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Machine learning prediction of dye adsorption by hydrochar: Parameter optimization and experimental validation.
Liu, Chong; Balasubramanian, Paramasivan; Li, Fayong; Huang, Haiming.
Afiliación
  • Liu C; School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China; Department of Chemical & Materials Engineering, University of Auckland, 0926, New Zealand.
  • Balasubramanian P; Department of Biotechnology & Medical Engineering, National Institute of Technology Rourkela, 769008, India.
  • Li F; College of Water Resources and Architectural Engineering, Tarim University, Xinjiang 843300, China.
  • Huang H; School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, China. Electronic address: huanghaiming52hu@163.com.
J Hazard Mater ; 480: 135853, 2024 Sep 16.
Article en En | MEDLINE | ID: mdl-39288523
ABSTRACT
In response to escalating global wastewater issues, particularly from dye contaminants, many studies have begun using hydrochar to adsorb dye from wastewater. However, the relationship between the preparation conditions of hydrochar, the properties of hydrochar, experimental conditions, types of dyes, and equilibrium adsorption capacity (Q) has not yet been fully explored. This study conducted a comprehensive assessment using twelve distinct ML models. The Gradient Boosting Regressor (GBR) model exhibited superior performance with R² (0.9629) and RMSE (0.1166) in the test dataset, marking it as the most effective among the evaluated models. Moreover, this study also proved the feasibility of the GBR model through stability testing and residual analysis. A feature importance analysis prioritized the variables as follows experimental conditions (41.5 %), properties of hydrochar (26.0 %), preparation conditions (18.1 %), and type of dye (14.4 %). Meanwhile, experimental conditions (C0 > 30 mmol/g, pH > 8, and higher solvent temperatures) and hydrochar properties (the BET surface area > 2000 m²/g, an (O+N)/C molar ratio < 0.6, and an H/C molar ratio of approximately 0.06) show higher Q for dyes. Experimental validation of the GBR model confirmed its practical utility with a suitable predictive accuracy (R² = 0.8704). Moreover, the study developed a Python-based GUI that has integrated the best GBR models to facilitate researchers' ongoing application and improvement of this predictive model. This study not only underscores the efficacy of ML in enhancing the understanding of dye adsorption by hydrochar but also sets a precedent for future research on sustainable contaminants removal through bio-based adsorbents.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Hazard Mater Asunto de la revista: SAUDE AMBIENTAL Año: 2024 Tipo del documento: Article País de afiliación: Nueva Zelanda Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Hazard Mater Asunto de la revista: SAUDE AMBIENTAL Año: 2024 Tipo del documento: Article País de afiliación: Nueva Zelanda Pais de publicación: Países Bajos