a neuro-fuzzy algorithm for modeling of fischer-tropsch synthesis over a bimetallic co/ni/al2o3 catalyst

an alumina supported co/ni catalyst was prepared by sol-gel procedure to study the catalytic behavior during fischer-tropsch synthesis in a fixed-bed reactor. the effect of co conversion (10-50%) on hydrocarbon product distribution (ch4, c5+ and c2-c4 olefin selectivities) was studied. selectivity for ch4 decreased, while those of c5+ and olefin selectivities increased with increasing co conversion. the catalysts properties were characterized at different stages using powder x-ray diffraction (xrd), brunauer-emmett-teller (bet) surface area measurements, and scanning electron microscopy (sem). a neuro-fuzzy model called locally liner model tree (lolimot) was applied to predict the catalytic behavior during fischer-tropsch reaction over the co/ni/al2o3 catalyst. the predicting system was established on co conversion values as a target based on three variables, including partial pressure of co and h2, and h2/co feed ratios as the input. to evaluate the generalization performance of the system, the k-fold cross validation was applied so that an excellent prediction was observed with mean square error (mse) which equals 7.4211e-004. finally, the extrapolation ability of lolimot was perused (beyond the training range). the obtained data from lolimot were compared with the experimental data, and the results indicated that lolimot is a worthy system modeling with high capability for data prediction, both within and beyond the training range.