Prediction of methyl orange dye (MO) adsorption using activated carbon with an artificial neural network optimization modeling

Alardhi, Saja Mohsen; Fiyadh, Seef Saadi; Salman, Ali Dawood; Adelikhah, Mohammademad

doi:10.1016/j.heliyon.2023.e12888

Cited by 36 publications

(14 citation statements)

References 46 publications

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“…Lately, there has been great resurgence in the development of complex algorithms used as tool for computation. Numerous researches discussed the potential of (ANNs) as a tool for computing, representing, and acquiring a mapping from one input to another [ [29] , [30] , [31] ]. The ANN is regarded as an approximator in science, with the ability to find relationships in given data sets; it is likely that (ANNs) will be able to address challenging issues including nonlinear modelling, pattern recognition, association, and classification.…”

Section: Introduction Of Artificial Neural Networkmentioning

confidence: 99%

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Fiyadha¹,

Alardhi²,

Omar³

et al. 2023

Heliyon

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Section: Introduction Of Artificial Neural Networkmentioning

confidence: 99%

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Fiyadha¹,

Alardhi²,

Omar³

et al. 2023

Heliyon

Self Cite

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“…On the other hand, methyl orange dye molecules can interact with –NH2 groups forming covalent and semi-covalent bonds. Additionally, the formation peaks like 2679.24 cm −1 , the diminishing of hydroxide groups (3329 cm −1 ) as well as the shifting of various functional group signifying peaks like (2132–2114 cm −1 , 1728 to 1710 cm −1 , 2306 to 2270 cm −1 , 1022 to 1051 cm −1 ) would indicate the interaction between the adsorbate and adsorbent where various functional groups played crucial role [ 82 , [99] , [100] , [101] ].…”

Section: Resultsmentioning

confidence: 99%

“…Finally, removal efficiency (RE) and adsorption capacity (Qt) were determined using Equations (1) , (2) ) respectively.

where, Co and Ct initial and final dye concentration respectively, V denotes solution volume and m is mass of adsorbent utilized [ [47] , [48] , [49] ].…”

Section: Materials Methodsmentioning

confidence: 99%

Preparation of Rumex abyssinicus based biosorbent for the removal of methyl orange from aqueous solution

Abewaa,

Adino,

Mengistu

2023

Heliyon

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“…From Eqs. ( 1 ) and ( 2 ), the dye removal from the solution and the adsorption capacity ( q e ) can be estimated 34 : where C o and C e are the initial and equilibrium concentrations of adsorbate (mg L −1 ), respectively, denotes the volume (L) of the solution, is the mass (g) of the adsorbent, and introduces the amount of the contaminant adsorbed (mg g −1 ).…”

Section: Methodsmentioning

confidence: 99%

Olive stone as an eco-friendly bio-adsorbent for elimination of methylene blue dye from industrial wastewater

Alardhi,

Salih,

Ali

et al. 2023

Sci Rep

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Adsorbents synthesized by activation and nanoparticle surface modifications are expensive and might pose health and ecological risks. Therefore, the interest in raw waste biomass materials as adsorbents is growing. In batch studies, an inexpensive and effective adsorbent is developed from raw olive stone (OS) to remove methylene blue (MB) from an aqueous solution. The OS adsorbent is characterized using scanning electron microscopy (SEM), Fourier Transform Infra-Red (FTIR), and Brunauer–Emmett–Teller (BET) surface area. Four isotherms are used to fit equilibrium adsorption data, and four kinetic models are used to simulate kinetic adsorption behavior. The obtained BET surface area is 0.9 m2 g−1, and the SEM analysis reveals significant pores in the OS sample that might facilitate the uptake of heavy compounds. The Langmuir and Temkin isotherm models best represent the adsorbtion of MB on the OS, with a maximum monolayer adsorption capacity of 44.5 mg g−1. The best dye color removal efficiency by the OS is 93.65% from an aqueous solution of 20 ppm at the OS doses of 0.2 g for 90 min contact time. The OS adsorbent serves in five successive adsorption cycles after a simple filtration-washing-drying process, maintaining MB removal efficiency of 91, 85, 80, and 78% in cycles 2, 3, 4, and 5, respectively. The pseudo second-order model is the best model to represent the adsorption process dynamics. Indeed, the pseudo second-order and the Elovich models are the most appropriate kinetic models, according to the correlation coefficient (R2) values (1.0 and 0.935, respectively) derived from the four kinetic models. The parameters of the surface adsorption are also predicted based on the mass transfer models of intra-particle diffusion and Bangham and Burt. According to the thermodynamic analysis, dye adsorption by the OS is endothermic and spontaneous. As a result, the OS material offers an efficient adsorbent for MB removal from wastewater that is less expensive, more ecologically friendly, and economically viable.

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Prediction of methyl orange dye (MO) adsorption using activated carbon with an artificial neural network optimization modeling

Cited by 36 publications

References 46 publications

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique

Preparation of Rumex abyssinicus based biosorbent for the removal of methyl orange from aqueous solution

Olive stone as an eco-friendly bio-adsorbent for elimination of methylene blue dye from industrial wastewater

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