Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Machine learning approach for photocatalysis: An experimentally validated case study of photocatalytic dye degradation
Original language description
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'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'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.
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
10511 - Environmental sciences (social aspects to be 5.7)
Result continuities
Project
<a href="/en/project/EH23_021%2F0009004" target="_blank" >EH23_021/0009004: Application potential development in the field of polymer materials in the context of circular economy compliance (POCEK)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<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
Journal of Environmental Management
ISSN
0301-4797
e-ISSN
1095-8630
Volume of the periodical
386
Issue of the periodical within the volume
Neuveden
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
nestránkováno
UT code for WoS article
001493189500001
EID of the result in the Scopus database
2-s2.0-105004874138