Investigation of 2-nitrophenol solar degradation in the simultaneous presence of K2S2O8 and H2O2: Using experimental design and artificial neural network

Bararpour, S. Toufigh; Feylizadeh, Mohammad Reza; Delparish, Amin; Qanbarzadeh, Mojtaba; Raeiszadeh, Milad; Feilizadeh, Mehrzad

doi:10.1016/j.jclepro.2017.11.191

Cited by 65 publications

(20 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study revealed that the ANN model displayed a higher prediction of the phenol degradation compared to the RSM model. Similarly, Bararpour et al 46 also reported the use of three-layer ANN architecture for modelling the prediction of 2-nitophenol photodegradation. The model analysis revealed that ANN presented a better prediction of the 2-nitrophenol phenol compared to the RSM model.…”

Section: Photodegradation Of Methyl Bluementioning

confidence: 98%

Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO₂‐based photocatalysts

Ayodele

Alsaffar

Mustapa

et al. 2020

J of Chemical Tech & Biotech

View full text Add to dashboard Cite

BACKGROUND The advanced oxidation process using photocatalysts has been proven to be an efficient technique used for the degradation of organic pollutants in wastewater. However, there exists a nonlinear relationship between the process parameters of the photodegradation reaction, which needs to be well understood for the design of an efficient photoreactor. This study employed a backpropagation artificial neural network (BPANN) for the modelling of photocatalytic degradation of indole, anthraquinone dye and methyl blue using undoped and Ag+‐doped TiO2 catalysts. RESULTS A Levenberg–Marquardt algorithm was employed to train the BPANN by varying the hidden neurons to obtained an optimized architecture. Optimized architectures with 3‐14‐1, 4‐12‐1 and 3‐16‐1 consist of the input layers, hidden layer and the output layer, were obtained using the datasets from photodegradation of indole, anthraquinone dye and methyl blue, respectively. The optimized BPANN accurately predicts the indole, anthraquinone dye and methyl blue degradation as a function of colour removal from the wastewater. High coefficients of determination (R2) of 0.999, 0.961 and 0.993 were obtained for the prediction of the photodegradation of indole, anthraquinone dye and methyl blue, respectively, with over 95% confidence level. The study revealed that dye concentration, catalyst dosage and reaction time have the highest level of importance for the photodegradation of indole, anthraquinone dye and methyl blue, respectively. CONCLUSION This study has demonstrated the robustness of BPANN for predictive modelling of photodegradation of organic pollutants such as indole, anthraquinone dye and methyl blue. © 2020 Society of Chemical Industry

show abstract

Section: Photodegradation Of Methyl Bluementioning

confidence: 98%

Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO₂‐based photocatalysts

Ayodele

Alsaffar

Mustapa

et al. 2020

J of Chemical Tech & Biotech

View full text Add to dashboard Cite

show abstract

“…The F-values and P-values of the model's variables were used to compare the impact of each variable on the final response. Smaller P-values and larger Fvalues indicate the higher impact of the corresponding variables on the response [25,44]. For instance, carbonation time had smaller P-value and larger F-value than the other independent variables, thus, caused a higher impact on the CO 2 capture capacity.…”

Section: Modelingmentioning

confidence: 99%

“…One approach to reducing the hydrophilicity is to modify the structure of Al 2 O 3 by shelling it with less hydrophilic material and forming a core-shell composite. To the best of authors' knowledge, this is the first study that investigates the effect of core-shell-structured supports on the hydrophilicity of the Response surface methodology (RSM) is a useful tool to optimize the effect of operating parameters with the minimum number of experiments [24,25]. RSM evaluates the interaction of operating parameters using an experimental design such as full factorial, central composite and Box Behnken [26].…”

Section: Introductionmentioning

confidence: 99%

Application of Core-Shell-Structured K₂CO₃-Based Sorbents in Postcombustion CO₂ Capture: Statistical Analysis and Optimization Using Response Surface Methodology

