m.r ehsani - Department of Chemical Engineering, Isfahan University of Technology, Isfahan ۸۴۱۵۶-۸۳۱۱۱, Iran
h bateni - Department of Chemical Engineering, Isfahan University of Technology, Isfahan ۸۴۱۵۶-۸۳۱۱۱, Iran
gh razi parchikolaei - Department of Chemical Engineering, Isfahan University of Technology, Isfahan ۸۴۱۵۶-۸۳۱۱۱, Iran

In this article, artificial neural network was used for modeling oxidative coupling of methane (OCM) over Mn/Na2WO4/SiO2 catalyst. A multilayer perceptron was used for simulating the relations between process operating conditions such as temperature, gas hourly space velocity(GHSV), CH4/O2 ratio and diluents gas (mol% N2) and aspects of catalytic performance including conversion of methane, C2 products selectivity, yield of C2 and C2H4/C2H6 ratio. In order to prevent network complexity, principal component analysis method was used and the numbers of output parameters were reduced from 4 to 2. For the first output and the second output, there were optimum network with 4-9-1 (one hidden layer which includes 9 neurons) topology and 4-6-1 (one hidden layer which includes 6 neurons) topology prepared, respectively.

oxidative coupling of methane OCM,Mn ، Na2Wo4 ، SIo2catalyst,principal components,artificial neural network ANN