Application of artificial neural networks for modeling of the treatment of wastewater contaminated with methyl tert-butyl ether (MTBE) by UV/H2O2 process

Salari, Dariush; Daneshvar, N.; Aghazadeh, Faezeh; Khataee, Alireza

doi:10.1016/j.jhazmat.2005.05.030

Cited by 134 publications

(64 citation statements)

References 25 publications

(40 reference statements)

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“…Artificial neural networks can be used to investigate the influence of parameters on the process without understanding the actual phenomena [10]. To optimize costs or efficiency, response surface methodologies can be used [11,12].…”

Section: Omentioning

confidence: 99%

“…The main difference with hydrogen peroxide actinometry was that the calibration was not performed in ultra pure water, but by using the real water matrix with a known absorbance.The initial value of I 0 was calculated according to the nominal power input of the lamps (data from the manufacturer), assuming that the LP-UV lamps have an efficiency of 33% [20]. (10) in which J(θ) represents the objective function based on N data points and y ij and ŷ ij (θ) represent the model prediction and experimental data of variable j, respectively. w j is the weight factor applied to the variables .…”

Section: Model Calibration and Validationmentioning

confidence: 99%

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Application of a mechanistic UV/hydrogen peroxide model at full-scale: Sensitivity analysis, calibration and performance evaluation

Audenaert

Vermeersch

Hulle

et al. 2011

Chemical Engineering Journal

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Numerous mechanistic models describing the UV/H 2 O 2 process have been proposed in literature. In this study, one of them was used to predict the behavior of a full-scale reactor. The model was calibrated and validated with nonsynthetic influent using different operational conditions. A local sensitivity analysis was conducted to determine the most important operational and chemical model parameters. Based on the latter, the incident UV irradiation intensity and two kinetic rate constants were selected for mathematical estimation. In order to investigate changes of the NOM content over time, some time delay was considered between calibration and validation data collection. Hydrogen peroxide concentration, the decadic absorption coefficient at 310 nm (UVA 310 , as a surrogate for natural organic matter) and pH could be satisfactorily predicted during model validation using an independent data set. It was demonstrated that quick real-time calibration is an option at less controllable full-scale conditions. The reactivity of UVA 310 towards hydroxyl radicals did not show significant variations over time suggesting no need for frequent recalibration. Parameters that determine the initiation step, i.e. photolysis of hydrogen peroxide, have a large impact on most of the variables. Some reaction rate constants were also of importance, but nine kinetic constants did show absolutely no influence to one of the variables. Parameters related to UV shielding by NOM were of main importance. At the conditions used in this study, i.e. H 2 O 2 concentrations between 0.5 and 4 mM, hydraulic residence times between 90 and 200 s and alkalinity concentrations between 2.5 and 6 mM, competitive radiation absorption by NOM was more detrimental to the micro pollutant removal efficiency than hydroxyl radical scavenging. Hydrogen peroxide concentration was classified as a non-sensitive variable, in contrast to the concentration of a micro pollutant which showed to be very to extremely influential to many of the parameters. UV absorption as a NOM surrogate is a promising variable to be included in future models. Model extension by splitting up the UVA 310

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Section: Omentioning

confidence: 99%

Section: Model Calibration and Validationmentioning

confidence: 99%

Application of a mechanistic UV/hydrogen peroxide model at full-scale: Sensitivity analysis, calibration and performance evaluation

Audenaert

Vermeersch

Hulle

et al. 2011

Chemical Engineering Journal

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“…The R 2 value found in this study was relatively higher than those reported in the literature; a coefficient of determination of 0.92 for prediction of CH 4 emission ventilation from longwall mines (Karacan, 2008), 0.90 for prediction of the CH 4 fraction of LFG (Ozkaya et al, 2007), 0.90 for prediction of the quantitative characteristics of water bodies (Palani et al, 2008), and 0.90 for prediction of performance of a wastewater treatment plant (Nasr et al, 2012). However, some previous studies achieved R 2 values up to 0.99 (Elmolla et al, 2010;Oguz et al, 2008;Salari et al, 2005). The relatively low values of R 2 and E of the ANN oxidation model obtained by this study might be attributed to data noise, due to the small sample size of the data set.…”

Section: Optimization Of Neuron Numbersmentioning

confidence: 64%

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Abushammala

Basri

Elfithri

et al. 2013

Journal of the Air & Waste Management Association

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Knowing the fraction of methane (CH 4 ) oxidized in landfill cover soils is an important step in estimating the total CH 4 emissions from any landfill. Predicting CH 4 oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH 4 oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O 2 ) concentration at a depth of 10 cm in cover soil, and CH 4 concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH 4 oxidation with a MSE of 0.0082, a coefficient of determination (R 2 ) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.Implications: To date, no attempts have been made to predict the percent of CH 4 oxidation within landfill cover soils using an ANN. This paper presents modeling of CH 4 oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH 4 oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH 4 oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH 4 emission and how much it contributes to global warming.

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“…Moreover, in order to reduce the area of the microalgal cultivator to reduce its size, it can be expected that the deep part of the microalgal cultivator is irradiated with light by an optical fiber to lead the light to the inside of the microalgal cultivator to generate ·OH. Because it is unnecessary to irradiate the sample with high-energy ultraviolet rays or use H 2 O 2 , this system provides an option for the decomposition of organic compounds, and especially the decolorization of chromophores, which has a substantially lower environmental impact (15,16).…”

Section: Discussionmentioning

confidence: 99%

Decolorization of Organic Compounds by Controlled Microalgal Culture

Hirayama¹

2017

Agrotechnol

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Application of artificial neural networks for modeling of the treatment of wastewater contaminated with methyl tert-butyl ether (MTBE) by UV/H2O2 process

Cited by 134 publications

References 25 publications

Application of a mechanistic UV/hydrogen peroxide model at full-scale: Sensitivity analysis, calibration and performance evaluation

Application of a mechanistic UV/hydrogen peroxide model at full-scale: Sensitivity analysis, calibration and performance evaluation

Modeling of methane oxidation in landfill cover soil using an artificial neural network

Decolorization of Organic Compounds by Controlled Microalgal Culture

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