application of artificial neural network in deoxygenation of water by glucoseoxidase immobilized in calcium alginate/mno2 composite

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 reactor study. in enzymatic method for removing dissolved oxygen of water, glucose oxidase accelerates the reaction between o2 and glucose. therefore, oxygen is removed. 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 30 oc, 80 mm, and 7, respectively. the less dissolved oxygen in water there is, the more prevention of corrosion will occur. in optimum operating condition, the concentration of do reached zero. after back-propagation training, the ann model was able to predict the remaining 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 target was proven to be satisfactory with a correlation coefficient of 0.995 for three model variables used in this study.