Enhancing sustainability in sewage treatment: A least squares support vector regression-based modeling approach for optimizing regeneration conditions of iFeCu DOI Creative Commons
Mieow Kee Chan, Wan Sieng Yeo, Joyce Chen Yen Ngu

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 64, P. 105694 - 105694

Published: July 1, 2024

Nanoparticles in wastewater treatment offer sustainable, efficient removal of persistent pollutants, with minimal resource usage and reusability. This study examines the reusability immobilized iron‑copper nanoparticles (iFeCu) for sewage treatment. The effect regeneration condition iFeCu including time temperature on performance was investigated. Moreover, a soft sensor model, namely principal component analysis discrimination ensemble least squares support vector regression (PCA-DA-E-LSSVR) model developed to predict iFeCu, an online studied evaluate its accuracy. Investigating conditions like temperature, it found that regenerated 1 mol (M) sodium carbonate (Na2CO3) at 40 °C 5 h performed comparably 60 15 h. Higher temperatures (>40 °C) decreased carbon dioxide monoxide emissions but reduced ammonia (NH3) by ∼22 %. Shorter times (<5 h) Na2CO3-iFeCu interaction, leading ∼11 % NH3 reduction. correlation coefficient, R2 value 0.8514 obtained proposed PCA-DA-E-LSSVR model. Overall, well-fits nonlinear data provides acceptable ranges results.

Language: Английский

Enhancing sustainability in sewage treatment: A least squares support vector regression-based modeling approach for optimizing regeneration conditions of iFeCu DOI Creative Commons
Mieow Kee Chan, Wan Sieng Yeo, Joyce Chen Yen Ngu

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 64, P. 105694 - 105694

Published: July 1, 2024

Nanoparticles in wastewater treatment offer sustainable, efficient removal of persistent pollutants, with minimal resource usage and reusability. This study examines the reusability immobilized iron‑copper nanoparticles (iFeCu) for sewage treatment. The effect regeneration condition iFeCu including time temperature on performance was investigated. Moreover, a soft sensor model, namely principal component analysis discrimination ensemble least squares support vector regression (PCA-DA-E-LSSVR) model developed to predict iFeCu, an online studied evaluate its accuracy. Investigating conditions like temperature, it found that regenerated 1 mol (M) sodium carbonate (Na2CO3) at 40 °C 5 h performed comparably 60 15 h. Higher temperatures (>40 °C) decreased carbon dioxide monoxide emissions but reduced ammonia (NH3) by ∼22 %. Shorter times (<5 h) Na2CO3-iFeCu interaction, leading ∼11 % NH3 reduction. correlation coefficient, R2 value 0.8514 obtained proposed PCA-DA-E-LSSVR model. Overall, well-fits nonlinear data provides acceptable ranges results.

Language: Английский

Citations

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