A particle scale micro-CT approach for 3D in-situ visualizing the Pb (II) adsorption in different crop residue-derived chars

Zhang, Hehu; Li, Yanfei; Xie, Ruyue; Zhu, Ying; Shi, Suan; Yang, Zengling; Han, Lujia

doi:10.1016/j.biortech.2021.126269

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…For machine learning, error is the difference between the predicted value of the model and the true value of the sample data. According to the difference in sample data, of biochar; c Some milestone events in machine learning for optimizing pyrolysis process and modeling adsorption process of biochar for pollutant removal (Cao et al 2016;Chowdhury et al 2019;Hai et al 2021;Li et al 2022a;Li et al 2022c;Palansooriya et al 2022;Zhang et al 2022a) error is divided into training error and test error. Among them, training error is called empirical error and testing error is called generalization error (Tanaka 2015).…”

Section: Evaluation Metrics For Machine Learningmentioning

confidence: 99%

“…The adsorption mechanism can be intuitively understood by giving the distribution image of pollutants in biochar particles. A 3D in situ visualization method was established to qualitatively and semi quantitatively characterize Pb (II) adsorption distribution in biochar particles (Zhang et al 2022a). Specifically, the biochar and the biochar adsorbed Pb (II) were imaged by X-ray micro CT (Additional file 1: Fig.…”

Section: Revelation Of Adsorption Mechanismmentioning

confidence: 99%

“…Fig. 1 a Number of publications and citations of machine learning for synthesis (Left) and pollutant adsorption (Right) of biochar between 2014 and 2021 in Web of Science Core Collection; b The frequency of keywords in machine learning for synthesis (Left) and pollutant adsorption (Right)of biochar; c Some milestone events in machine learning for optimizing pyrolysis process and modeling adsorption process of biochar for pollutant removal(Cao et al 2016;Chowdhury et al 2019;Hai et al 2021;Li et al 2022a; Li et al 2022c;Palansooriya et al 2022;Zhang et al 2022a) …”

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confidence: 99%

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Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

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Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

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Section: Evaluation Metrics For Machine Learningmentioning

confidence: 99%

Section: Revelation Of Adsorption Mechanismmentioning

confidence: 99%

mentioning

confidence: 99%

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Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

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“…Therefore, the selection of environmentally friendly adsorbents such as biochar from biowastes under anoxic or anaerobic conditions is conducive to decreasing global carbon emissions and realizing sustainable environmental development (Su et al 2021;Zhu et al 2022). Crop straws are rich in lignocellulose, cellulose, and nutrients, making them one of the most common and easily available agricultural biomass wastes in the world (Zhang et al 2022). The amount of straws is at a billion tons per year in China.…”

Section: Introductionmentioning

confidence: 99%

Enhanced adsorption of hydroquinone by transition metals and heteroatom-codoped biochar: Preparation, performance, and mechanism

xiaolu,

Tian,

Lei

et al. 2024

Preprint

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The adsorption capacity of pristine biochar for hydroquinone solutions was limited, while modified biochar could significantly enhance adsorption performance. Modified biochar BCNP-Fe, BCHNP-Fe, and BCP-Fe were produced by mixing corn stover with the solutions of ammonium phosphate and ferrous sulfate, the solutions of diammonium hydrogen phosphate and ferrous sulfate, and the solutions of phosphoric acid and ferrous sulfate for removing hydroquinone solutions. The maximum adsorption capacities of BCNP-Fe, BCHNP-Fe, and BCP-Fe were 154.321, 203.666, and 142.045 mg/g, respectively, which were 1.09, 1.44 and 1.00 folds of BC. The adsorption kinetics and isotherm studies of the modified biochars probably involved physical and chemical adsorption. Thermodynamic studies further suggested that higher temperature was favorable for adsorption, and adsorption exhibited endothermic behavior. The adsorption mechanism of hydroquinone on BCNP-Fe, BCHNP-Fe, and BCP-Fe included the π-π interactions, electron donor-acceptor, hydrogen bonding, pore filling, hydrophilicity, and surface complexation. BCNP-Fe, BCHNP-Fe, and BCP-Fe were promising adsorbents for removing hydroquinone solutions as well as promising candidates for water treatment due to their simple preparation and high adsorption capacity.

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A Micro-CT Approach for 3D In-Situ Visualizing the Cu (II) Adsorption in Corn Cob Biochar

Xiong

Han

Zhang

et al. 2022

Water Air Soil Pollut

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A particle scale micro-CT approach for 3D in-situ visualizing the Pb (II) adsorption in different crop residue-derived chars

Cited by 10 publications

References 27 publications

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Enhanced adsorption of hydroquinone by transition metals and heteroatom-codoped biochar: Preparation, performance, and mechanism

A Micro-CT Approach for 3D In-Situ Visualizing the Cu (II) Adsorption in Corn Cob Biochar

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