Modeling and Simulation
A. Abdi; M.Sh Izadkhah; A. Karimi; M. Razzaghi; H. Moradkhani
Volume 15, Issue 3 , September 2018, , Pages 82-93
Abstract
A three-layer artificial neural network (ANN) model was developed to predict the remained DO (deoxygenation) in water after DO removal with an enzymatic granular biocatalyst (GB), based on the experimental data obtained in a laboratory stirring batch study. The effects of operational parameters such ...
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A three-layer artificial neural network (ANN) model was developed to predict the remained DO (deoxygenation) in water after DO removal with an enzymatic granular biocatalyst (GB), based on the experimental data obtained in a laboratory stirring batch study. The effects of operational parameters such as initial pH, initial glucose concentration and temperature on DO removal were investigated. On the basis of batch reactor test results, the optimum value of operating temperature, glucose concentration and pH were found to be 30oC, 80 mM and 7, respectively. After back-propagation training, the ANN model was able to predict the remained DO with a tangent sigmoid function (tansig) at hidden layer with 7 neurons and a linear transfer function (purelin) at the output layer. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient of 0.995 for three model variables used in this study.
Modeling and Simulation
A. Ahmadpour; A. Haghighiasl; N. Fallah
Volume 15, Issue 1 , February 2018, , Pages 46-72
Abstract
In this research, photocatalytic degradation method has been introduced to clean up Spent Caustic of Olefin units of petrochemical industries (neutralized Spent Caustic by means of sulfuric acid) in the next step, adaptable method and effective parameters in the process performance have been investigated. ...
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In this research, photocatalytic degradation method has been introduced to clean up Spent Caustic of Olefin units of petrochemical industries (neutralized Spent Caustic by means of sulfuric acid) in the next step, adaptable method and effective parameters in the process performance have been investigated. Chemical oxygen demand (COD) was measured by the commercial zinc oxide that synthesized with precipitation synthesis method in a two-shell photoreactor. The percent of reduction of COD in the photocatalytic process was modeled using Box–Behnken design and artificial neural network techniques. It was concluded that the ANN was a more accurate method than the design of experiment. The effect of important parameters including oxidant dosage, aeration rate, pH, and catalyst loading was investigated. The results showed that all of the parameters, except pH, had positive effects on increasing COD removal. According to the obtained results, adsorption and photolysis phenomena had a negligible effect on COD removal.