The use of artificial neural network for modeling the decolourization of acid orange 7 solution of industrial by ozonation process

Fatimah, Siti; Wiharto, Wiharto

doi:10.1088/1757-899x/172/1/012052

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…In the present study, Levenberg-Marquardt backpropagation algorithm was used for network training. This algorithm accelerates attaining convergence and provides the accuracy of the results (38). The tansigmoid (Eq.…”

Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Adsorption of humic acid from aqueous solutions onto shellfish ash: Kinetic and isotherm studies and artificial neural network modeling

Jamshidi

Birgani

Jorfi

et al. 2020

Environ Health Eng Manag

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Background: Natural organic matters such as humic acid react with chlorine and produce disinfection by products such as trihalomethanes that are carcinogenic. In this study, shellfish ash was used as a novel adsorbent for removal of humic acid. Methods: The present study was performed under various laboratory conditions including pH, adsorbent dose, contact time, and initial concentration of humic acid. Residual concentrations of humic acid in the samples were determined by a UV-Vis spectrophotometer at 254 nm wavelength. Artificial neural network (ANN) modeling studies were also performed. Results: Elemental analysis showed that the shellfish ash was 98% pure calcium. It was indicated that the maximum adsorption capacity was achieved in acidic conditions (pH = 3) and pH zpc was found to be 10.3. The adsorption data followed the Langmuir model (R 2 > 0.9). The adsorption of humic acid followed the pseudo-second-order kinetic (R 2 = 0.999). ANN modeling also provided the accurate prediction of humic acid adsorption for testing data (R 2 = 0.989). Conclusion:According to the results, shellfish ash is recommended as an effective biosorbent for removal of organic pollutants such as humic acid.

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Section: Artificial Neural Network Modelingmentioning

confidence: 99%

Adsorption of humic acid from aqueous solutions onto shellfish ash: Kinetic and isotherm studies and artificial neural network modeling

Jamshidi

Birgani

Jorfi

et al. 2020

Environ Health Eng Manag

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“…These differences in the number of azo groups will affect the degradation efficiency of the dyestuff under the same treatment conditions. It has also been investigated using artificial neural network modeling (ANN) by Fatimah et.al [9] that the difference in the number of azo bonds present in the dye affects the percentage of decolorization. In acidic conditions, the ozone molecule is highly selective in attacking the active group of the dyestuff, such as the presence of double bonds or aromatic groups present in the dyestuff.…”

Section: Introductionmentioning

confidence: 99%

Multiple linear regression for the analysis of the parameters used in dyes decolourisation by ozonation techniques

Fatimah

Wiharto

2018

MATEC Web Conf.

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The ozonation process of dye Acid Orange 7 (AO7), Acid Yellow 19 (AY19), and Acid Black 1 (AB1) have been performed. The experimental results predicted the magnitude of the influence of each variable by using Multiple Linear Regression (MLR). This process produces predictive modeling of the variables studied. The variables studied in this ozonation process are independent variables consisting of ozone concentration (mg/L), concentration of dye (mg/L), pH and temperature (°C). The dependent variables studied were the percentage of dye decolorization. The feasibility of the prediction model used has also been tested using the t-test. Based on the prediction model, R2 values for AO7, AY19, and AB1 dyes are 0.84. 0.87, and 0.93 respectively. The analysis of the influence of the independent variables on the percentage of dye decolorization predicted that the concentration of ozone, dyestuff concentration and pH, significantly influenced the ozonation process of AO7, AY19, and AB1. In the ozonation process the temperature variables have a significant effect on AY19 and AB1, but stronger influence on AO7 ozonation process. Based on the predictions of the influence of each variable, the process in the laboratory can be optimized more effectively and efficiently, so that the cost and time factor can be reduced.

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Activated persulfate by chelating agent Fe·/complex for in situ degradation of phenol: intermediate identification and optimization study

et al. 2018

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The use of artificial neural network for modeling the decolourization of acid orange 7 solution of industrial by ozonation process

Cited by 6 publications

References 19 publications

Adsorption of humic acid from aqueous solutions onto shellfish ash: Kinetic and isotherm studies and artificial neural network modeling

Adsorption of humic acid from aqueous solutions onto shellfish ash: Kinetic and isotherm studies and artificial neural network modeling

Multiple linear regression for the analysis of the parameters used in dyes decolourisation by ozonation techniques

Activated persulfate by chelating agent Fe·/complex for in situ degradation of phenol: intermediate identification and optimization study

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