Phenazopyridine degradation by electro-Fenton process with magnetite nanoparticles-activated carbon cathode, artificial neural networks modeling

Gholizadeh, Amir Mohammad; Zarei, Mahmoud; Ebratkhahan, Masoud; Hasanzadeh, Aliyeh

doi:10.1016/j.jece.2020.104999

Cited by 45 publications

(11 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The elaborated explanation for the sample preparation and intermediate identification has been presented elsewhere. 27 The total organic carbon concentration was measured by a TOC analyzer (multi N/C 2100S, Analytik Jena, Germany). The concentration of anions was determined by a Dionex ICS-2100 ion chromatography (IC) system (Thermo Fisher, USA).…”

Section: Methodsmentioning

confidence: 99%

“…Degradation by-products was recognized by Agilent 6890 gas chromatography (GC, Canada) combined with an Agilent 5973 mass spectrometer (MS, Canada), which had a capillary column of TG-5MS. The elaborated explanation for the sample preparation and intermediate identification has been presented elsewhere . The total organic carbon concentration was measured by a TOC analyzer (multi N/C 2100S, Analytik Jena, Germany).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Synthesis of N-Doped Magnetic WO_3–x@Mesoporous Carbon Using a Diatom Template and Plasma Modification: Visible-Light-Driven Photocatalytic Activities

Gholami

Khataee

Bhatnagar

et al. 2021

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

Synthesis of three-dimensional photocatalysts offers great potential for chemical conversion and hydrogen generation as appropriate solutions for environmental protection and energy shortage challenges. In this study, the magnetic WO 3−x @ mesoporous carbon (M-WO 3−x @MC) was synthesized through the evaporation-induced self-assembly method applying diatom frustules as a natural template. Then, plasma modification was used to prepare the N-doped M-WO 3−x @MC (NM-WO 3−x @MC) with enhanced photocatalytic activity and durable performance. The WO 3−x was embedded in the conductive MC, which was also partially reduced by the carbon precursor within the heat-treatment procedure. The obtained M-WO 3−x @MC was treated by the plasma under an N 2 atmosphere for the production of the final photocatalyst containing both the N-doped WO 3−x and MC. As a result, the NM-WO 3−x @MC had larger surface area (208.4 m 2 g −1 ), narrower band gap (2.3 eV), more visible light harvesting, and confined electron−hole pairs recombination. The H 2 generation rates of net WO 3 nanorods and NM-WO 3−x @MC nanocomposite were estimated as 532 and 2765 μmol g −1 h −1 , respectively. Additionally, more than 90% of antibiotics (cephalexin, cefazolin and cephradine) degradation and 76% of total organic carbon elimination were obtained after 120 and 240 min of photocatalytic process under visible light irradiation. Eventually, more than eight intermediates were detected for each antibiotic degradation using the gas chromatography−mass spectrometer method, and based on the obtained results, the possible degradation pathways were suggested.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Synthesis of N-Doped Magnetic WO_3–x@Mesoporous Carbon Using a Diatom Template and Plasma Modification: Visible-Light-Driven Photocatalytic Activities

Gholami

Khataee

Bhatnagar

et al. 2021

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

show abstract

“…These models are optimized through linear and exponential regression [6,8,9]. Some work along these line incorporates neural networks that are responsible for generating concentration curves over time [30,31,32,33]. These methods outperform the latter by being capable of optimizing multiple parameters, but they do not allow to obtain an analytical expression of the model.…”

Section: Related Workmentioning

confidence: 99%

Deep reinforcement learning for automated search of model parameters: photo-fenton wastewater disinfection case study

Hernández-García

Cuesta-Infante

Moreno-SanSegundo

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

Numerical optimization solves problems that are analytically intractable at the cost of arriving at a sufficiently good but rarely optimal solution. To maximize the result, optimization algorithms are run with the guidance and supervision of a human, usually an expert in the problem. Recent advances in deep reinforcement learning motivate interest in an artificial agent capable of learning to do the expert’s task. Specifically, we present a proximal policy optimization agent that learns to optimize in a real case study such as the modeling of the photo-fenton disinfection process, which involves a number of parameters that have to be adjusted to minimize the error of the model with respect to the experimental data collected in several trials. The expert spends an average of 4 h to find a suitable set of parameters. On the other hand, the agent we present does not require a human expert to guide or validate the optimization procedure and achieves similar results in $$2.5\times$$ 2.5 × less time.

