Evaluating the Biodegradation of Veterinary Antibiotics Using Kinetics Model and Response Surface Methodology

Chollom, Martha Noro; Bakare, Babatunde Femi; Rathilal, Sudesh; Tetteh, Emmanuel Kweinor

doi:10.3390/molecules27175402

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Moreover, RSM uses a relatively small number of experiments to build a model of the system, reducing the overall experimental effort required to optimize the process [ 30 , 31 ]. In particular, it can be used to predict the optimal conditions for the process, saving time and resources by avoiding trial-and-error experimentation [ 32 ]. The interacting impacts of the independent variables on the process can be assessed using RSM [ 33 , 34 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

Response Methodology Optimization and Artificial Neural Network Modeling for the Removal of Sulfamethoxazole Using an Ozone–Electrocoagulation Hybrid Process

Nghia,

Tuyen,

Quynh

et al. 2023

Molecules

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Removing antibiotics from water is critical to prevent the emergence and spread of antibiotic resistance, protect ecosystems, and maintain the effectiveness of these vital medications. The combination of ozone and electrocoagulation in wastewater treatment provides enhanced removal of contaminants, improved disinfection efficiency, and increased overall treatment effectiveness. In this work, the removal of sulfamethoxazole (SMX) from an aqueous solution using an ozone–electrocoagulation (O–EC) system was optimized and modeled. The experiments were designed according to the central composite design. The parameters, including current density, reaction time, pH, and ozone dose affecting the SMX removal efficiency of the OEC system, were optimized using a response surface methodology. The results show that the removal process was accurately predicted by the quadric model. The numerical optimization results show that the optimum conditions were a current density of 33.2 A/m2, a time of 37.8 min, pH of 8.4, and an ozone dose of 0.7 g/h. Under these conditions, the removal efficiency reached 99.65%. A three-layer artificial neural network (ANN) with logsig-purelin transfer functions was used to model the removal process. The data predicted by the ANN model matched well to the experimental data. The calculation of the relative importance showed that pH was the most influential factor, followed by current density, ozone dose, and time. The kinetics of the SMX removal process followed the first-order kinetic model with a rate constant of 0.12 (min−1). The removal mechanism involves various processes such as oxidation and reduction on the surface of electrodes, the reaction between ozone and ferrous ions, degradation of SMX molecules, formation of flocs, and adsorption of species on the flocs. The results obtained in this work indicate that the O–EC system is a potential approach for the removal of antibiotics from water.

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Section: Introductionmentioning

confidence: 99%

Response Methodology Optimization and Artificial Neural Network Modeling for the Removal of Sulfamethoxazole Using an Ozone–Electrocoagulation Hybrid Process

Nghia,

Tuyen,

Quynh

et al. 2023

Molecules

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Central Composite Design Optimisation of Banana Peels/Magnetite for Anaerobic Biogas Production from Wastewater

et al. 2022

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Biogas production from wastewater as a function to curb waste and provide energy security has gained worldwide attention. Ensuring the stability of anaerobic digestion (AD) of physiochemical and biological complexity necessitates optimization. In this study, a central composite design (CCD) from a response surface methodology (RSM) was employed to evaluate and optimize the effects of bio-stimulation of banana peels coupled with magnetite on the anaerobic digestion of wastewater to produce biogas. An experimental matrix of 14 runs using the CCD, with two factors (nanoparticle and biochar load) as a function of pH, biogas production, and COD removal by the AD process was operated at a constant mesophilic temperature (37 °C) for 28 days. The analysis of variance (ANOVA) showed that the quadratic models attained were significant (p-values < 0.05) with a high coefficient of determination (R2) values closer to 1. The optimized conditions, including nanoparticle (0.46 g) and biochar (0.66 mgVS/L), resulted in biogas production (19.26 mL/day), pH (7.07), and COD removal (75.17%). This suggests 100% desirability at a 95% confidence level. This finding depicts the trade-off between biogas productivity, biodegradability, and stability of the AD process established for future consideration of using nanoparticles as bio-stimulant.

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Evaluating the Biodegradation of Veterinary Antibiotics Using Kinetics Model and Response Surface Methodology

Cited by 2 publications

References 25 publications

Response Methodology Optimization and Artificial Neural Network Modeling for the Removal of Sulfamethoxazole Using an Ozone–Electrocoagulation Hybrid Process

Response Methodology Optimization and Artificial Neural Network Modeling for the Removal of Sulfamethoxazole Using an Ozone–Electrocoagulation Hybrid Process

Central Composite Design Optimisation of Banana Peels/Magnetite for Anaerobic Biogas Production from Wastewater

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