Modeling of liquid hydrocarbon products from syngas

Abstract

Abstract 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 demonstrate the CO conversion and selectivity of hydrocarbons dependence on the CO hydrogenation conditions. The results indicated that CO conversion and $$C_{5}^{ + }$$ C5+ selectivity increased with rising both temperature and pressure. The methane selectivity showed upward trend as the temperature increased. It also increased 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.

Keywords

• Fischer–Tropsch synthesis
• Artificial neural network
• Response surface method
• CO conversion
• Hydrocarbon selectivity