prediction of fe-co-mn/mgo catalytic activity in fischer-tropsch synthesis using nu-support vector regression

support vector regression (svr) is a learning method based on the support vector machine (svm) that can be used for curve fitting and function estimation. in this paper, the ability of the nu-svr to predict the catalytic activity of the fischer-tropsch (ft) reaction is evaluated and the result is compared with two other prediction techniques including: multilayer perceptron (mlp) and subtractive clustering-adaptive neuro-fuzzy inference system (sub-anfis). the fischer-tropsch synthesis (fts) was studied in a fixed bed micro-reactor under different operating conditions. an extensive experimental data set of mgo supported fe-co-mn catalyst was used to predict the fts. the input variables of three aforesaid models were: reactor temperature, h2/co ratio and total pressure, while the co conversion (catalytic activity) was used as an output variable. finally, the achieved results from these approaches were compared. the results reveal that thenu-svr model has more accurate (mse = 0.0014) than the mlp (mse = 0.0097) and anfis (mse = 0.0043) approaches.