Determination of optimal operating parameters for tetracycline removal by adsorption from synthetic and real aqueous solutions

Costa, Letícia Reggiane de Carvalho; Ribeiro, Luana de Moraes; Hidalgo, Gelsa Edith Navarro; Féris, Liliana Amaral

doi:10.1080/10934529.2020.1829887

Cited by 12 publications

(12 citation statements)

References 42 publications

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“…For the selection of the most adequate breakthrough curve model, five statistical metrics were applied. The coefficient of determination ( R 2 ), widely used in adsorption works ,, and the adjusted coefficient of determination ( R ajstd 2 ) both based on its maximization. And three Bayesian metrics, the Akaike information criterion ( AIC : eq ), , the corrected Akaike information criterion ( AIC C : eq ), and the Bayesian information criterion ( BIC : eq ) based on its minimization. A I C = prefix− 2 0.25em normall o normalg 0.25em [ p false( boldX false| bold-italicθ false) ] + 2 N normalP where N P is the total number of parameters and p ( X | θ ) represents the likelihood function. AI C C = AIC + 2 N normalP ( N P + 1 ) N obs − N normalP − 1 where N obs is the number of observations from the sample that was applied. B I C = prefix− 2 0.25em 0.25em normall o normalg 0.25em [ p false( boldX...…”

Section: Methodsmentioning

confidence: 99%

“…For the selection of the most adequate breakthrough curve model, five statistical metrics were applied. The coefficient of determination ( R 2 ), widely used in adsorption works ,, and the adjusted coefficient of determination ( R ajstd 2 ) both based on its maximization. And three Bayesian metrics, the Akaike information criterion ( AIC : eq ), , the corrected Akaike information criterion ( AIC C : eq ), and the Bayesian information criterion ( BIC : eq ) based on its minimization. where N P is the total number of parameters and p ( X | θ ) represents the likelihood function. where N obs is the number of observations from the sample that was applied. …”

Section: Methodsmentioning

confidence: 99%

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Bayesian Computational Technique for Modeling Caffeine Adsorption in a Fixed-Bed Column: Use of the Maximum Adsorption Capacity Deterministically and Experimental Design

Oliveira

Arsufi

Estumano

et al. 2023

Ind. Eng. Chem. Res.

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This work applied a Bayesian computational technique for parameter estimation of adsorption breakthrough curve models with experimental data of caffeine (CAF) adsorption onto granular activated carbon (GAC). Different operational conditions were evaluated (volumetric flow: Q, adsorbent mass: W, and initial CAF concentration: C 0) by a two-level factorial experimental design (23) to determine the best operational conditions. The models (Thomas, Yoon–Nelson, Yan, Clark, Gompertz, and Log-Gompertz) were fitted to the experimental data, estimating and not estimating the maximum adsorption capacity (q S). For model selection, five statistical metrics were calculated. The results showed that the proposed Bayesian technique, not estimating q S, was effective and all analyzed operational conditions obtained 95% of CAF removal. In the best condition, when q S reached 7.317 mgCAF/gGAC, the model that best adjusted the experimental data was Log-Gompertz, being suitable for practical approaches, and for its mechanisms, the Clark model best predicted the evaluated fixed-bed column.

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Section: Methodsmentioning

confidence: 99%

“…For the selection of the most adequate breakthrough curve model, five statistical metrics were applied. The coefficient of determination ( R 2 ), widely used in adsorption works ,, and the adjusted coefficient of determination ( R ajstd 2 ) both based on its maximization. And three Bayesian metrics, the Akaike information criterion ( AIC : eq ), , the corrected Akaike information criterion ( AIC C : eq ), and the Bayesian information criterion ( BIC : eq ) based on its minimization. where N P is the total number of parameters and p ( X | θ ) represents the likelihood function. where N obs is the number of observations from the sample that was applied. …”

Section: Methodsmentioning

confidence: 99%

Bayesian Computational Technique for Modeling Caffeine Adsorption in a Fixed-Bed Column: Use of the Maximum Adsorption Capacity Deterministically and Experimental Design

Oliveira

Arsufi

Estumano

et al. 2023

Ind. Eng. Chem. Res.

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“…Studies present in the literature have found similar results, in which the increase in the adsorbent concentration also causes an increase in the removal of the selected pollutant until its saturation. 12,26,29 3.4. Influence of the Contact Time.…”

Section: Influence Of the Adsorbent Concentrationmentioning

confidence: 99%

“…44,45 Inverse problems can be defined as those that aim to determine causes from experimental measures of effects; thus, its solution, being the estimation of the parameters, is usually done through the minimum squares method. 26,29,46,47 Although, this approach can modify the intrinsic interpretation of the parameters, violating the assumptions of the method, because a priori information on the parameters and uncertainties of the measurements are not implemented in its resolution. 45,48−52 The removal efficiency of the adsorbent is obtained from equilibrium and kinetic batch assays, which provides the adsorption capacity and process velocity, respectively.…”

Section: Introductionmentioning

confidence: 99%

Applying the Bayesian Technique, Statistical Analysis, and the Maximum Adsorption Capacity in a Deterministic Way for Caffeine Removal by Adsorption: Kinetic and Isotherm Modeling

de Oliveira,

Nunes,

Cardoso Estumano

et al. 2024

Ind. Eng. Chem. Res.

