We investigate effects of catalyst activity, catalyst particle shape (sphere, slab, and hollow cylinder), size (i.e., diffusion length), catalyst distribution (uniform vs eggshell type distribution for a spherical particle), and process conditions (temperature, pressure, syngas composition, and conversion level) on catalyst effectiveness factor and methane selectivity inside the catalyst pellet. In numerical simulations we utilize kinetic parameters for CO consumption rate and CH4 formation rate determined from experiments with a highly active Co/Re/γ-Al2O3 catalyst. It is found that the use of small spherical particles (0.2–0.5 mm) or eggshell distribution for larger spherical particles with catalyst layer thickness less than approximately 0.13 mm is needed to avoid negative impact of diffusional limitations on CH4 selectivity under typical Fischer–Tropsch synthesis operating conditions. For monolith reactors with wash-coated catalyst, diffusional limitations can be avoided by using a catalyst layer thickness less than 0.11 mm at base case conditions (473 K, 25 bar, and H2/CO molar ratio of 2).

Please cite this article in press as: B. Todic, et al., Fischer-Tropsch synthesis product selectivity over an industrial iron-based catalyst: Effect of process conditions, Catal. Today (2015), http://dx. a b s t r a c tThe effect of process conditions on product selectivity of Fischer-Tropsch synthesis (FTS) over industrial iron-based catalyst (100 Fe/5 Cu/4.2 K/25 SiO 2 ) was studied in a 1-L stirred tank slurry reactor. Experiments were performed over a range of different reaction conditions, including three temperatures (T = 493, 513 and 533 K), four pressures (P = 0.8, 1.5, 2.25 and 2.5 MPa), two synthesis gas feed molar ratios (H 2 /CO = 0.67 and 2) and gas space velocity from 0.52 to 23.5 Ndm 3 /g-Fe/h. The effect of process conditions on reaction pathways of FTS and secondary 1-olefin reactions was analyzed by comparing product selectivities, chain growth probabilities and ratios of main products (n-paraffin, 1-and 2-olefin). Reduction of methane production and increase of C 5+ products was achieved by decreasing temperature, inlet H 2 /CO ratio and/or increasing pressure. Overall selectivity toward methane and C 5+ did not show significant changes with variations in residence time. All of the product selectivity variations were shown to be related to changes in chain length dependent growth probabilities.

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In this review we discuss the reasons responsible for higher than expected methane selectivity during Fischer–Tropsch synthesis (FTS) over cobalt-based catalysts and describe novel microreactors for use in FTS.

A multiphase fixed-bed reactor (FBR) model for Fischer−Tropsch Synthesis has been developed. A high level of details is considered for description of the phenomena on the reactor and particle scale. Detailed kinetics is used, with parameters estimated from experiments with a cobalt-based catalyst. Model robustness has been validated using literature data. Performance analysis was made for a conventional scale FBR with egg-shell distribution of catalyst and a millimeter-scale FBR with small particles and uniform distribution. In both cases, diffusion limitations are almost eliminated due to use of small diffusion lengths. For similar qualitative results, a milli-scaled design would result in a significantly lower reactor volume, but the capital costs could be high due to large wall area and a vast number of tubes. Heat removal is efficient in both cases, and pressure drop in the milliscale reactor is low due to the use of a shorter bed and lower velocity.

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Fundamentally based model for pressure drop in gas-flowing solids-fixed bed contactors is presented, together with a phenomenological semiempirical model for prediction of dynamic holdup. These simple models do not require any parameters that need to be determined by measurements in the actual system of interest. The predictions are compared with all available data and give good agreement for a wide range of experimental conditions, different constructions, types, and dimensions of packing and for a variety of flowing solids properties.Countercurrent flow of gas and fine solids through packed beds was patented as an idea in 1948, 1 and the first recorded industrial use occurred in 1965 (Compagnie de Saint Gobain) 2 in heat-transfer applications. The fluid dynamics of such systems received considerable attention over the years, 3-14 and this included heat-and mass-transfer studies. 8,[15][16][17] The interest in exploiting the unique features of the countercurrent gas-fine solids systems was enhanced by the studies of Westerterp and colleagues, 18,19 who proposed the use of fine solids as a regenerative adsorbent flowing through the bed of catalyst for methanol synthesis. Additional reactor-oriented studies included catalytic oxidation of hydrogensulfide 20 and regenerative desulfurization of flue gases. 17,21 The application of this type of gas-flowing solidsfixed bed contactors would be enhanced if the fluid dynamics in these systems could be fully quantified, at least in the macroscopic engineering sense. Reliable prediction of pressure drop, flowing solids holdup, residence time distribution, back mixing, and so forth, are some of the quantities needed when assessing the applicability of the "flowing" or "trickling solids" systems in a variety of processes.Previous studies resulted in a semiquantitative description of the fluid dynamics of the system and empirical correlations for determination of some quantities of interest. Three flow regimes were observed, similar to gas-liquid systems: preloading, loading, and flooding. Gas-flowing solids interaction increases with the increase in gas superficial velocity. When the terminal velocity of gas relative to flowing particles is approached, a sudden increase in pressure drop and fine solids holdup occurs, together with accumulation of solids at the top of the bed and unstable operation, which is characteristic for flooding.The complexity of the fluid dynamics of these systems did not permit, so far, a unique pressure drop equation to emerge without empirical constants. The problem is further complicated by the lack of consensus in accounting for the effects of particle shape, size, roughness, bed porosity distribution, and so forth. The models presented were often developed by fitting the data of a few studies and were not extensively tested, thus lacking in predicting ability. Moreover, the approach used was such that data on the system of interest were always needed to complete the correlation (i.e., one had to have data to predict them!). 6,10 In our earlier s...

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