Batch studies of turquoise blue dye (TB) adsorption onto activated carbon prepared from low-cost adsorbents: an ANN approach

Ramesh, Pamila; Padmanabhan, Vasanthi; Saravanan, P.; Thillainayagam, Bhagavathi Pushpa

doi:10.1007/s13399-021-01355-7

Cited by 8 publications

(3 citation statements)

References 44 publications

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“…𝑞 t = 𝐾 dif 𝑡 0.5 + 𝐶 (10) where, Kdif was to the intra-particle diffusion rate constant and C was the intercept.…”

Section: Kinetics Adsorption Studiesmentioning

confidence: 99%

“…Cost, efficiency, ease of operation, the amount of secondary waste and facile recovery make adsorption process as a suitable approach for effluent treatment [9]. Developing a cost-effective, biodegradable and efficient adsorbent has been a big challenge for researchers to remove dangerous azo dyes [10,11]. Graphene-based materials, hydrogels and bio-polymers can be utilized for dye adsorption [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Magnetic Nanocomposites Containing Low and Medium-Molecular Weight Chitosan for Dye Adsorption: Hydrophilic Property Versus Functional Groups

Mirzai

Asadabadi

2021

J Polym Environ

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The azo dye removal from polluted water is vital from a sustainable viewpoint. In this study, we investigated the influence of chitosan molecular weight on the adsorptive removal of basic blue 41. For preparing nanocomposite containing medium-molecular weight chitosan (NC(M)), cross-linking of chitosan was done using diethylenetriaminepentaacetic acid, silica-modified magnetite nanoparticles and graphene oxide. Techniques including FT-IR, XRD, FESEM, TGA/DTG, VSM and N2 adsorption/desorption isotherm were applied for characterization of NC(M). The adsorption behavior of synthesized NC(M) was compared with as-prepared adsorbent containing low-molecular weight chitosan (NC(L)) (Asadabadi 2021). The experimental design was carried out using the Central Composite Design. The effect of initial pH, temperature and adsorbent concentration on the percentage of dye removal were examined and the optimum values of variables were determined. Despite NC(M) which had maximum 31% dye removal, NC(L) led to approximately 95% adsorptive removal at optimum conditions. An increase in the monomer number of chitosan caused to reduce hydrophilic property of NC(M), which in turn resulted in a repulsion force between adsorbent and dye. However, H-bonding, coulumbic attraction and pi-stacking interactions contributed in the adsorption mechanism of NC(L). The kinetics study showed that about 30 min necessitated reaching the equilibrium and the rate-limiting steps changed from film diffusion to intraparticle diffusion as time passed. The kinetics data were satisfactorily fitted by the modified pseudo-n-order model.The maximum adsorption capacity of NC(L) was obtained 55.87 mg•g -1 . The modified Langmuir-Freundlich isotherm was the best model to reproduce data. NC(L) was recovered seven times without dramatic changes in its adsorption efficiency.

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“…𝑞 t = 𝐾 dif 𝑡 0.5 + 𝐶 (10) where, Kdif was to the intra-particle diffusion rate constant and C was the intercept.…”

Section: Kinetics Adsorption Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Magnetic Nanocomposites Containing Low and Medium-Molecular Weight Chitosan for Dye Adsorption: Hydrophilic Property Versus Functional Groups

Mirzai

Asadabadi

2021

J Polym Environ

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“…In recent years, there has been a growing interest in employing artificial neural networks (ANN) for modeling the adsorption of organic dyes (Babaei et al, 2016;Hasanzadeh et al, 2019;Dahlan et al, 2023;Ramesh et al, 2023). ANNs offer a powerful tool to capture intricate relationships in data, enabling more precise predictions of adsorption behaviours.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Network for Modeling Adsorption of Congo Red Onto Activated Hazelnut Shell

