Artificial Neural Network Approach Modeling for Sorption of Cobalt from Aqueous Solution Using Modified Maghemite Nanoparticles

Hassan, Mohamed R.; Fikry, R. M.; Yakout, Sobhy M.

doi:10.1061/(asce)ee.1943-7870.0001565

Cited by 12 publications

(8 citation statements)

References 58 publications

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“…Parameters of input will be selected for output influencing parameters. The back-propagation method has been widely used to model genetic challenges (Hassan et al 2020).…”

Section: Methodsmentioning

confidence: 99%

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An emergent addition for the optimal systemization of wastewater utilization plants using artificial intelligence

Hasan

2021

Water Science and Technology

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The treatment of wastewater is an essential factor in preventing pollutants and promoting the quality of the water. The inherent complexity, influential impact and the solid waste infrastructure lead to confusion and variance in the primary clarifier for wastewater. These inconsistencies lead to variations in the purity and capacity constraints of wastewater and the existential impact of water receipt. The water treatment is a complicated task that has means of chemical, technical & biochemical influences. A credible ANN method is necessary for another waste water treatment plant to prevent the breakdown of the processes. Virtual reality seems to have become a strong solution for preventing waste management uncertainties and problems. This is not only due to high deformations but also to significant external disturbances that water systems are controlling challenges. Climate is among the most significant of such disturbances. Various environmental conditions actually include different influx frequencies and levels of substances. Water contamination has become one of the extremely serious growing conservation; sewage treatment plant identification is a key major issue here and the agencies enforce tighter requirements for the operating of wastewater software systems. This article plans to create models of achievement and prospects for the possible future guidance of recent research borders for the use of artificial intelligence in wastewater treatment plants which concurrently deal with pollutants. This study has shown us that the composite ANN provides a greater level of competence in plant prediction and systemization. Highlight Systematize of Wastewater Utilization Plants, Artificial Neural Networks, artificial intelligence, Prediction Analysis, Reliability.

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“…Parameters of input will be selected for output influencing parameters. The back-propagation method has been widely used to model genetic challenges (Hassan et al 2020).…”

Section: Methodsmentioning

confidence: 99%

“…The network used was a feedback system or a feedback system multi-layer (Hassan et al 2020). Using a backpropagation algorithm, forwarding connections are most frequently practiced.…”

Section: Methodsmentioning

confidence: 99%

An emergent addition for the optimal systemization of wastewater utilization plants using artificial intelligence

Hasan

2021

Water Science and Technology

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“…The figure shows that the percentage of lead(II) ions increased gradually with an increase in the adsorbent mass of MnFe 2 O 4 nanoparticles and increasing the masses of MnFe 2 O 4 nanoparticles from 0.2 to 1.4 g/L increased the percentage of adsorption of lead(II) ions from 35 to 100%. This increase in the percentage of adsorption of lead(II) ions may be attributed to the increase in the adsorption sites toward the metal ions and, consequently, the surface area of the adsorbent (Hassan Mohamed et al 2020).…”

Section: Effect Of Sorbent Massmentioning

confidence: 99%

“…The linear form of the pseudo-first-order kinetic model can be represented by the Lagergren rate in the following equation (Hassan Mohamed et al 2020):…”

Section: Pseudo-first-order Kinetic Modelmentioning

confidence: 99%

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Magnetically synthesized MnFe2O4 nanoparticles as an effective adsorbent for lead ions removal from an aqueous solution

Hassan

Aly

2021

Journal of Water Supply: Research and Technology-Aqua

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The adsorption behavior of lead(II) using a new magnetic adsorbent is investigated. The facile synthesis of MnFe2O4 was carried out using the co-precipitation method. The different parameters that affected the adsorption process were investigated such as contact time, metal ion concentration, pH, temperature, and the adsorbent dosage. The maximum lead(II) sorption capacity was found to be 75.75 (mg/g) and obtained using 1 g/L MnFe2O4 when pH equals 5.3, a temperature of 25 °C, and contact time as 60 min. The adsorption isotherm study indicated that the Langmuir model was the best model that described the adsorption process using 1 g/L MnFe2O4. Based on the values of correlation coefficient data (R2), the kinetic adsorption parameters were well defined by the second-order kinetic model. Furthermore, the temperature effect findings have been confirmed that the removal of lead ions was endothermic. The desorption efficiency reached more than 88% when used 0.01 M NaOH as an eluent.

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Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

Jadhav

Pathak

Raut

2023

Environ Monit Assess

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Artificial Neural Network Approach Modeling for Sorption of Cobalt from Aqueous Solution Using Modified Maghemite Nanoparticles

Cited by 12 publications

References 58 publications

An emergent addition for the optimal systemization of wastewater utilization plants using artificial intelligence

An emergent addition for the optimal systemization of wastewater utilization plants using artificial intelligence

Magnetically synthesized MnFe2O4 nanoparticles as an effective adsorbent for lead ions removal from an aqueous solution

Water and wastewater quality prediction: current trends and challenges in the implementation of artificial neural network

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