Thermodynamic Equilibrium Kinetics of Sewage Sludge Ash Pellets for the Removal of Methylene Blue (MB) from Aqueous Solutions

El-Deen, Rasha A. Sary; Mahmoud, Nouran Y.; Mahmoud, Ahmed S.

doi:10.4028/p-vw0a4u

Cited by 5 publications

(5 citation statements)

References 21 publications

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“…The adsorption systems neither consume a lot of electricity nor generate large amounts of sludge [4,5]. Moreover, the adsorbent material could be appropriately synthesized to provide effective adsorption sites to capture the pollutants from wastewater [6,7]. However, the adsorption process is highly influenced by several operational factors such as time, pH, and mixing speed [8].…”

Section: Introductionmentioning

confidence: 99%

Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process

Mostafa

Mahmoud

et al. 2022

Adsorption Science & Technology

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Predicting the adsorption performance to remove organic pollutants from wastewater is an essential environmental-related topic, requiring knowledge of various statistical tools and artificial intelligence techniques. Hence, this study is the first to develop a quadratic regression model and artificial neural network (ANN) for predicting biochemical oxygen demand (BOD) removal under different adsorption conditions. Nanozero-valent iron encapsulated into cellulose acetate (CA/nZVI) was synthesized, characterized by XRD, SEM, and EDS, and used as an efficient adsorbent for BOD reduction. Results indicated that the medium pH and adsorption time should be adjusted around 7 and 30 min, respectively, to maintain the highest BOD removal efficiency of 96.4% at initial BOD concentration C o = 100 mg/L, mixing rate = 200 rpm, and adsorbent dosage of 3 g/L. An optimized ANN structure of 5–10–1, with the “trainlm” back-propagation learning algorithm, achieved the highest predictive performance for BOD removal ( R 2 : 0.972, Adj- R 2 : 0.971, RMSE: 1.449, and SSE: 56.680). Based on the ANN sensitivity analysis, the relative importance of the adsorption factors could be arranged as pH > adsorbent dosage > time ≈ stirring speed > C o . A quadratic regression model was developed to visualize the impacts of adsorption factors on the BOD removal efficiency, optimizing pH at 7.3 and time at 46.2 min. The accuracy of the quadratic regression and ANN models in predicting BOD removal was approximately comparable. Hence, these computational-based methods could further maximize the performance of CA/nZVI material for removing BOD from wastewater under different adsorption conditions. The applicability of these modeling techniques would guide the stakeholders and industrial sector to overcome the nonlinearity and complexity issues related to the adsorption process.

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Section: Introductionmentioning

confidence: 99%

Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process

Mostafa

Mahmoud

et al. 2022

Adsorption Science & Technology

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“…They found that the optimal time was 60 min with a removal efficiency of 87.9 and 100% for COD and BOD 5 respectively. Moreover, Rasha A. Sary El-deen et al [47] worked on the removal of COD from Domestic Wastewater using Entrapped Sewage Sludge Ash and the effective time was 60 min with a removal efficiency of 78% for an initial COD concentration 400 mg/L.…”

Section: Discussionmentioning

confidence: 99%

Reduction of Oxidizable Pollutants in Waste Water from the Wadi El Bey River Basin Using Magnetic Nanoparticles as Removal Agents

et al. 2023

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Many of the current strategies for removing pollutants from water are based on nanomaterials and nanotechnology. Lower values of Biological Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) in water results in reduction in the amount of oxidizable pollutants. We present a study on the reduction of COD and BOD5 in water from Wadi El Bey River (Tunisia), using magnetite nanoparticles (MNPs) and magnetic fields. The COD and BOD5 removal reached values higher than 50% after 60 min, with optimum efficiency at pH values of ≈8 and for MNPs concentrations of 1 g/L. The use of a permanent magnetic field (0.33 T) showed an increase of COD and BOD5 removal from 61% to 76% and from 63% to 78%, respectively. This enhancement is discussed in terms of the MNPs coagulation induced by the magnetic field and the adsorption of ionic species onto the MNPs surface due to Fe3O4 affinity.

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“…where Removal (R) (%) is heptachlor epoxide removal efficiency (%), initial concentration (Co) (mg/L), and final concentration (Cf) is the final (mg/L) [33,34].…”

Section: 3batch Adsorption Studiesmentioning

confidence: 99%

Heptachlor Epoxide -Pesticide- Removal from Aqueous Solutions Using Nano Zero Valent Iron: Operating Conditions, Response Surface Methodology, and Artificial Intelligence Neural Networks

Mohamed

Mahmoud

2023

SSP

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There are many studies that detail how hazardous pesticides are to aquatic life, plants, animals, and humans, but there are fewer that describe how pesticides are treated during a separate treatment procedure. This study, which examines the removal of the highly toxic pesticide "heptachlor epoxide", is crucial to achieving SDG 6. Under operational circumstances, the prepared green nanoiron was successfully synthesized and characterized for the removal of heptachlor epoxide from aqueous solutions. For starting heptachlor epoxide concentrations of 100 and 10 μg/L at neutral medium pH 7, 0.8 g/L of green nZVI for 80 min, and a 200 RPM stirring rate, the removal efficiency varied between 55 and 100%, respectively. The RSM results indicated that the model R2 was 94.6%, and all operating conditions were significant to describe the removal efficiency with a p-value <0.05. The linear regression histogram indicated that the variation between expected and experimental removal efficiency ranged between (-1, 1%). The ANNs results by using MLP with network 6-3-1 indicated that nZVI was able to reduce heptachlor epoxide concentrations with a Sum of Squares Error of 0.052 for training and 0.177 for testing. Also, the ANNs described the importance of operating conditions and indicated that the most effective operating conditions were dose and less important was stirring rate, showing agreement with the obtained RSM results. Finally, this paper recommended using nZVI for heptachlor epoxide removal. Keywords Environmental toxicology; climate action; SDG 6; nanotechnology; pesticide removal; Heptachlor epoxide.

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Thermodynamic Equilibrium Kinetics of Sewage Sludge Ash Pellets for the Removal of Methylene Blue (MB) from Aqueous Solutions

Cited by 5 publications

References 21 publications

Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process

Computational-Based Approaches for Predicting Biochemical Oxygen Demand (BOD) Removal in Adsorption Process

Reduction of Oxidizable Pollutants in Waste Water from the Wadi El Bey River Basin Using Magnetic Nanoparticles as Removal Agents

Heptachlor Epoxide -Pesticide- Removal from Aqueous Solutions Using Nano Zero Valent Iron: Operating Conditions, Response Surface Methodology, and Artificial Intelligence Neural Networks

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