Modeling and optimization of photocatalytic treatment of landfill leachate using tungsten-doped TiO2 nano-photocatalysts: Application of artificial neural network and genetic algorithm

Azadi, Sama; Karimi-Jashni, Ayoub; Javadpour, Sirus

doi:10.1016/j.psep.2018.03.038

Cited by 46 publications

(16 citation statements)

References 57 publications

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“…The MLP structure used for estimating the esterification yield as dependent variables and independent variables of acid ratio, gelling temperature, complex time, and calcination temperature is depicted in Figure 10. The MLP neural network consists of three layers: an input layer, a hidden layer, and an output layer [42]. In our study, an input layer has four neurons, each of which represents an independent variable.…”

Section: Multi-layer Perceptron (Mlp) Modelmentioning

confidence: 99%

“…tion temperature is depicted in Figure 10. The MLP neural network consists of three layers: an input layer, a hidden layer, and an output layer [42]. In our study, an input layer has four neurons, each of which represents an independent variable.…”

Section: Multi-layer Perceptron (Mlp) Modelmentioning

confidence: 99%

“…The statistical methods, such as response surface method (RSM) [38,39], Taguchi [40], and Box-behnken [41] have rarely been studied in the context of optimising the catalyst preparation conditions. In addition, the limitations of these methods, such as non-applicability of the proposed model outside of the studied range of factors, non-applicability for optimising the problems of highly non-linear multiple objectives, and non-applicability for non-quadratic approximations, artificial neural networks (ANNs) combined with the genetic algorithm (GA) are able to suitably analyse, model and optimise the process parameters [42]. Although a few studies have evaluated the preparation conditions of heterogeneous catalysts via ANN and the process of optimising the parameters using GA [43,44], a lack of study can be observed regarding the preparation conditions of binary or ternary systems of metal oxides.…”

Section: Introductionmentioning

confidence: 99%

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Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

et al. 2022

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Design and fabrication of a catalyst with the highest activity can be achieved by optimising the synthesis conditions. In this study, the sol-gel synthesis conditions of citric acid concentration, gelling temperature, complex time, and calcination temperature were studied for the preparation of a novel semi-hexagonal calcium/titania-zirconia nano-catalyst used in the esterification reaction. After synthesis of around 24 samples at various conditions, their activity was tested in the esterification reaction and the results were analysed by multi-layer perceptron (MLP) and support vector machine (SVM) models. Both models predicted the actual data with high coefficients of determination, and indicated that the calcination temperature has the most influence on the activity of the prepared semi-hexagonal calcium/titania-zirconia nano-catalyst for the esterification reaction. Moreover, the genetic algorithm (GA) was utilised for optimising the preparation conditions based on the SVM model, due to its higher generalisation capability for prediction. The prepared nano-catalysts under the optimum conditions of 1.42 acid ratio, gelling temperature of 72 °C, complex time of 2.65 h, and calcination temperature of 487 °C showed good crystalline structure and metal–metal and metal–oxygen cation bonding. Finally, the fabricated catalyst had a high surface area (276.5 m2/g) with 3.5 nm pore diameter and almost uniform particle size (80–110 nm) distribution, leading to a high conversion of 97.6% in the esterification reaction, with good catalytic stability up to five times.

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Section: Multi-layer Perceptron (Mlp) Modelmentioning

confidence: 99%

Section: Multi-layer Perceptron (Mlp) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

et al. 2022

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“…Advanced oxidation processes (AOPs) are promising, relatively efficient, and ecological-friendly developments suitable for pharmaceuticals degradation [17][18][19]. Highly reactive oxidizing agents (ROS) species, including radical superoxide (O2 •-) and ( • OH) hydroxyl radicals, could be generated in AOPs, and complicated organic molecules can be efficiently oxidized into more specific products [20,21]. Heterogeneous photocatalysis is also widely utilized in AOPs to degrade various forms of recalcitrant organic contaminants, such as phenolic substances, pesticides, and pharmaceuticals [22,23].…”

Section: Advanced Oxidation Processes (Aops)mentioning

confidence: 99%

The Treatment of Wastewater Containing Pharmaceuticals

2021

J. Environ. Treat. Tech.

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The massive production of pharmaceuticals and excessive consumption will lead to their leakage into various water sources. Conventional treatment methods have proven ineffective in the treatment of these contaminants. Thus, choosing the appropriate treatment method is extremely important to deal with these pollutants. This review presents an overview of pharmaceuticals in wastewater and studies the difference between the conventional and advanced oxidation processes (AOPs) for pharmaceutical treatment. AOPs can be an ideal solution for the degradation of these contaminants. The factors that affect the removal efficiency for AOPs were discussed, such as type of catalyst, light intensity, initial concentration of contamination, catalyst dose, and pH of the solution. The degradation pathway for some pharmaceuticals has also been discussed.

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“…Karimi-Jashni et al [47] applied multilayer ANN FF for prediction of Chemical Oxygen Demand (COD) photocatalytic degradation, that is used as a variable for landfill leachate. They used tungsten doped TiO 2 nano-photocatalyst for the reaction performance.…”

Section: Ann In Photocatalysismentioning

confidence: 99%

Modelling of Tio2 Properties for the Band Gap Prediction Using Artificial Neural Networks

FIDALGO¹

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Modeling and optimization of photocatalytic treatment of landfill leachate using tungsten-doped TiO2 nano-photocatalysts: Application of artificial neural network and genetic algorithm

Cited by 46 publications

References 57 publications

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

Modelling and Optimisation of the Sol-Gel Conditions for Synthesis of Semi-Hexagonal Titania-Based Nano-Catalyst for Esterification Reaction

The Treatment of Wastewater Containing Pharmaceuticals

Modelling of Tio2 Properties for the Band Gap Prediction Using Artificial Neural Networks

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