Artificial Neural Networks, Optimization and Kinetic Modeling of Amoxicillin Degradation in Photo-Fenton Process Using Aluminum Pillared Montmorillonite-Supported Ferrioxalate Catalyst

2012 ◽  
Vol 51 (50) ◽  
pp. 16311-16319 ◽  
Author(s):  
O. B. Ayodele ◽  
H. S. Auta ◽  
N. Md Nor
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kinetic Modeling ◽  
Fenton Process ◽  
Pillared Montmorillonite ◽  
Artificial Neural ◽  
Neural Networks Optimization

Intelligence Selection System for Honing Parameter Based on Genetics and Artificial Neural Networks

2010 ◽  
Vol 102-104 ◽  
pp. 846-850
Author(s):  
Wen Yu Pu ◽  
Yan Nian Rui ◽  
Lian Sheng Zhao ◽  
Chun Yan Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Selection System ◽  
Process Data ◽  
Replacement Strategy ◽  
Machining Quality ◽  
Main Factors ◽  
Artificial Neural ◽  
Neural Networks Optimization ◽  
The Relationship

Appropriate selecting of process parameters influences the machining quality greatly. For honing, the main factors are product precision, material components and productivity. In view of this situation, a intelligence selection model for honing parameter based on genetics and artificial neural networks was built by using excellent robustness, fault-tolerance of artificial neural networks optimization process and excellent self-optimum of genetic algorithm. It can simulate the decision making progress of experienced operators, abstract the relationship from process data and machining incidence, realize the purpose of intelligence selection honing parameter through copying, exchanging, aberrance, replacement strategy and neural networks training. Besides, experiment was performed and the results helped optimize the theories model. Both the theory and experiment show the updated level and feasibility of this system.

scholarly journals Degradation of textile dyes Remazol Yellow Gold and reactive Turquoise: optimization, toxicity and modeling by artificial neural networks

2018 ◽  
Vol 2017 (3) ◽  
pp. 812-823 ◽  
Author(s):  
Graziele Elisandra do Nascimento ◽  
Daniella Carla Napoleão ◽  
Rayany Magali da Rocha Santana ◽  
Lívia Vieira Carlini Charamba ◽  
Julierme Gomes Correia de Oliveira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Aromatic Compounds ◽  
Salmonella Enteritidis ◽  
Degradation Process ◽  
Fenton Process ◽  
Artificial Neural ◽  
Nonlinear Kinetic ◽  
Sunlight Radiation ◽  
The Ideal

Abstract In this work, the degradation of Remazol Yellow Gold RNL-150% and Reactive Turquoise Q-G125 were investigated using AOP: photolysis, UV/H2O2, Fenton and photo-Fenton. It was found that the photo-Fenton process employing sunlight radiation was the most efficient, obtaining percentages of degradation above 87%. The ideal conditions for the degradation of the dyes were determined from a factorial design 23 and study of the [H2O2] ([H2O2] equal to 100 mg·L−1); [Fe] equal to 1 mg·L−1 and pH between 3 and 4. In the kinetic study, a degradation of more than 97% was obtained after 150 min for the chromophoric groups and 91% for the aromatic compounds. The experimental data obtained presented a good fit to the nonlinear kinetic model. The model of artificial neural networks multilayer perceptron (MLP) (4-11-5) using the software Statistica 8.0 enabled the modeling of the degradation process and showed a better prediction of the data. The toxicity to the seeds of Lactuca sativa and the bacteria Escherichia coli and Salmonella enteritidis allowed to evaluate the effectiveness of the process. The results of this study suggest that the use of photo-Fenton process with sunlight radiation is an effective way to degrade the dyes under study.

scholarly journals Degradation of the pharmaceuticals nimesulide and ibuprofen using photo-Fenton process: toxicity studies, kinetic modeling and use of artificial neural networks

2018 ◽  
Vol 22 ◽  
pp. 3 ◽  
Author(s):  
Rubens Teles Monteiro ◽  
Rayany Magali da Rocha Santana ◽  
Ana Maria Ribeiro Bastos da Silva ◽  
Alex Leandro Andrade de Lucena ◽  
Léa Elias Mendes Carneiro Zaidan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Surface Waters ◽  
Oxygen Demand ◽  
Aquatic Environments ◽  
Fenton Process ◽  
A Value ◽  
Linear Fit ◽  
Artificial Neural ◽  
The Mathematical Model

The growth of pollution in aquatic environments increases every day, causing compounds like pharmaceuticas to be detected in surface waters. Thus, tecniques such as advanced oxidation processes (AOP) have been used to degrade this compounds. In this work, the efficiency of AOP in the degradation of nimesulide and ibuprofen pharmaceuticals was evaluated through chromatographic analysis as well as organic matter through the levels of chemical oxygen demand (COD) and total organic carbon (TOC). It was verified that the photo-Fenton process presented the bests results, degrading 89.70% of nimesulide and 93.35% of ibuprofen. This same process managed to reduce COD by 91.60% and mineralize 90.04% of the TOC. The kinetic study showed a good linear fit (R2=0.993) for the clustered kinetic model, as well as a good fit to the mathematical model of artificial neural networks (ANNs), with a value of R2=1.000 for the MLP4-4-1 BFGS 4567 model. Finally, the toxicity of the solution after treatment was verified against the seeds of Lactuta sativa, Cichorium endívia, Ocimum basilicum and American Hard grain. It was found that the seeds that received the solution before treatment had a lower germination amount than the ones where the post AOP treatment solution was added. Then, the root growth was evaluated, in which a relative toxic effect was observed.

Sign in / Sign up

Export Citation Format

Share Document