Understanding of mass transfer resistance for the adsorption of solute onto porous material from the modified mass transfer factor models

Fulazzaky, Mohamad Ali; Khamidun, Mohd Hairul; Omar, Roslan

doi:10.1016/j.cej.2013.05.100

Cited by 89 publications

(50 citation statements)

References 46 publications

(63 reference statements)

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“…For these fluids, the C/C 0 ratio can extrapolate the boundary of the unit, considering the different adsorption affinities of the various solutes. Curves of this type reflect the phenomenon of sequential exchange, in which a more selective solute is able to remove, from the site, another previously exchanged solute, which is released into the liquid medium [45]. Similar results have been reported by another researcher who used an adsorption process in a multicomponent system [46].…”

Section: Comparing Model Responses and Experimental Datasupporting

confidence: 78%

“…This can be explained by the fact that the diffusion process that controls the adsorption becomes slow, and hence, the adsorbent needs more time to bind with the lactose efficiently. The decrease in the breakthrough time with an increase in the flow rate may be due to a fixed saturation capacity of the bed based on the same driving force giving rise to a shorter time for saturation at higher flow rates [45,47].…”

Section: Comparing Model Responses and Experimental Datamentioning

confidence: 99%

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Modeling of milk lactose removal by column adsorption using artificial neural networks: MLP and RBF

Leite

Santos

Costa

et al. 2019

CI&CEQ

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Article Highlights • Mathematical models are proposed for the removal of lactose from milk • Multilayer perceptron and radial basis function neural networks are applied • Neural models (MLP and RBF) were developed to predict breakthrough curves • The operational parameters were evaluated for lactose adsorption capacity • The models effectively predicted the breakthrough curves under various conditions

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Section: Comparing Model Responses and Experimental Datasupporting

confidence: 78%

Section: Comparing Model Responses and Experimental Datamentioning

confidence: 99%

Modeling of milk lactose removal by column adsorption using artificial neural networks: MLP and RBF

Leite

Santos

Costa

et al. 2019

CI&CEQ

View full text Add to dashboard Cite

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“…The ability of chitin to remove toxic metals by adsorption has already been reported elsewhere (Jaafarzadeh et al 2014;Karthikeyan et al 2005;Mohan and Syed-Shafi 2013;Sofiane and Sofia 2015;Xiong 2010), but the mechanism has not been fully explained (McKay et al 1999;Volesky 1990). The use of the mass transfer models developed by Fulazzaky et al (2011Fulazzaky et al ( , 2013Fulazzaky et al ( , 2015 for the determination of external, internal and global mass transfer factors can allow us to better understand the adsorption mechanism.…”

Section: Introductionmentioning

confidence: 99%

Removal of heavy metals by chitin: equilibrium, kinetic and thermodynamic studies

2019

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Adsorption is one of the most commonly used methods for the wastewaters treatment. In this work, we studied the impact of experimental conditions on the adsorption of heavy metals M(II) (M = Cd, Ni, Cu, Pb and Zn) in batch system using chitin obtained from crab shells. This biomaterial is selected because of its low cost, availability and efficiency. The M(II) adsorption was found to be dependent on the initial pH, contact time, initial concentration of M(II) and biomass dose. The kinetic models of Elovich, pseudo-first-order and pseudo-second-order kinetic models were successfully applied, providing the best fitting of the experimental data. The Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isotherms and the thermodynamic parameters were also discussed. The adsorption capacity peaks at: 50, 47.61, 43.4, 40 and 38.46 (mg L −1) for Pb(II), Cu(II),Ni(II), Cd(II) and Zn(II), respectively. The negative free energy (∆G°) and positive enthalpy (∆H°) indicated spontaneous and endothermic adsorption.

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“…37 A low RF value of 3.09% obtained during the MCF experiments with injected nutrients may be related to the development of a laboratory conditioning simulation module with the indigenous Geobacillus toebii R-32639 colonies; however, it was not the optimal response. 6,7,[41][42][43] The use of bioproducts (or biosurfactants) in drilling FO during MCF experiments may change the formation wettability from oil-wet to water-wet AC. The difference in RF value between these two techniques should be due to the oil recovery mechanisms involved with MCF experiments; fractured chalk blocks could be dependent on the wettability of the chalk, as the wettability has great impact on the fracture/matrix hydrocarbon exchange, 38,39 while the mechanisms of CO Fig.…”

Section: Microbial Core Ooding Experiments To Increase Crude Oil Promentioning

confidence: 99%

Laboratory simulation of microbial enhanced oil recovery using Geobacillus toebii R-32639 isolated from the Handil reservoir

2015

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Microbial enhanced oil recovery (MEOR) is a biological based technology to enhance crude oil (CO) recovery at old oil wells by manipulating function or structure or both of the microbial environments existing in oil reservoirs. Even if many biosurfactant-producing microbes can be found in different environments, the ability of Geobacillus toebii R-32639 to enhance CO recovery needs to be verified. In this study, artificial cores (ACs) are used to simulate the application of MEOR by three scenarios of injecting nutrients, microbial culture and bioproducts. The use of Geobacillus toebii R-32639 as biosurfactant producer enables a lowering by 25.3% of interfacial tension and by 14.1% of CO viscosity and also enables the degrading of a 7.4-28.8% fraction of (C 12 -C 34 ) hydrocarbons after 7 days of incubation. The injection of microbial culture into a laboratory-scale CO reservoir of the AC for the desirable purpose of enhanced oil recovery could feasibly achieve an average recovery factor of 14.27% but lower by 5.50% the AC porosity and by 91.0% the AC permeability; this is the best-case scenario of the microbial core flooding (MCF) experiments. The simulation of MEOR by using bacterial strains of Geobacillus toebii R-32639 provides valuable insight into the use of MCF experiments to obtain greater oil production from existing reservoirs.

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Understanding of mass transfer resistance for the adsorption of solute onto porous material from the modified mass transfer factor models

Cited by 89 publications

References 46 publications

Modeling of milk lactose removal by column adsorption using artificial neural networks: MLP and RBF

Modeling of milk lactose removal by column adsorption using artificial neural networks: MLP and RBF

Removal of heavy metals by chitin: equilibrium, kinetic and thermodynamic studies

Laboratory simulation of microbial enhanced oil recovery using Geobacillus toebii R-32639 isolated from the Handil reservoir

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