show abstract

“…While most of these approaches include statistical-based models like RSM, machine learning-based models such as ANNs have been started to apply as an alternative modelling and prediction tool. Although ANNs are the subject of many studies for time series prediction (Egrioglu et al 2013;Cagcag Yolcu et al 2018;Yolcu et al 2019), there are limited studies in the literature that use different ANN types to model Fenton-based processes (Elmolla et al 2010;Zarei et al 2010;Jaafarzadeh et al 2012;Sabour and Amiri 2017;Radwan et al 2018;Baştürk and Alver 2019;Talwar et al 2019;Tolba et al 2019;Gholizadeh et al 2021). Modelling a dependent variable through certain independent variables is essentially done to predict this dependent variable at different and especially nonexistent independent variable values.…”

Section: Introductionmentioning

confidence: 99%

Combination of ANNs and heuristic algorithms in modelling and optimizing of Fenton processes for industrial wastewater treatment

Cüce

Yolcu

Temel

2022

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

In this study, it is aimed to evaluate COD removal performance of Classical-Fenton and Photo-Fenton Processes from cosmetic wastewater by different prediction models. Besides Response Surface Methodology (RSM), three neural networks were used to more reliably and effectively predict the behavior of dependent variable at different values of relevant parameters. These neural networks; multi-layer perceptron trained by Levenberg-Marquardt (MLP-LM); multi-layer perceptron and single multiplicative neuron model trained by particle swarm optimization algorithm (MLP-PSO; SMN-PSO). H2O2 doses, Fe(II) doses, and H2O2/Fe(II) rates were independent variables of prediction models to optimize both processes in batch reactors. The generated predictions for whole data set were compared with each other. The prediction performances of models were evaluated by RMSE and MAPE error criteria. Regression analysis was also applied to determine the performance of the best model.The results obtained from all prediction tools showed that the model produces the best predictive results in almost all cases is SMN-PSO model in terms of both criteria. In addition, the genetic algorithm was utilized for SMN-PSO model results to find the optimum values of the study. Thus, without the need to perform many different experiments, the optimum parameter values can be determined to get maximum removal ratios.

show abstract

Phenazopyridine degradation by electro-Fenton process with magnetite nanoparticles-activated carbon cathode, artificial neural networks modeling

Cited by 45 publications

References 67 publications

Synthesis of N-Doped Magnetic WO_3–x@Mesoporous Carbon Using a Diatom Template and Plasma Modification: Visible-Light-Driven Photocatalytic Activities

Synthesis of N-Doped Magnetic WO_3–x@Mesoporous Carbon Using a Diatom Template and Plasma Modification: Visible-Light-Driven Photocatalytic Activities

Deep reinforcement learning for automated search of model parameters: photo-fenton wastewater disinfection case study

Combination of ANNs and heuristic algorithms in modelling and optimizing of Fenton processes for industrial wastewater treatment

Contact Info

Product

Resources

About

Phenazopyridine degradation by electro-Fenton process with magnetite nanoparticles-activated carbon cathode, artificial neural networks modeling

Cited by 45 publications

References 67 publications

Synthesis of N-Doped Magnetic WO3–x@Mesoporous Carbon Using a Diatom Template and Plasma Modification: Visible-Light-Driven Photocatalytic Activities

Synthesis of N-Doped Magnetic WO3–x@Mesoporous Carbon Using a Diatom Template and Plasma Modification: Visible-Light-Driven Photocatalytic Activities

Deep reinforcement learning for automated search of model parameters: photo-fenton wastewater disinfection case study

Combination of ANNs and heuristic algorithms in modelling and optimizing of Fenton processes for industrial wastewater treatment

Contact Info

Product

Resources

About

Synthesis of N-Doped Magnetic WO_3–x@Mesoporous Carbon Using a Diatom Template and Plasma Modification: Visible-Light-Driven Photocatalytic Activities

Synthesis of N-Doped Magnetic WO_3–x@Mesoporous Carbon Using a Diatom Template and Plasma Modification: Visible-Light-Driven Photocatalytic Activities