Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate

Sabour, Mohammad Reza; Amiri, Allahyar

doi:10.1016/j.wasman.2017.03.048

Cited by 109 publications

(30 citation statements)

References 33 publications

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“…5(c), it can be seen that the BPANN model is very robust in predicting dye colour removal compared to the RSM model reported by Marabet et al. 26 The robustness of the BPANN model compared to that of RSM is consistent with the work of Sabour and Amiri, 42 who reported that ANN has better predictability than RSM in the modelling of Fenton treatment of landfill leachate. The residuals obtained from predictive modelling are within a 95% confidence level.…”

Section: Photodegradation Of Indolesupporting

confidence: 76%

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

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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

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Section: Photodegradation Of Indolesupporting

confidence: 76%

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

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“…Chemical oxygen demand (COD) was one of the effective variables in this study. Sabour et al [30] explored a comparative study of an ANN and RSM for simultaneous optimization of multiple targets in the Fenton treatment of landfill leachate. Three targets were used to cover different aspects of post-treatment products such as supernatant and sludge: mass content ratio (MCR) and mass removal efficiency (MRE).…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of the Fenton Process for the Treatment of Landfill Leachate Using an Artificial Neural Network

Roudi

Chelliapan

Mohtar

et al. 2018

Water

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In this study, the artificial neural network (ANN) technique was employed to derive an empirical model to predict and optimize landfill leachate treatment. The impacts of H 2 O 2 :Fe 2+ ratio, Fe 2+ concentration, pH and process reaction time were studied closely. The results showed that the highest and lowest predicted chemical oxygen demand (COD) removal efficiency were 78.9% and 9.3%, respectively. The overall prediction error using the developed ANN model was within −0.625%. The derived model was adequate in predicting responses (R 2 = 0.9896 and prediction R 2 = 0.6954). The initial pH, H 2 O 2 :Fe 2+ ratio and Fe 2+ concentrations had positive effects, whereas coagulation pH had no direct effect on COD removal. Optimized conditions under specified constraints were obtained at pH = 3, Fe 2+ concentration = 781.25 mg/L, reaction time = 28.04 min and H 2 O 2 :Fe 2+ ratio = 2. Under these optimized conditions, 100% COD removal was predicted. To confirm the accuracy of the predicted model and the reliability of the optimum combination, one additional experiment was carried out under optimum conditions. The experimental values were found to agree well with those predicted, with a mean COD removal efficiency of 97.83%.

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“…The past decade has witnessed numerous research activities in the field of nanofluids and their thermal performance. Most of these studies are devoted to the determination of thermal indices such as effective thermal conductivity under static conditions, heat transfer coefficient and heat transfer coefficient with phase change [15]. more research in the field of nanofluid by increasing particle mobility leads to a new science in relation to thermal conductivity.…”

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confidence: 99%

Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology

Petrudi

Fathi

Rahmani

2020

J. Mod. Sim. Mater.

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Heat transfer science is one of the most important and most applied engineering sciences, with the importance of energy management and energy conservation being doubled. Because of their properties, nanofluids have been widely used in various industries, making them particularly important to study. In this paper, the Nusselt number and coefficient of friction with volume fraction ranging from 0 to 0.1 at approximately Reynolds numbers of 200 to 5000 are studied experimentally. Higher thermal conductivity, better stability, lower pressure drop was observed using nanoparticles of solid particles. NSGA II algorithm was used to maximize Nusselt number and minimum friction coefficient by changing temperature and volume fraction of nanoparticles. To obtain Nusselt number and friction coefficient based on the temperature and volume fraction of the nanoparticles, the experimental data response surface methodology was used and with increasing Reynolds number, the Nusselt number increased and the friction coefficient decreased. In order to evaluate the objective functions in the optimization, the response surface methodology is attached to the optimization algorithm. At the end, the Pareto Front and its corresponding optimal points are presented.

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Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate

Cited by 109 publications

References 33 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

Prediction and Optimization of the Fenton Process for the Treatment of Landfill Leachate Using an Artificial Neural Network

Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology

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Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate

Cited by 109 publications

References 33 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

Prediction and Optimization of the Fenton Process for the Treatment of Landfill Leachate Using an Artificial Neural Network

Multi-objective Optimization to Increase Nusselt Number and Reduce Friction Coefficient of Water/Carbon Nanotubes via NSGA II using Response Surface Methodology

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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