Design and modeling of a nanocomposite system for demineralization of sweet whey
Abstract This research investigates the design of a nanofiltration (NF) system based on a nanocomposite membrane containing graphene oxide (GO) for the demineralization of sweet whey and the modeling the NF process. The effects of various process variables, including, transmembrane pressure (TMP), R...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89009-w |
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| author | Mina Rezapour Mohsen Esmaiili Mehdi Mahmoudian Alireza Behrooz Sarand |
| author_facet | Mina Rezapour Mohsen Esmaiili Mehdi Mahmoudian Alireza Behrooz Sarand |
| author_sort | Mina Rezapour |
| collection | DOAJ |
| description | Abstract This research investigates the design of a nanofiltration (NF) system based on a nanocomposite membrane containing graphene oxide (GO) for the demineralization of sweet whey and the modeling the NF process. The effects of various process variables, including, transmembrane pressure (TMP), Reynolds number, feed pH, and temperature, on the rejection of the minerals were surveyed. Consequently, the recovery of whey proteins from industrial whey using fabricated membranes in a cross-flow membrane module was also investigated. Among the input variables, the pH of the whey solution has the greatest effect on membrane flux and salt rejection performance. In the dead-end filtration system, the highest flux was achieved for the GO-modified membrane under laboratory conditions with a pressure of 10 bar and a pH of 6. The dynamic flux behavior of whey output and salt rejection from whey was modeled using convolutional neural network (CNN) machine learning tools. Linear and non-linear correlations demonstrated that the CNN model correlates well with experimental data on dynamic flux (R2–1.00). Overall, this study on dynamic flux and rejection of minerals from whey using CNN modeling can improve optimal conditions for whey demineralization and reduce laboratory testing costs by predicting the results of untested experimental variables. |
| format | Article |
| id | doaj-art-e73ffde72aaa448d8eadb070f23cd2d1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e73ffde72aaa448d8eadb070f23cd2d12025-02-09T12:33:38ZengNature PortfolioScientific Reports2045-23222025-02-0115112110.1038/s41598-025-89009-wDesign and modeling of a nanocomposite system for demineralization of sweet wheyMina Rezapour0Mohsen Esmaiili1Mehdi Mahmoudian2Alireza Behrooz Sarand3Food Science and Technology Department, Agricultural Faculty, Urmia UniversityFood Science and Technology Department, Agricultural Faculty, Urmia UniversityNanotechnology Department, Faculty of Chemistry, Urmia UniversityChemical Engineering Department, Urmia University of TechnologyAbstract This research investigates the design of a nanofiltration (NF) system based on a nanocomposite membrane containing graphene oxide (GO) for the demineralization of sweet whey and the modeling the NF process. The effects of various process variables, including, transmembrane pressure (TMP), Reynolds number, feed pH, and temperature, on the rejection of the minerals were surveyed. Consequently, the recovery of whey proteins from industrial whey using fabricated membranes in a cross-flow membrane module was also investigated. Among the input variables, the pH of the whey solution has the greatest effect on membrane flux and salt rejection performance. In the dead-end filtration system, the highest flux was achieved for the GO-modified membrane under laboratory conditions with a pressure of 10 bar and a pH of 6. The dynamic flux behavior of whey output and salt rejection from whey was modeled using convolutional neural network (CNN) machine learning tools. Linear and non-linear correlations demonstrated that the CNN model correlates well with experimental data on dynamic flux (R2–1.00). Overall, this study on dynamic flux and rejection of minerals from whey using CNN modeling can improve optimal conditions for whey demineralization and reduce laboratory testing costs by predicting the results of untested experimental variables.https://doi.org/10.1038/s41598-025-89009-wModelingDemineralizationWheyNanofiltrationGraphene oxideConvolutional neural network |
| spellingShingle | Mina Rezapour Mohsen Esmaiili Mehdi Mahmoudian Alireza Behrooz Sarand Design and modeling of a nanocomposite system for demineralization of sweet whey Scientific Reports Modeling Demineralization Whey Nanofiltration Graphene oxide Convolutional neural network |
| title | Design and modeling of a nanocomposite system for demineralization of sweet whey |
| title_full | Design and modeling of a nanocomposite system for demineralization of sweet whey |
| title_fullStr | Design and modeling of a nanocomposite system for demineralization of sweet whey |
| title_full_unstemmed | Design and modeling of a nanocomposite system for demineralization of sweet whey |
| title_short | Design and modeling of a nanocomposite system for demineralization of sweet whey |
| title_sort | design and modeling of a nanocomposite system for demineralization of sweet whey |
| topic | Modeling Demineralization Whey Nanofiltration Graphene oxide Convolutional neural network |
| url | https://doi.org/10.1038/s41598-025-89009-w |
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