Study of oxytetracycline degradation by means of anodic oxidation process using platinized titanium (Ti/Pt) anode and modeling by artificial neural networks

Belkacem, S.; Bouafia, Souad; Chabani, Malika

doi:10.1016/j.psep.2017.07.007

Cited by 45 publications

(16 citation statements)

References 40 publications

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“…(1). The use of Pt/Ti anode for anodic oxidation process of pollutants oxidation is a proven technology for the different pollutants oxidation by direct oxidation as well

true normalM + normalH_{2} normalO \to normalM (HO^{•}) + normalH^{+} + normale^{-}

…”

Section: Resultsmentioning

confidence: 99%

Treatment of Arsenite‐Contaminated Water by Electrochemical Advanced Oxidation Processes

et al. 2020

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Arsenite removal from aqueous medium is quite challenging as most of the conventional methods fail to exhibit arsenite removal completely. The present article examines the possibility of electrochemical advanced oxidation processes to remove arsenite from water medium. Anodic oxidation and electro‐Fenton processes failed to remove generated arsenate in the system but found effective to oxidize arsenite completely from its initial concentrations of 1 mg/L and 10 mg/L. Arsenite oxidation is mainly due to in‐situ generated hydrogen peroxide and hydroxyl radicals for anodic oxidation and electro‐Fenton processes. However, peroxicoagulation process was found effective for the oxidation of arsenite as well as removal of arsenic in water. Complete removal of arsenic was observed within 30 min of electrolysis time for pH range in between 3 to 11. Overall, peroxicoagulation is an effective way to remove arsenic from aqueous solution.

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“…(1). The use of Pt/Ti anode for anodic oxidation process of pollutants oxidation is a proven technology for the different pollutants oxidation by direct oxidation as well

true normalM + normalH_{2} normalO \to normalM (HO^{•}) + normalH^{+} + normale^{-}

…”

Section: Resultsmentioning

confidence: 99%

Treatment of Arsenite‐Contaminated Water by Electrochemical Advanced Oxidation Processes

et al. 2020

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“…Therefore, we could get a conclusion that BP neural network model could simulate and reproduce the application of ionic liquid in the extraction of triterpenoid acid from G. Lucidum with high accuracy. The weights are coefficients between the artificial neurons, which are analogous to synapse strengths between the axons and dendrites of real biological neurons [30]. In Table 5, the internodes weights produced by the ANN model were used in this work.…”

Section: Ann Model Developmentmentioning

confidence: 99%

“…Artificial neural network (ANNs) is an information processing system based on imitating brain function, which can quickly and accurately realize the simulation and prediction of data information [30]. ANNs reduces the time of experiment and model development without the mathematical description of the phenomena involved in the process [31,32].…”

Section: Introductionmentioning

confidence: 99%

Ionic Liquid-Based Ultrasonic-Assisted Extraction Coupled with HPLC and Artificial Neural Network Analysis for Ganoderma lucidum

Cui

Lü

et al. 2020

Molecules

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Ganoderma lucidum is widely used in traditional Chinese medicine (TCM). Ganoderic acid A and D are the main bioactive components with anticancer effects in G. lucidum. To obtain the maximum content of two compounds from G. lucidum, a novel extraction method, an ionic liquid-based ultrasonic-assisted method (ILUAE) was established. Ionic liquids (ILs) of different types and parameters, including the concentration of ILs, ultrasonic power, ultrasonic time, rotational speed, solid–liquid ratio, were optimized by the orthogonal experiment and variance analysis. Under these optimal conditions, the total extraction yield of the two compounds in G. lucidum was 3.31 mg/g, which is 36.21% higher than that of the traditional solvent extraction method. Subsequently, an artificial neural network (ANN) was developed to model the performance of the total extraction yield. The Levenberg–Marquardt back propagation algorithm with the sigmoid transfer function (logsig) at the hidden layer and a linear transfer function (purelin) at the output layer were used. Results showed that single hidden layer with 9 neurons presented the best values for the mean squared error (MSE) and the correlation coefficient (R), with respectively corresponding values of 0.09622 and 0.93332.

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“…However, they have been oriented to address the kinetic modeling from experimental design perspectives: the ANN is used to model a measure of the treatment performance (the total removal of contaminant, the final H 2 O 2 concentration, etc.) at the final time of the treatment as a function of the working conditions (Nascimento et al, 1994;Duran et al, 2006;Guimarães et al, 2007;Jaafarzadeh et al, 2012;Khataee et al, 2014;Hassani et al, 2015;De Tuesta et al, 2015;Expósito et al, 2017;Belkacem et al, 2017).…”

Section: Application To a Photo-fenton Batch Processmentioning

confidence: 99%

Data-driven soft-sensors for online monitoring of batch processes with different initial conditions

Shokry

Vicente

Escudero

et al. 2018

Computers & Chemical Engineering

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A soft-sensing methodology applicable to batch processes operated under changeable initial conditions is presented. These cases appear when the raw materials specifications differ from batch to batch, different production scenarios should be managed, etc. The proposal exploits the capabilities of the machine learning techniques to provide practical soft-sensing approach with minimum tuning effort in spite of the fact that the inherent dynamic behavior of batch systems are tracked through other online indirect measurements. Current data modelling techniques have been also tested within the proposed methodology to demonstrate its advantages. Two simulation casestudies and a pilot-plant case-study involving a complex batch process for wastewater treatment are used to illustrate the problem, to assess the modelling approach and to compare the modelling techniques. The results reflect a promising accuracy even when the training information is scarce, allowing significant reductions in the cost associated to batch processes monitoring and control.

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Study of oxytetracycline degradation by means of anodic oxidation process using platinized titanium (Ti/Pt) anode and modeling by artificial neural networks

Cited by 45 publications

References 40 publications

Treatment of Arsenite‐Contaminated Water by Electrochemical Advanced Oxidation Processes

Treatment of Arsenite‐Contaminated Water by Electrochemical Advanced Oxidation Processes

Ionic Liquid-Based Ultrasonic-Assisted Extraction Coupled with HPLC and Artificial Neural Network Analysis for Ganoderma lucidum

Data-driven soft-sensors for online monitoring of batch processes with different initial conditions

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