A review exploring the adsorptive removal of organic micropollutants on tailored hierarchical carbon nanotubes

Alayan, Haiyam Mohammed; Aljumaily, Mustafa Mohammed; AlSaadi, Mohammed Abdulhakim; Mjalli, Farouq S.; Hashim, Mohd Ali

doi:10.1080/02772248.2021.1932888

Cited by 8 publications

(4 citation statements)

References 151 publications

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“…The chemical sorption that takes place during the adsorption of MB onto CNT is considered to be the rate-controlling phase, as per the Pseudo-second order model. This sorption involves valence forces that arise from the sharing or exchanging of electrons between the pigment and the adsorbent 31 – 33 . Additionally, Fig.…”

Section: Findings and Discussionmentioning

confidence: 99%

“…6 c displays a relatively linear graph acquired through the regression analysis of qt against t from the regression analysis of qt versus t0.5, which yields an R 2 value of 0.914. However, the disparity between the line and the origin implies that external mass transfer could potentially play a significant role in the adsorption process, in addition to intraparticle diffusion 28 , 31 . The observation is supported by the noteworthy intercepts witnessed in the linear segment of the graph (C = 101.79), which signifies a notable involvement of the CNT surface in the removal of MB and emphasizes the significance of diffusion in the boundary layer 33 .…”

Section: Findings and Discussionmentioning

confidence: 99%

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Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

Mijwel,

Ahmed,

Afan

et al. 2023

Sci Rep

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This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550 °C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 × 10–4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater.

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Section: Findings and Discussionmentioning

confidence: 99%

Section: Findings and Discussionmentioning

confidence: 99%

Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

Mijwel,

Ahmed,

Afan

et al. 2023

Sci Rep

Self Cite

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“…The air in the permeate side is evacuated by applying a continuous vacuum below the equilibrium vapour pressure [16]. VMD can be used for a variety of purposes, and not only for seawater desalination, including ethanol recovery, the removal of trace pollutants, and the removal of volatile organic compounds (VOCs) from water [17,18]. Higher permeate water flux can be obtained with minor conductive heat loss across the membrane in the VMD setup [19].…”

Section: Introductionmentioning

confidence: 99%

Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques

Ismael,

Khaleel,

Ibrahim

et al. 2023

Membranes

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Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales. In this work, a novel hybrid model was developed based on incorporating a spotted hyena optimizer (SHO) with support vector machine (SVR) to predict the flux pressure in VMD. The SVR–SHO hybrid model was validated with experimental data and benchmarked against other machine learning tools such as artificial neural networks (ANNs), classical SVR, and multiple linear regression (MLR). The results show that the SVR–SHO predicted flux pressure with high accuracy with a correlation coefficient (R) of 0.94. However, other models showed a lower prediction accuracy than SVR–SHO with R-values ranging from 0.801 to 0.902. Global sensitivity analysis was applied to interpret the obtained result, revealing that feed temperature was the most influential operating parameter on flux, with a relative importance score of 52.71 compared to 17.69, 17.16, and 14.44 for feed flowrate, vacuum pressure intensity, and feed concentration, respectively.

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“…Nowadays, supplying suitable freshwater has become very important for human life and different purposes due to the rapid population growth (Alayan et al 2020). One of the leading technologies that have been used in the treatment of water is membrane technology owing to its competent removal of pollutants from contaminated water(Al-Furaiji et al 2021; Alayan et al 2021). Reverse osmosis (RO) is an example of membrane technologies which has been used for treating saline water (Kalash et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A Mini-Review on Thin-Film Composite Hollow Fiber Membranes for Forward Osmosis Applications

Al-Musawy¹,

Al-Furaiji²,

Aljumaily³

et al. 2023

Ecol. Eng. Environ. Technol.

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Forward osmosis (FO) is an emerging technology that has been extensively studied in the last decade as an efficient method for desalination and water treatment. FO presents many benefits over traditional desalination technologies such as reverse osmosis and distillation. Nevertheless, there are many decisive challenges; the great significance one is the new modification and advances in the preparation of the TFC membranes that must be achieved to enhance the FO performance. Therefore, preparing a suitable TFC membrane with a low structural parameter, low tortuosity, and high porosity are preferred in preparing the TFC membranes to get higher water flux and lower salt flux. This paper reviewed the recent development and advances in using TFC hollow fiber membranes in FO applications. Within that, the most widely applied monomers to prepare the thin polyamide layer (PA) in TFC membranes and the additives that are added during the preparation of the PA layer and their effect on the performance of the TFC membranes have been discussed. Moreover, an effort is made to generate a TFC membrane properties and performance trend according to the results of the water permeate flux and reverse salt flux of the modified TFC FO membranes and the future perspectives and concluding remarks on the FO membrane are evaluated.

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A review exploring the adsorptive removal of organic micropollutants on tailored hierarchical carbon nanotubes

Cited by 8 publications

References 151 publications

Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques

A Mini-Review on Thin-Film Composite Hollow Fiber Membranes for Forward Osmosis Applications

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