Highly mesoporous K2CO3 and KOH/activated carbon for SDBS removal from water samples: Batch and fixed-bed column adsorption process

Author(s):  
Soheil Valizadeh ◽  
Habibollah Younesi ◽  
Nader Bahramifar
2021 ◽  
Vol 11 (2) ◽  
Author(s):  
Nathalia Krummenauer Haro ◽  
Ivone Vanessa Jurado Dávila ◽  
Keila Guerra Pacheco Nunes ◽  
Marcela Andrea Espina de Franco ◽  
Nilson Romeu Marcilio ◽  
...  

AbstractThis work studied the removal of paracetamol through the adsorption process using the granular activated carbon. The results indicated that it was possible to obtain 95% of removal under the experimental conditions of pH 6, 120 min of process and 5 g L−1 of solid adsorbent. The kinetic model that best fit the experimental data was the pseudo-first order. The isotherm model that best fit the experimental data was the Sips. The thermodynamic tests indicated that the adsorption process was favorable and spontaneous and confirmed the endothermic nature of the process. In fixed bed column adsorption, the best operating condition found was obtained using the flow rate of 3 mL min−1 and bed mass equal to 0.5 g. In this case, the system presented the highest volume of treated PAR effluent, of 810 mL per gram of carbon in the bed, besides a longer rupture time and bed saturation.


LWT ◽  
2014 ◽  
Vol 59 (2) ◽  
pp. 1025-1032 ◽  
Author(s):  
Anderson Marcos Dias Canteli ◽  
Danielle Carpiné ◽  
Agnes de Paula Scheer ◽  
Marcos R. Mafra ◽  
Luciana Igarashi-Mafra

2019 ◽  
Vol 25 (4) ◽  
pp. 369-382
Author(s):  
Manuela Leite ◽  
Matheus Santos ◽  
Eulina Costa ◽  
Acenini Balieiro ◽  
Álvaro Lima ◽  
...  

Artificial neural network (ANN) techniques are effective in modeling nonlinear processes, are simple to implement and require low computational time. In this work, the lactose adsorption process for continuous flow in a fixed-bed column with a molecularly imprinted polymer (MIP) adsorbent was modeled using an ANN technique. The neural models allowed predicting the relative lactose concentration (C/C0) from the interactions between the variables of contact time (min), temperature (?C), granulometry (mesh), bed height (cm) and flow rate (mL min-1). The ANN models were developed in MATLAB using multilayer perceptrons (MLP) and a radial basis function network (RBF). The MLP model was developed using a three-layer feed forward backpropagation network with 5, 8 and 4 neurons in the first, second and third layer, respectively. The function (RBF) network is also proposed and its performance is compared to a traditional network type. The best architecture configuration RBF model was developed using 5, 14 and 1 neurons in the first, second and third layer, respectively. The proposal of development of mathematical models applied to multi-component adsorption system for milk using these approaches is innovative. The resulting breakthrough curve models for lactose adsorption were in good agreement with the experimental results. Performance indices, such as R?, MSE, RMSE, SSE, MAE and RME were used to evaluate the reliabilities and accuracies of the models. A comparison between the ANN models shows the ability to predict the breakthrough curves of lactose removal in the milk adsorption process. Though, the MLP network model shows more accurately a higher correlation coefficient (R2 = 0.9751) and lower values for the obtained error indices. The accuracy of the model is confirmed by the comparison between the predicted and experimental data. The results showed that both neural models efficiently described the non-linear process of lactose adsorption in a fixed-bed column.


2015 ◽  
Vol 48 (2) ◽  
pp. 123-126 ◽  
Author(s):  
Kotaku Takeuchi ◽  
Yoshimasa Amano ◽  
Motoi Machida ◽  
Fumio Imazeki

2017 ◽  
Vol 890 ◽  
pp. 93-97
Author(s):  
Wara Dyah Pita Rengga ◽  
Sri Wahyuni ◽  
Agung Feinnudin

The performance of nanosilver loaded bamboo-based activated carbon as an adsorbent used for the adsorptive removal of formaldehyde in the air. The size porous of the active carbon is predominantly on the size of mesoporous and microporous. Adsorption tests have been evaluated in laboratory scale fixed-bed column, at different temperatures and initial formaldehyde concentration. In order to investigate is both equilibrium and thermodynamic aspects. The experimental data was fitted with Langmuir model and fit well with the adsorption capacity of 91-110 mg/g. The increase in temperature reduces the adsorption capacity. The thermodynamic parameters show that the values of ∆Go obtained to confirm the feasibility of activated carbon effective sorbents of formaldehyde. The formaldehyde adsorption process is exothermic and adsorbent has a good affinity to formaldehyde.


2020 ◽  
Vol 81 (10) ◽  
pp. 2109-2126 ◽  
Author(s):  
Seyed Omid Ahmadinejad ◽  
Seyed Taghi Omid Naeeni ◽  
Zahra Akbari ◽  
Sara Nazif

Abstract One of the major pollutants in leachate is phenol. Due to safety and environmental problems, removal of phenol from leachate is essential. Most of the adsorption studies have been conducted in batch systems. Practically, large-scale adsorption is carried out in continuous systems. In this research, the adsorption method has been used for phenol removal from leachate by using walnut shell activated carbon (WSA) and coconut shell activated carbon (CSA) as adsorbents in a fixed-bed column. The effect of adsorbent bed depth, influent phenol concentration and type of adsorbent on adsorption was explored. By increasing the depth of the adsorbent bed in the column, phenol removal efficiency and saturation time increase significantly. Also, by increasing the influent concentration, saturation time of the column decreases. To predict the column performance and describe the breakthrough curve, three kinetic models of Yon-Nelson, Adams-Bohart and Thomas were applied. The results of the experiments indicate that there is a good match between the results of the experiment and the predicted results of the models.


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