Publikace detail

OPTIMIZING NANOFILTRATION FOR SUSTAINABLE PHARMACEUTICAL WASTEWATER TREATMENT USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS.

Autoři: Ezeogu Nkechi | Cuhorka Jiří | Mikulášek Petr

Rok: 2025

Druh publikace: ostatní - článek ve sborníku

Název zdroje: Workshop of Student's Presentation 2025 : book of abstracts

Název nakladatele: Czech Membrane Platform

Místo vydání: Česká Lípa

Strana od-do: nestránkováno

Tituly:

Jazyk	Název	Abstrakt	Klíčová slova
eng	OPTIMIZING NANOFILTRATION FOR SUSTAINABLE PHARMACEUTICAL WASTEWATER TREATMENT USING RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORKS.	This study presents a comparative modelling approach for predicting pharmaceutical rejection from synthetic wastewater using thin-film composite nanofiltration membranes (AFC 40 and AFC 80). Three key process parameters, transmembrane pressure (10–30 bar), feed flow rate (5–15 L/min), and feed concentration (5–20 mg/L) were evaluated to optimize system performance. Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were applied to model and predict solute rejection behaviour. The findings highlight the potential of machine learning-based predictive tools in optimizing nanofiltration processes for pharmaceutical removal, offering a robust framework for advancing sustainable wastewater treatment strategies.	OPTIMIZING; NANOFILTRATION; PHARMACEUTICAL; WASTEWATER; RESPONSE SURFACE METHODOLOGY; ARTIFICIAL NEURAL NETWORKS.

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