Mesutoğlu

2023

Preprint

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Activated hazelnut shell (HSAC), an organic waste, was utilized for the adsorptive removal of Congo Red (CR) dye from aqueous solutions, and a modeling study was conducted using Artificial Neural Networks (ANNs). For the adsorption study, pH (3-9), initial CR concentration (5-400 mg/L), contact time (2-240 min.), and adsorbent quantity (0.5-10 g/L) parameters were investigated. Conducted in a batch system, the adsorption experiments resulted in a notable removal efficiency of 87% under optimal conditions. The kinetic data for hazelnut shell activated carbon (HSAC) removal of CR were most accurately represented by the pseudo-second-order kinetic model. Furthermore, the equilibrium data demonstrated a strong agreement with the Freundlich model. The maximum adsorption capacity of HSAC for CR was determined to be 34.8 mg/g. Considering the various experimental parameters influencing CR adsorption, an Artificial Neural Network (ANN) model was constructed. The analysis of the ANN model revealed a correlation of 98%, indicating that the output parameter could be reliably predicted. Thus, it was concluded that ANN could be employed for the removal of CR from water using HSAC.

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Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH₃)₆]Cl₃ complex

Landolsi,

Echouchene,

Chouaieb

et al. 2024

Can J Chem Eng

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The study focuses on the efficiency of hexaamminecobalt (III) chloride (HACo, [Co(NH3)6]Cl3) immobilized on activated carbon for removing methylene blue (MB) from water solutions. The primary objective of this study was to assess the sorption performance of HACo immobilized on activated carbon in removing MB from water solutions. Additionally, predictive models were developed to optimize the MB removal percentage. Lastly, the study aimed to determine the optimal conditions for achieving maximum MB removal. Samples were characterized using scanning electron microscopy. Batch sorption experiments were conducted to analyze the impact of MB concentration, adsorbent mass, pH, temperature, and contact time. Predictive models were built using multiple linear regression and neural network techniques, specifically artificial neural networks (ANN) and hybrid ANN–particle swarm optimization (ANN‐PSO). The PSO‐ANN model with a single hidden layer of eight neurons trained using the Levenberg–Marquardt algorithm demonstrated high accuracy in predicting MB removal percentage, with mean absolute percentage error (MAPE) = 0.083788, root mean square error (RMSE) = 0.11441, and R2 = 0.99693. The MB adsorption process followed a mono‐layer with one energy model and a pseudo‐first‐order kinetic model. Optimization using the genetic algorithm revealed that the maximum MB removal percentage of 99.56% is achievable at an MB concentration of 9.36 mg/L, adsorbent mass of 15.72 mg, and temperature of 311.2 K. The study confirms the effectiveness of HACo immobilized on activated carbon for MB removal. The PSO‐ANN predictive model proved superior in accuracy compared to empirical models. Optimization results provide the optimal conditions for maximizing MB removal, offering valuable insights for practical applications.

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Batch studies of turquoise blue dye (TB) adsorption onto activated carbon prepared from low-cost adsorbents: an ANN approach

Cited by 8 publications

References 44 publications

Magnetic Nanocomposites Containing Low and Medium-Molecular Weight Chitosan for Dye Adsorption: Hydrophilic Property Versus Functional Groups

Magnetic Nanocomposites Containing Low and Medium-Molecular Weight Chitosan for Dye Adsorption: Hydrophilic Property Versus Functional Groups

The Use of Artificial Neural Network for Modeling Adsorption of Congo Red Onto Activated Hazelnut Shell

Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH₃)₆]Cl₃ complex

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Batch studies of turquoise blue dye (TB) adsorption onto activated carbon prepared from low-cost adsorbents: an ANN approach

Cited by 8 publications

References 44 publications

Magnetic Nanocomposites Containing Low and Medium-Molecular Weight Chitosan for Dye Adsorption: Hydrophilic Property Versus Functional Groups

Magnetic Nanocomposites Containing Low and Medium-Molecular Weight Chitosan for Dye Adsorption: Hydrophilic Property Versus Functional Groups

The Use of Artificial Neural Network for Modeling Adsorption of Congo Red Onto Activated Hazelnut Shell

Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH3)6]Cl3 complex

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Product

Resources

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Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH₃)₆]Cl₃ complex