摘要
The modeling of hydrocarbon selectivity and CO conversion of the Fischer-Tropsch synthesis over Fe-Ni/Al2O3 catalyst by using coupled artificial neural networks (ANN) and design of experiment (DOE) approaches were investigated. The variable parameters for modeling consisted of the pressure range between 2 and 10 bar and the temperature range of 523-573 K. After training of data by ANN and determination of DOE points by central composite design (CCD), the results were compiled together for producing simulated data used in the response surface method (RSM). The RSM was used as an applied mathematics model to dem on strate the CO conversi on and selectivity of hydrocarbons depende nee on the CO hydrogenation conditions. The results indicated that CO conversion and Cg selectivity increased with rising both temperature and pressure. The methane selectivity showed upward trend as the temperature in creased. It also in creased by decreasing pressure. Finally, the optimization of the catalytic process was carried out and conditions with maximum desired product were obtained. A comparison of experimental values and RSM values show that the RSM equations are able to predict the behavior of experimental data.