2020

Self Cite

View full text Add to dashboard Cite

This study investigates the effect of core-shell-structured supports prepared with alumina as the core on the CO 2 capture performance of K 2 CO 3 . One main issue in using alumina-basedsupported K 2 CO 3 is the high moisture uptake of the sorbent, which converts active sites of K 2 CO 3 to hydrated by-products with a very low CO 2 capture capacity. To address this issue, the support was shelled with a less hydrophilic material using a core-shell technique. Six core-shell-structured supports were prepared using alumina-based cores (γ-alumina and Boehmite), and TiO 2 , ZrO 2 and SiO 2 shells. K 2 CO 3 was impregnated on each support and tested in a thermogravimetric analyzer over ten cycles. K 2 CO 3 /Boehmite/TiO 2 showed the lowest moisture uptake and the highest surface area, and thus the best CO 2 capture performance. A semi-empirical model was developed using a response surface methodology

show abstract

“…Therefore, most of the corresponding models have been only developed based on the statistical analysis of experimental data. [32][33][34][35][36][37] A limited number of kinetic studies have been performed for gasphase photocatalytic hydrogen generation from substrates such as carbon dioxide and water vapor 38 as well as alcohols. 39 Nevertheless, regarding photocatalytic hydrogen production in liquid phase of substrates (hydrogen sources), all of the observed studies simply used the Langmuir-Hinshelwood (L-H) model (Equation (1)) to develop their kinetic models.…”

Section: Depositionmentioning

confidence: 99%

“…Notwithstanding this, the fundamentals of photocatalytic processes are not yet found comprehensively to obtain a reliable kinetic model. Therefore, most of the corresponding models have been only developed based on the statistical analysis of experimental data …”

Section: Introductionmentioning

confidence: 99%

An intrinsic kinetic model for liquid‐phase photocatalytic hydrogen production

2019

Self Cite

View full text Add to dashboard Cite

A kinetic study was accomplished to describe the photocatalytic production of hydrogen in liquid phase. A reaction mechanism and a kinetic model were proposed to predict the rate of hydrogen production, which is a function of light intensity, catalyst loading, substrate concentration, and time. To assess the capability of the proposed model, glycerol and ethanol were selected as representative hydrogen sources (substrates). The experimental data performed under different operating conditions, based on Box-Behnken experimental design, were used to train the developed kinetic model, optimize the parameters using genetic algorithms and check its accuracy. The analysis confirms the validity of the model under different operating conditions. In addition, the ability of the model to predict the rate of hydrogen production for other substrates, photocatalysts, and operating conditions was confirmed by comparing model predictions with experimental data from literature.

show abstract

Investigation of 2-nitrophenol solar degradation in the simultaneous presence of K2S2O8 and H2O2: Using experimental design and artificial neural network

Cited by 65 publications

References 46 publications

Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO₂‐based photocatalysts

Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO₂‐based photocatalysts

Application of Core-Shell-Structured K₂CO₃-Based Sorbents in Postcombustion CO₂ Capture: Statistical Analysis and Optimization Using Response Surface Methodology

An intrinsic kinetic model for liquid‐phase photocatalytic hydrogen production

Contact Info

Product

Resources

About

Investigation of 2-nitrophenol solar degradation in the simultaneous presence of K2S2O8 and H2O2: Using experimental design and artificial neural network

Cited by 65 publications

References 46 publications

Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2‐based photocatalysts

Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO2‐based photocatalysts

Application of Core-Shell-Structured K2CO3-Based Sorbents in Postcombustion CO2 Capture: Statistical Analysis and Optimization Using Response Surface Methodology

An intrinsic kinetic model for liquid‐phase photocatalytic hydrogen production

Contact Info

Product

Resources

About

Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO₂‐based photocatalysts

Backpropagation neural networks modelling of photocatalytic degradation of organic pollutants using TiO₂‐based photocatalysts

Application of Core-Shell-Structured K₂CO₃-Based Sorbents in Postcombustion CO₂ Capture: Statistical Analysis and Optimization Using Response Surface Methodology