MODELING THE OPTIMIZATION PARAMETERS BY MACHINE LEARNING ALGORITHMS TO IMPROVE THE ENVIRONMENT VIA PREDICATE OUTPUT OF A WATER TREATMENT SYSTEM

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28610%2F25%3A63601218" target="_blank" >RIV/70883521:28610/25:63601218 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

MODELING THE OPTIMIZATION PARAMETERS BY MACHINE LEARNING ALGORITHMS TO IMPROVE THE ENVIRONMENT VIA PREDICATE OUTPUT OF A WATER TREATMENT SYSTEM

Popis výsledku v původním jazyce

Removal of chromium (Cr(VI)) via adsorption is a sustainable and green approach to achievingthe Sustainable Development Goals. Algal biomass serves as a reliable solution for adsorbingCr (VI) and achieving considerable removal from wastewater. The process of removal, with itsintricate experimental conditions, time duration, and repetitive iterations, makes adsorption achallenging mechanism to achieve perfection in Cr (VI) removal. For the accurate analysis andeffective prediction of the impact of a specific adsorbent for the removal of Cr (VI) undervarious experimental conditions, the development of a machine-learning (ML) model isnecessary. The novelty of this study resides in the consideration of algae biomass adsorbentsfor the removal of Cr (VI), with various experimental conditions and their prediction via MLintegrated with particle swarm optimization (PSO). The results supported the prediction of anEnsemble Learning Tree (ELT) optimized via PSO (Coefficient of determination (R2) = 0.9972,Root Mean Square Error (RMSE) =1.2499 e-13 for testing). On the other hand, the performanceof the decision tree (DT) was poor (R2 = 0.9719, RMSE = 1.6561 e-14). Partial dependence plots(PDPs) of the input parameters reveal that the adsorbent dosage, time, and initial concentrationare the most effective experimental parameters.

Název v anglickém jazyce

MODELING THE OPTIMIZATION PARAMETERS BY MACHINE LEARNING ALGORITHMS TO IMPROVE THE ENVIRONMENT VIA PREDICATE OUTPUT OF A WATER TREATMENT SYSTEM

Popis výsledku anglicky

Removal of chromium (Cr(VI)) via adsorption is a sustainable and green approach to achievingthe Sustainable Development Goals. Algal biomass serves as a reliable solution for adsorbingCr (VI) and achieving considerable removal from wastewater. The process of removal, with itsintricate experimental conditions, time duration, and repetitive iterations, makes adsorption achallenging mechanism to achieve perfection in Cr (VI) removal. For the accurate analysis andeffective prediction of the impact of a specific adsorbent for the removal of Cr (VI) undervarious experimental conditions, the development of a machine-learning (ML) model isnecessary. The novelty of this study resides in the consideration of algae biomass adsorbentsfor the removal of Cr (VI), with various experimental conditions and their prediction via MLintegrated with particle swarm optimization (PSO). The results supported the prediction of anEnsemble Learning Tree (ELT) optimized via PSO (Coefficient of determination (R2) = 0.9972,Root Mean Square Error (RMSE) =1.2499 e-13 for testing). On the other hand, the performanceof the decision tree (DT) was poor (R2 = 0.9719, RMSE = 1.6561 e-14). Partial dependence plots(PDPs) of the input parameters reveal that the adsorbent dosage, time, and initial concentrationare the most effective experimental parameters.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

20501 - Materials engineering

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