Modelling Caffeine and Paracetamol Removal from Synthetic Wastewater Using Nanofiltration Membranes: A Comparative Study of Artificial Neural Networks and Response Surface Methodology
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25310%2F25%3A39923014" target="_blank" >RIV/00216275:25310/25:39923014 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3390/membranes15080222" target="_blank" >https://doi.org/10.3390/membranes15080222</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/membranes15080222" target="_blank" >10.3390/membranes15080222</a>
Alternative languages
Result language
angličtina
Original language name
Modelling Caffeine and Paracetamol Removal from Synthetic Wastewater Using Nanofiltration Membranes: A Comparative Study of Artificial Neural Networks and Response Surface Methodology
Original language description
The integration of computational intelligence techniques into pharmaceutical wastewater treatment offers promising opportunities to improve process efficiency and minimize operational costs. This study compares the predictive capabilities of Response Surface Methodology (RSM) and Artificial Neural Network (ANN) models in forecasting the rejection efficiencies of caffeine and paracetamol using AFC 40 and AFC 80 nanofiltration (NF) membranes. Experiments were conducted under varying operating conditions, including transmembrane pressure, feed concentration, and flow rate. The predictive performance of both models was evaluated using statistical metrics. While both approaches closely matched the experimental results, the ANN model consistently yielded lower error values and higher R^2 values, indicating superior predictive performance. These findings support the application of ANNs as a robust modelling tool in optimizing NF membrane processes for pharmaceutical removal.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20402 - Chemical process engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Membranes
ISSN
2077-0375
e-ISSN
2077-0375
Volume of the periodical
15
Issue of the periodical within the volume
8
Country of publishing house
CH - SWITZERLAND
Number of pages
19
Pages from-to
222
UT code for WoS article
001557985500001
EID of the result in the Scopus database
2-s2.0-105014267899