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The present work evaluates the Bayesian statistical method in adsorption, kinetic, and isotherm studies for estimation of the parameters along with the experimental data of the solid's maximum adsorption capacity. For the experimental data, the removal of caffeine (CAF) on granular activated carbon (GAC), previously characterized, was evaluated in five different temperatures (288, 298, 308, 313, and 323 K), and the following operational conditions were investigated: pH; adsorbent concentration (C GAC ), and contact time (t). The maximum adsorption capacity was estimated in the models, the usual approach in the literature, and not by applying its experimental value (deterministic: kinetic, q e ; isotherm, q max ). The kinetic models applied were pseudo-first order (PFO), pseudo-second order (PSO), and Elovich, and for the isotherms, Langmuir, Freundlich, Redlich−Peterson, and Sips, which were selected through five statistical metrics (classical and Bayesian). The results showed that it was possible to obtain a CAF removal of 90% (residual concentration of 0.978 mg•L −1 ) at room temperature by applying pH = 6, t = 120 min, and C GAC = 10 g•L −1 . The proposed method, with the maximum adsorption capacity used deterministically and five statistical metrics, is a more reliable and accurate resolution approach for both kinetic and isotherm studies because the values of q e and q max were over-or underestimated in all temperatures evaluated in this work; the selection of the models was altered and only possible through the Bayesian metrics due to the proximity of the values obtained for R 2 . The kinetic study showed that the Elovich model fitted the experimental data more adequately for the temperatures of 288, 298, 303, and 313 K and the PFO for 323 K. For the equilibrium isotherms, the Langmuir model best fitted the system at 288 K, while the Redlich−Peterson model was more adequate at the remaining temperatures (298, 303, 313, and 323 K), leading to the conclusion that the kinetics and equilibrium of CAF adsorption onto GAC is directly affected by the temperature, being enhanced in higher temperatures, which obtained an experimental q max = 24.85 mg•g −1 at 323 K; in addition, the incorporation of caffeine occurs, mainly, on the surface of the solid.

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“…For the identification of CAF and AMP in surface water, samples were prepared using the solid phase extraction technique (EFS), using cartridges containing reversed polymeric phase (Strata-X, Phenomenex) and a Manifold system (Supelco). Methods were determined/adapted from existing methodologies (Ruela et al 2005;Jank et al 2014;Arsand et al 2018;Costa et al 2020).…”

Section: Sample Preparation For Antibiotic Quantificationmentioning

confidence: 99%

Integration of ozonation with water treatment for pharmaceuticals removal from Arroio Diluvio in southern Brazil

Costa

Féris

2023

Water Science and Technology

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Pharmaceutical compounds can reach water bodies through sewage systems. The conventional process of water treatment is insufficient for the removal of these contaminants. The ozonation has great potential to be integrated into the physical-chemical treatment, since it promotes the reduction of pharmaceuticals, reduces the generation of disinfection byproducts and can reduce operational costs. In this work, the integration of the ozonation process with water treatment was studied. The ozone was applied in the pre-oxidation and intermediate ozonation stages, to evaluate the dependence of different variables. Water samples were collected from Arroio Diluvio, a river of the city of Porto Alegre (RS – Brazil). The doses of ozone were maintained between 0.5 and 1.0 mgO3 L−1 while the sulfate was varied between 25 and 150 mg·L−1. The pre-ozonation resulted in a removal of caffeine and ampicillin at pH 10.0, time of 15 min and coagulant concentration of 52.5 mg·L−1. The intermediate ozonation provided a removal of both pharmaceuticals, with pH 10.0 and a time of 5 min of bubbling. Based on the results, it was confirmed that the synergy of the ozonation process with conventional water treatment is an effective, sensitive and fast method for the removal of drugs from the aqueous medium.

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Determination of optimal operating parameters for tetracycline removal by adsorption from synthetic and real aqueous solutions

Cited by 12 publications

References 42 publications

Bayesian Computational Technique for Modeling Caffeine Adsorption in a Fixed-Bed Column: Use of the Maximum Adsorption Capacity Deterministically and Experimental Design

Bayesian Computational Technique for Modeling Caffeine Adsorption in a Fixed-Bed Column: Use of the Maximum Adsorption Capacity Deterministically and Experimental Design

Applying the Bayesian Technique, Statistical Analysis, and the Maximum Adsorption Capacity in a Deterministic Way for Caffeine Removal by Adsorption: Kinetic and Isotherm Modeling

Integration of ozonation with water treatment for pharmaceuticals removal from Arroio Diluvio in southern Brazil

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