Adsorption capacity prediction and optimization of electrospun nanofiber membranes for estrogenic hormone removal using machine learning algorithms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28610%2F24%3A63582278" target="_blank" >RIV/70883521:28610/24:63582278 - isvavai.cz</a>

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/10.1002/pat.6638" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/pat.6638</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/pat.6638" target="_blank" >10.1002/pat.6638</a>

Result language

angličtina

Original language name

Adsorption capacity prediction and optimization of electrospun nanofiber membranes for estrogenic hormone removal using machine learning algorithms

Original language description

This study focuses on developing four machine learning (ML) models (Gaussian process regression (GPR), support vector machine (SVM), decision tree (DT), and ensemble learning tree (ELT)) optimized and hyperparameters tuned via genetic algorithm (GA) and particle swarm optimization (PSO) to analyze and predict the adsorption capacity of four estrogenic hormones. These hormones are a serious cause of fish femininity and various forms of cancer in humans. Their adsorption via electrospun nanofibers offers a sustainable and relatively environmentally friendly solution compared to nanoparticle adsorbents, which require secondary treatment. The intricate task is to find the relationship between input parameters to obtain optimum conditions, which requires an efficient ML model. The GPR integrated GA hybrid model performed the most accurate and precise results with R2 = 0.999 and RMSE = 2.4052e−06, followed by ELT (0.9976 and 4.3458e−17), DT (0.9586 and 2.4673e−16), and SVM (0.7110 and 0.0639). The 2D and 3D partial dependence plots showed temperature, dosage, initial concentration, contact time, and pH as vital adsorption parameters. Additionally, Shapley&apos;s analysis further revealed time and dosage as the most sensitive parameters. Finally, a user-friendly graphical user interface (GUI) was developed as a predictor utilizing the optimum hybrid model (GPR-GA), and the results were experimentally validated with a maximum error of &lt; 3.3% for all tests. Thus, the GUI can legitimately work for any desired material with given input conditions to efficiently monitor the removal concentration of all four estrogenic hormones simultaneously at wastewater treatment plants.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10404 - Polymer science

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Polymers for Advanced Technologies

ISSN

1042-7147

e-ISSN

1099-1581

Volume of the periodical

35

Issue of the periodical within the volume

11

Country of publishing house

GB - UNITED KINGDOM

Number of pages

19

Pages from-to

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UT code for WoS article

001369326800001

EID of the result in the Scopus database

2-s2.0-85208607135