffar, M. Shukadev, V. Prithviraj, V. Deepak Kumar, Food Pack. Shelf Life 31 (2022) 100778 (https://doi.org/10.1016/j.fpsl.2021.100778)
A. J. Sisi, A. Khataee, M. Fathinia, B. Vahid, Y. Orooji, J. Mol. Liq. 316 (2020) 113801 (https://doi.org/10.1016/j.molliq.2020.113801)
Z. Jun, C. Leland, S. Dongyi, W. Y. Zhan, Appl. Energy. 345 (2023) 121373 (https://doi.org/10.1016/j.apenergy.2023.121373).