Correlations and prediction of adsorption capacity and affinity of aromatic compounds on activated carbons

Wu, Wenhao; Miao, Gangfen; Yan, Xinxin; Xing, Baoshan; Yang, Kun

doi:10.1016/j.scitotenv.2019.135457

Cited by 11 publications

(12 citation statements)

References 33 publications

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“…It can be observed that DA model showed a better fitting than Freundlich model, and gives more information for the adsorption process from the related parameters such as saturation adsorption capacity ( Q 0 ) and adsorption affinity ( E ) ( Table S3 and Table S5 ). Adsorption capacity of organic contaminants including nitrobenzene, phenols and anilines by KAC, even with the addition of 50 mg TOC/L FA, can be up to 1100 mg/g ( Table S5 ), which was about 3 times higher than that on other carbon materials reported in the literatures [ 7 , 37 , 38 , 52 ] ( Fig. 4 a ) .…”

Section: Resultsmentioning

confidence: 73%

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Simultaneous adsorption of fulvic acid and organic contaminants by KOH activated mesoporous biochar with large surface area

Hu,

Wu,

Zhou

et al. 2024

Heliyon

Self Cite

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Section: Resultsmentioning

confidence: 73%

“…2 b ) . Because hydrogen bonding interaction plays an important role in adsorption organic contaminants by carbon materials [ 33 , 35 , 37 , 38 ]. α m is an important parameter that can reflect the ability to form hydrogen bonding interaction between organic contaminants and carbon materials.…”

Section: Resultsmentioning

confidence: 99%

Simultaneous adsorption of fulvic acid and organic contaminants by KOH activated mesoporous biochar with large surface area

Hu,

Wu,

Zhou

et al. 2024

Heliyon

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, this approach has been considered for an adsorption study for the first time. In addition, unlike previous works, which commonly implemented traditional predictive models, such as linear regression or correlation analysis for adsorption, , a data-driven model was developed on the basis of the collected data to help forecast the current adsorption system’s output or projected design. This advanced alternative could outperform traditional methods in handling complex data with a nonlinear relationship. − In conclusion, this study aims to provide insights into the adsorption of nutrients on AC and BC through data mining, a bootstrap method, and machine learning by covering four major areas: (1) discovering and summarizing data, (2) correlation analysis, (3) resolving research questions using the bootstrap 95% CI approach, and (4) estimating adsorption capacity by machine learning.…”

Section: Introductionmentioning

confidence: 99%

Insight into the Adsorption of Nutrients from Water by Pyrogenic Carbonaceous Adsorbents Using a Bootstrap Method and Machine Learning

Nguyen

Hằng

et al. 2022

ACS EST Water

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Herein, findings from 98 research papers [52 biochar (BC) and 46 activated carbon (AC) papers] were analyzed using a bootstrap method and a 95% confidence interval (CI) to provide insights into nutrient adsorption. The results indicated the solution temperature, pore volume, specific surface area, and pyrolysis temperature were correlated significantly to the maximum adsorption capacity (Q m ), achieving r values of 0.37, 0.23, 0.16, and 0.14, respectively. A statistically significant difference was witnessed for nitrate Q m between AC and BC with median values of 17.64 and 0.51 mg/g, respectively, and CIs of 11.01−27.56 and 0.39−0.82 mg/g, respectively. In contrast, the difference in Q m between AC and BC for phosphate and ammonium contained a high probability of chance. On average, engineered biochar achieved higher Q m values of ammonium and phosphate (12.13 and 24.73 mg/g, respectively) in comparison with that of the original biochar. However, there is a good probability of no difference in nitrate between the two, albeit greater nitrate adsorption on the modified biochar with a mean Q m of 3.03 mg/g. The tuned Cubist estimated equilibrium adsorption capacity with an R 2 of ∼0.90−0.91. The median and bootstrap 95% CI can be used as an average standard for designing adsorbate−adsorbent systems.

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“…Although the equilibrium characteristics of organic contaminant adsorption may be estimated using predictive correlations based on specific compound and adsorbent properties [1,2], such predictive methods are limited to certain classes of organic contaminants and adsorbents. In general, equilibrium isotherms of various water contaminants are invariably obtained from laboratory experiments.…”

Section: Introductionmentioning

confidence: 99%

Fitting a little-known isotherm equation to S-shaped adsorption equilibrium data

Chu¹

2021

Separation and Purification Technology

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A research topic of current interest concerns the measurement and modeling of adsorption equilibrium isotherms of water contaminants. The bulk of measured equilibrium isotherms exhibit the convex-upward or favorable curve shape, which can be described by the two-parameter Langmuir and Freundlich isotherms. An increasing number of recent studies have however reported S-shaped or sigmoidal equilibrium isotherms. Modeling such equilibrium data requires the use of isotherm equations with a highly flexible functional form. This work introduces a little-known isotherm useful for tracking the trajectory of S-shaped equilibrium data. This relatively simple three-parameter isotherm, first proposed by Krishnamurti in 1951, has theoretical merit because it is based on a cooperative adsorption mechanism. It is shown that previously published S-shaped equilibrium data of water contaminants can be represented by the Krishnamurti isotherm. Specific examples discussed are (1) ammonium ion adsorption by the clay mineral sepiolite, (2) uptake of the antibiotic ciprofloxacin by a magnetic nanosorbent, and (3) fluoride ion removal by a layered double hydroxide adsorbent. Further, it is found that a modified form of the Krishnamurti isotherm is superior to its original counterpart in tracing sigmoidal equilibrium data. The original and modified Krishnamurti isotherms can be practically very useful to describe S-shaped equilibrium data and for adsorptive process modeling.

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Correlations and prediction of adsorption capacity and affinity of aromatic compounds on activated carbons

Cited by 11 publications

References 33 publications

Simultaneous adsorption of fulvic acid and organic contaminants by KOH activated mesoporous biochar with large surface area

Simultaneous adsorption of fulvic acid and organic contaminants by KOH activated mesoporous biochar with large surface area

Insight into the Adsorption of Nutrients from Water by Pyrogenic Carbonaceous Adsorbents Using a Bootstrap Method and Machine Learning

Fitting a little-known isotherm equation to S-shaped adsorption equilibrium data

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