Prediction of Fischer–Tropsch Synthesis Kinetic Parameters Using Neural Networks

Abstract

Abstract The kinetic mechanism of the Fischer–Tropsch synthesis (FTS) is complex resembling a polymerization reaction. The kinetic rate constants for initiation, propagation and termination steps and the constants for the equilibrium reactions for methylene formation (in situ monomer) need to be estimated. A mathematical model for the FTS allows for simulating several operating conditions and determining the best operating conditions to produce a specific product distribution, so the kinetic parameters must be statistically valid. This work used neural networks (NNs) to estimate the FTS kinetic parameters, instead of using methods based on least squared error. The results show that NNs with three hidden layers were able to output good estimates of the kinetic parameters with less than 5% of deviation.