Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28610%2F25%3A63595698" target="_blank" >RIV/70883521:28610/25:63595698 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0301479725016597?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0301479725016597?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jenvman.2025.125683" target="_blank" >10.1016/j.jenvman.2025.125683</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation

Popis výsledku v původním jazyce

In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of bandgap, dye concentration, photocatalyst dosage, solution volume, specific surface area, and time duration on photocatalytic degradation rate constant of cationic dyes was discerned using selected ML models, i.e., ensembled learning tree (ELT), gaussian process regression (GPR), support vector machine (SVM), and decision tree (DT). Thus, the data points were sourced from literature studies recently published in 2024 and 2023 on materials related to working on fundamental principles of photocatalysis. The ELT-PSO hybrid model outperformed all models with R2 = 0.992 and RMSE = 2.6408e−04, followed by DT, GPR, and SVM. The partial dependence plots and Shapley&apos;s analysis demonstrate that the type of dye, bandgap, dye initial concentration, and time duration are essential parameters for photocatalytic degradation, while sensitivity analysis further displayed solution volume and time duration to be the most influential parameters for rate constant determination. The optimized ML model&apos;s prediction was also experimentally validated using as-synthesized different compositions of Cu2O/WO3 heterostructures and ZnO nanoparticles. The results suggest that an ML-optimized study can be used in designing photocatalysts with optimum properties desired for the removal of cationic dyes at high rates from wastewater, thus saving energy and cost for a sustainable environment.

Název v anglickém jazyce

Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation

Popis výsledku anglicky

In this study, machine learning (ML) models coupled with genetic algorithm (GA) and particle swarm optimization (PSO) were applied to predict the relative influence of experimental parameters of photocatalytic dye removal. Specifically, the impact of bandgap, dye concentration, photocatalyst dosage, solution volume, specific surface area, and time duration on photocatalytic degradation rate constant of cationic dyes was discerned using selected ML models, i.e., ensembled learning tree (ELT), gaussian process regression (GPR), support vector machine (SVM), and decision tree (DT). Thus, the data points were sourced from literature studies recently published in 2024 and 2023 on materials related to working on fundamental principles of photocatalysis. The ELT-PSO hybrid model outperformed all models with R2 = 0.992 and RMSE = 2.6408e−04, followed by DT, GPR, and SVM. The partial dependence plots and Shapley&apos;s analysis demonstrate that the type of dye, bandgap, dye initial concentration, and time duration are essential parameters for photocatalytic degradation, while sensitivity analysis further displayed solution volume and time duration to be the most influential parameters for rate constant determination. The optimized ML model&apos;s prediction was also experimentally validated using as-synthesized different compositions of Cu2O/WO3 heterostructures and ZnO nanoparticles. The results suggest that an ML-optimized study can be used in designing photocatalysts with optimum properties desired for the removal of cationic dyes at high rates from wastewater, thus saving energy and cost for a sustainable environment.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

<a href="/cs/project/EH23_021%2F0009004" target="_blank" >EH23_021/0009004: Rozvoj aplikačního potenciálu v oblasti polymerních materiálů v kontextu naplňování principů cirkulární ekonomiky (POCEK)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Journal of Environmental Management

ISSN

0301-4797

e-ISSN

1095-8630

Svazek periodika

386

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

nestránkováno

Kód UT WoS článku

001493189500001

EID výsledku v databázi Scopus

2-s2.0-105004874138