Predicting the adsorption capacity of organic dye using zirconium-based metal–organic framework: a comparative analysis of RSM and ANN-based models

Dahlan, Irvan; Obi, Christopher Chiedozie; Poopathi, Veshmen

doi:10.1007/s13399-023-05012-z

Cited by 3 publications

(1 citation statement)

References 44 publications

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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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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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The use of artificial neural network for modelling adsorption of Congo red onto activated hazelnut shell

Çimen Mesutoğlu

2024

Environ Monit Assess

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Biogenic synthesis of MgO nanoparticles using Arbutus Unedo leaf extract for removal of Bemacid Red from wastewater: Optimizing using BBD-RSM and ANN approach

Hamidallah,

El Rharib,

Elsenety

et al. 2024

Journal of Water Process Engineering

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Predicting the adsorption capacity of organic dye using zirconium-based metal–organic framework: a comparative analysis of RSM and ANN-based models

Cited by 3 publications

References 44 publications

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

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

The use of artificial neural network for modelling adsorption of Congo red onto activated hazelnut shell

Biogenic synthesis of MgO nanoparticles using Arbutus Unedo leaf extract for removal of Bemacid Red from wastewater: Optimizing using BBD-RSM and ANN approach

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