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.