Statistical modeling and optimization of itaconic acid reactive extraction using response surface methodology (RSM) and artificial neural network (ANN)

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dc.authoridDatta, Dipaloy/0000-0002-2048-9064
dc.contributor.authorChellapan, Suchith
dc.contributor.authorDatta, Dipaloy
dc.contributor.authorKumar, Sushil
dc.contributor.authorUslu, Hasan
dc.date.accessioned2024-11-07T13:34:30Z
dc.date.available2024-11-07T13:34:30Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractIn this paper, regression models were proposed to predict the degrees of extraction (%Y) for the reactive extraction of itaconic acid using response surface methodology (RSM) and artificial neural network (ANN). The prominent design parameters like itaconic acid concentration, extractant (tri-n-octylamine), and modifier (dichloromethane, an active diluent) composition were considered, and their impact on the extraction efficiency was determined. RSM and ANN fitted the experimental data with a correlation coefficient of 0.970 and 0.993, respectively. The statistical significance of the models (RSM and ANN) was ascertained by ANOVA analysis. The optimal design factors were determined to be 0.072 mol center dot L-1 acid concentration, 16.075 %v/v extractant composition, and 62.15 %v/v modifier composition at which the values of experimental and predicted %Y of 98.86% and 100.69%, respectively, were obtained by RSM model.
dc.identifier.doi10.1016/j.cdc.2021.100806
dc.identifier.issn2405-8300
dc.identifier.scopus2-s2.0-85120694270
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1016/j.cdc.2021.100806
dc.identifier.urihttps://hdl.handle.net/11480/16022
dc.identifier.volume37
dc.identifier.wosWOS:001221818100002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofChemical Data Collections
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectArtificial neural network
dc.subjectItaconic acid
dc.subjectModifier
dc.subjectReactive extraction
dc.subjectResponse surface methodology
dc.titleStatistical modeling and optimization of itaconic acid reactive extraction using response surface methodology (RSM) and artificial neural network (ANN)
dc.typeData Paper

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