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The effectiveness of an internal filtration system intended for separation of wax-catalyst from Fischer–Tropsch synthesis products is investigated in the present study. The generalization performances of in-house Regularization Network (RN) equipped with efficient training algorithm is recruited for prediction of filtrate flux. The network was trained by resorting several sets of experimental data obtained from a specific system of air/paraffin liquid phase/alumina oxide particle conducted in a slurry bubble column reactor. The RN is employed to explore the relationship between the slurry phase temperature (10–60 °C), pressure difference (0.3, 0.6 and 0.9 bar) and time (0–120 min) on the rate of outcome filtrate from various size of filter element (4, 8 and 12 microns). The superior recall and validation performances with different exemplars data points show that the optimally trained RN which has solid roots in multivariate regularization theory, is a reliable tool for prediction of filtrate flux. Faithful generalization performance of RN revels that around 66 % reduction in filtrate flux is observed by decreasing temperature from 60∘   C   {}_{}^ \circ Cto10∘   C   {}_{}^ \circ Cfor filter pore size of 4 microns. Decreasing of slurry viscosity is the main reason of such behavior. Increasing pressure driving force has a significant effect on elevating filtrate flux. Due to cake formation, filtrate flux is decreased from 2 to 1.4 (ml/min.cm 2 ) at constant temperature of 60∘   C   {}_{}^ \circ Cfor filter pore size of 8 microns. Furthermore, the backwashing process is more effective for smaller pore size filter and temperature variation does not have any considerable effect on filter recovery.

chemical product and process modeling

Robust Prediction of Filtrate Flux for Separation of Catalyst Particles from Wax Effluent of Fischer-Tropsch Bubble Column Reactor via Regularization Network