Aerobic bioreactors: A Bayesian point of view applied to hydrodynamic characterization and experimental evaluation of tracers

Ferreira, Jackline Rodrigues; Senna, Adriano Passos; Macêdo, Emanuel Negrão; Estumano, Diego Cardoso

doi:10.1016/j.ces.2023.118850

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…The algorithm used to accept and reject the parameters drawn was Metropolis–Hastings, which has already been applied in the adsorption field in the literature. ,,− As for the statistical metrics to assess how well the model can reproduce the experimental data, the coefficient of determination was adopted and in the case of assessing kinetic models in which there is more than one, the Bayesian–BIC metric was used to assess which is the best model …”

Section: Methodsmentioning

confidence: 99%

Classical and Bayesian Computational Statistics Applied to Acetylsalicylic Acid Mitigation in Aqueous Solutions: Study of Batch Scale, Kinetic Modeling, and Fixed-Bed Column Adsorption

Jurado-Davila,

Reolon,

Nunes

et al. 2024

Ind. Eng. Chem. Res.

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This study focused on the adsorption of acetylsalicylic acid (ASA) using activated carbon, alongside an approach integrating Monte Carlo Bayesian modeling. The influence of the adsorbent mass, contact time, and pH was studied in batch adsorption. A fixed-bed column study was conducted to evaluate practical conditions such as the feed-flow rate, adsorbent bed, and initial ASA concentration. The Monte Carlo Bayesian method was applied to analyze the parameter estimation procedure and to improve our understanding of the underlying mechanisms of adsorption. The efficiency of ASA adsorption achieved was 2.5 mg g −1 . The kinetic model of the pseudofirst-order (PFO) presented a lower Bayesian information criteria (BIC) value (113.72). The diffusion of the column bed (D z ) and the Langmuir constant (K L ) presented significant variation when the D z varied in the range of [50.71−142.45] and the K L varied in the range of [0.020−0.059]. The results indicate that the model used is robust enough to simulate the breakthrough curves even with a low coefficient of determination.

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Section: Methodsmentioning

confidence: 99%

Classical and Bayesian Computational Statistics Applied to Acetylsalicylic Acid Mitigation in Aqueous Solutions: Study of Batch Scale, Kinetic Modeling, and Fixed-Bed Column Adsorption

Jurado-Davila,

Reolon,

Nunes

et al. 2024

Ind. Eng. Chem. Res.

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“…This article used the Monte Carlo technique via Markov chain to estimate the unknown parameters and thus use the direct model to predict the breakthrough curves. The acceptance/rejection algorithm used was Metropolis–Hastings and is presented below. − …”

Section: Methodsmentioning

confidence: 99%

Immobilization of Marbofloxacin for Water Treatment by Adsorption in Batch Scale and Fixed-Bed Column: Applying of Monte Carlo Bayesian Modeling

Jurado-Davila,

Oshiro,

Estumano

et al. 2024

Ind. Eng. Chem. Res.

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Pharmaceutical molecules identified in the wastewater belong to several classes of antibiotics such as marbofloxacin (MBX). This study focuses on the immobilization of MBX by adsorption in batch scale and a fixed-bed column of granular activated carbon (GAC). The effects of significant operational parameters, such as adsorbent dosage, contact time, and pH, on the adsorption efficiency were investigated. After this, a fixed-bed column study was carried out to evaluate the behavior of breakthrough curves, adsorption capacity, and bed saturation. Additionally, a novel strategy was incorporated that uses Monte Carlo Bayesian modeling to improve the ability to predict the most suitable conditions of the process. Results showed a high efficiency of MBX removal by the methods used where the most suitable conditions for batch scale were at natural pH, a GAC mass of 1.5 g, and 60 min of contact time for about 80% removal. Regarding fixed bed column experiments that were evidenced, the impact of the initial MBX concentration on the breakthrough curve impacted the adsorbent’s saturation rate, generating changes in the concentration gradient. In addition to the experimental data analysis, the model applied to simulate the breakthrough curves showed be robust, and it was capable of making good predictions, given that the lowest coefficient of determination (R 2) was 0.85. The mass balance model used was validated and was able to make good predictions when compared with the experimental data, so it can be used to make inferences about the breakthrough curve under different operating conditions within the range in which the experiments were carried out.

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“…In this work, a proposed distribution q(P*, P (i−1) ) was used to generate a new candidate parameter vector P*, dependent only on the current state but not explicitly on the previous states, P (i−1) . This new parameter vector P* = P (i−1) (1 + wε) was randomly generated using a Gaussian transition kernel, characterized by a mean P (i−1) and standard deviation wP (i−1) , where ε is a random variable drawn from a Gaussian probability distribution with a mean of zero and standard deviation equal to 1, denoted as 1, N (0.1) [27,[30][31][32][33][34][35][36][37][38][39][40][41].…”

Section: Markov Chain Monte Carlo Methodsmentioning

confidence: 99%

Use of Bayesian Methods in the Process of Uranium Bioleaching by Acidithiobacillus ferrooxidans

Cardoso,

Dias,

Moura

et al. 2023

Applied Sciences

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This research is focused on investigating the utilization of Bayesian methodologies, specifically the Markov Chain Monte Carlo method, as well as filter sampling by importance and sequential resampling. The objective is to estimate kinetic parameters and state variables associated with the uranium bioleaching process by Acidithiobacillus ferrooxidans. Experimental data of cell concentration, uranium concentration, and concentrations of ferrous and ferric ions, obtained from literature, were employed. These measurements were evaluated using a mathematical model expressed by a system of ordinary differential equations. Three different mathematical models were evaluated, considering different uncertainties in experimental measurements and mathematical models (1% and 5%). The estimation results presented a good fit to the experimental data, with coefficients of determination in the range of 0.95 to 0.99. The validation of the mathematical models was obtained by reproducing the experimental measurements and the Bayesian techniques proved to be suitable for application in the bioleaching process.

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Aerobic bioreactors: A Bayesian point of view applied to hydrodynamic characterization and experimental evaluation of tracers

Cited by 6 publications

References 30 publications

Classical and Bayesian Computational Statistics Applied to Acetylsalicylic Acid Mitigation in Aqueous Solutions: Study of Batch Scale, Kinetic Modeling, and Fixed-Bed Column Adsorption

Classical and Bayesian Computational Statistics Applied to Acetylsalicylic Acid Mitigation in Aqueous Solutions: Study of Batch Scale, Kinetic Modeling, and Fixed-Bed Column Adsorption

Immobilization of Marbofloxacin for Water Treatment by Adsorption in Batch Scale and Fixed-Bed Column: Applying of Monte Carlo Bayesian Modeling

Use of Bayesian Methods in the Process of Uranium Bioleaching by Acidithiobacillus ferrooxidans

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