Simultaneous adsorption-photocatalytic degradation of tetracycline by CdS/TiO2 nanosheets/graphene nanocomposites: Experimental study and modeling

Firouzi, Fatemeh; Pirbazari, Azadeh Ebrahimian; Saraei, Fatemeh Esmaeili Khalil; Tabatabai‐Yazdi, Fatemeh‐Sadat; Esmaeili, Amin; Khodaee, Ziba

doi:10.1016/j.jece.2021.106795

Cited by 9 publications

(1 citation statement)

References 47 publications

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“…Scientific studies have applied ML models to wastewater datasets to predict water quality, assess environmental impact, and evaluate treatment performance 57 – 62 . However, these studies often need more substantial evidence regarding the suitability of the chosen ML algorithm and utilize limited datasets, typically with few input variables.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-powered estimation of malachite green photocatalytic degradation with NML-BiFeO3 composites

Salahshoori,

Yazdanbakhsh,

Baghban

2024

Sci Rep

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This study explores the potential of photocatalytic degradation using novel NML-BiFeO3 (noble metal-incorporated bismuth ferrite) compounds for eliminating malachite green (MG) dye from wastewater. The effectiveness of various Gaussian process regression (GPR) models in predicting MG degradation is investigated. Four GPR models (Matern, Exponential, Squared Exponential, and Rational Quadratic) were employed to analyze a dataset of 1200 observations encompassing various experimental conditions. The models have considered ten input variables, including catalyst properties, solution characteristics, and operational parameters. The Exponential kernel-based GPR model achieved the best performance, with a near-perfect R2 value of 1.0, indicating exceptional accuracy in predicting MG degradation. Sensitivity analysis revealed process time as the most critical factor influencing MG degradation, followed by pore volume, catalyst loading, light intensity, catalyst type, pH, anion type, surface area, and humic acid concentration. This highlights the complex interplay between these factors in the degradation process. The reliability of the models was confirmed by outlier detection using William’s plot, demonstrating a minimal number of outliers (66–71 data points depending on the model). This indicates the robustness of the data utilized for model development. This study suggests that NML-BiFeO3 composites hold promise for wastewater treatment and that GPR models, particularly Matern-GPR, offer a powerful tool for predicting MG degradation. Identifying fundamental catalyst properties can expedite the application of NML-BiFeO3, leading to optimized wastewater treatment processes. Overall, this study provides valuable insights into using NML-BiFeO3 compounds and machine learning for efficient MG removal from wastewater.

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Section: Introductionmentioning

confidence: 99%