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• W2891832629 abstract "Abstract 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 {}_{}^ circ C to10 {}_{}^ circ C for 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 {}_{}^ circ C for 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." @default.
• W2891832629 created "2018-09-27" @default.
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• W2891832629 date "2018-09-04" @default.
• W2891832629 modified "2023-10-18" @default.
• W2891832629 title "Robust Prediction of Filtrate Flux for Separation of Catalyst Particles from Wax Effluent of Fischer-Tropsch Bubble Column Reactor via Regularization Network" @default.
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• W2891832629 doi "https://doi.org/10.1515/cppm-2018-0022" @default.
• W2891832629 hasPublicationYear "2018" @default.
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