Irregularities in Product Distribution of FischerâTropsch Synthesis Due

Article pubs.acs.org/IECR

Irregularities in Product Distribution of Fischer−Tropsch Synthesis Due to Experimental Artifact Junhu Gao,†,‡,§ Baoshan Wu,†,‡ Liping Zhou,†,‡,§ Yong Yang,†,‡ Xu Hao,†,‡ Jian Xu,‡ YuanYuan Xu,‡ and Yongwang Li*,†,‡ †

State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan, Shanxi, 030001, P.R. China ‡ National Engineering Laboratory for Indirect Coal Liquefaction, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan, Shanxi 030001, P.R. China § Graduate University of Chinese Academy of Sciences, Beijing 100039, P.R. China S Supporting Information *

ABSTRACT: Experimental product distribution of Fischer−Tropsch synthesis frequently presents notable deviations from the typical double-α Anderson−Schulz−Flory pattern: bump or dip around the breaking carbon number, positive or negative deviation for heavy hydrocarbons. These irregularities were studied experimentally in a fixed-bed reactor over an industrial Fe/ Mn catalyst, and theoretically by a product separation model based on Aspen Plus software. First, it was found that the unsteady state of reaction condition or improper gas chromatograph procedure could lead to deviation for heavy hydrocarbon distribution. Second, the bump near the breaking carbon number could be attributed to the accumulation of water in hot trap, which leads to an inaccurate measurement of the wax amount. This irregularity can be eliminated by selecting either a higher temperature or a lower pressure of the hot trap. Third, vaporization or flash loss of the oil sample during product collection could result in dip in light hydrocarbon distribution. High syngas conversion levels should be avoided for accurate data acquirement.

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INTRODUCTION Fischer−Tropsch synthesis (FTS) is an increasingly important process, as it can convert coal or natural gas through syngas (CO and H2) into urgently needed liquid fuels or chemicals.1 Generally, FTS is regarded as a polymerization reaction wherein C1 monomer units are added stepwise into growing hydrocarbon chains on catalyst surface.2,3 Therefore, the hydrocarbon selectivity of FTS is expected to be described by an ideal ASF (Anderson−Schulz−Flory) statistical model with a single chain growth probability (α).2,4 However, the hydrocarbon selectivity values experimentally obtained usually deviate from ideal ASF distribution and can only be described approximately by a so-called double-α model, with a breaking carbon number in C6−C12 range.5,6 Although many assumptions have been proposed,3,7−12 the cause of the double-α ASF distribution is now still unclear. To gain more insight into this problem, it is first necessary to obtain large amount of accurate experimental data. Unfortunately, it is usually difficult to collect and analyze FTS product accurately, since the product is a complicated mixture consisting of gaseous, liquid, and solid hydrocarbons, as well as some byproduct, such as water, CO2, and oxygenates.3 Many authors13−18 have reported that the hydrocarbon distributions experimentally obtained frequently present some irregularities, as illustrated in Figure 1. This makes the FTS mechanism investigation much more complex. Four types of irregularities can be identified in Figure 1: (1) bump (positive deviation) and dip (negative deviation) around the turning point of the double-α distribution; (2−3) positive or negative deviation for heavy hydrocarbons; (4) bump at high carbon © 2012 American Chemical Society

Figure 1. Schematic diagram of experimentally obtained product distribution. Solid lines: typical double-α ASF distribution with chain growth factors: α1 and α2; dashed lines, irregularities in product distribution. Case 1, positive deviation (bump) and negative deviation (dip) around the turning point; cases 2 and 3, positive and negative deviations for heavy hydrocarbons; case 4, bump at high carbon numbers.

numbers. Among them, case 1 was the most observed and has been found in the reports of many FTS research groups as well as our laboratory, with each of several experimental systems: fixed-bed reactor, mechanically stirred autoclave reactor, and bubble column reactor.16 Some authors admitted that they could not explain this phenomenon,17 while others gave some Received: Revised: Accepted: Published: 11618

July July July July

30, 17, 25, 25,

2011 2012 2012 2012

dx.doi.org/10.1021/ie201671g | Ind. Eng. Chem. Res. 2012, 51, 11618−11628

Industrial & Engineering Chemistry Research

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hypothesis only. For example, Donnelly and Satterfield16 thought that the product flash loss that happened during sample collection combined with the variation of reaction condition might lead to the appearance of bump or dip in product distribution. Bukur et al.15 suggested that the bump might be attributed to the cracking of heavy hydrocarbon, since the presence of acid sites in FTS catalyst favors the hydrogenolysis or cracking reaction. However, there has been no experimental or theoretical proof confirming those explanations. The origin of these irregularities in product distribution is very important to the studies of FTS reaction mechanism. This study attempts to explore whether these irregularities are caused by experimental artifacts and whether they are related to the reaction nature. Furthermore, experimental conditions for obtaining reliable experimental data are discussed. Specifically, the effect of product collecting and analyzing methods on FTS product distribution under certain reaction conditions are investigated experimentally in a fixed-bed reactor and theoretically by a product separation model.

two-trap scheme has also been recommended and adopted by many other authors.13−16

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EXPERIMENTAL SECTION The fixed-bed reactor, as shown in Figure 2, was a stainless steel tube (inner diameter (ID) = 10 mm; length = 0.8 m) immersed

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SELECTION OF REACTOR SYSTEM Various experiment setups, such as continuous stirred tank reactor (CSTR), internal cycle berty reactor, continuous spinning basket reactor, micro-fixed-bed reactor, and integral fixed-bed reactor, have been tested in the previous studies by our group.18 The results showed that it is difficult to select an ideal reactor for FTS mechanism and kinetic studies.18 For a CSTR, the accumulation of heavy product significantly increases its time required to achieve steady state, which will severely influence the accuracy of the experimental product distribution.19,20 The internal cycle Berty reactor, although free of concentration and temperature gradients, is improper to use in a gas−liquid−solid reaction system because the product wax with higher boiling point will block the motor if it leaks. Continuous spinning basket reactor is also an internal cycle reactor, which has a rotating device above the reactor; thus, it is without the problem of wax blocking. However, the centrifugal force of the rotating catalyst bed makes it not representative of real FTS reaction conditions. For a differential fixed-bed reactor, the small amount of product formed may introduce experimental or analyzing error. An integral fixed-bed reactor, although having certain temperature and concentration gradients along the catalyst bed, is simple for product collection and analysis because of large amount of product formed. At the same time, it needs shorter time to obtain steady state than a CSTR. Accordingly, the fixed-bed reactor was selected in this study. The fixed-bed reactor was heated by a continuously stirred salt bath for the aim of reducing the reactor axial temperature gradient, which was reported having some impact on the product selectivity3,8 Furthermore, higher synthesis gas space velocity was employed to get lower CO conversion. Although it is true that the product composition measured at the exit of the reactor is averaged by the continuously changed CO conversion or axial temperature, the experimental results in this paper will demonstrate that these gradients can hardly lead to the irregularities described in Figure 1. Third, a stable industrial Fe/ Mn catalyst was used to minimize the influence of catalyst deactivation.21 Finally, a two-trap product collection scheme was selected because it can split FTS product into gaseous phase, liquid phase, and solid phase, which are convenient for the product analysis by three or more gas chromatograms. The

Figure 2. Schematic diagram of the fixed-bed reactor system: 1, purification columns (desulphur, deoxygen, decarbonyl, dewater); 2 and 7, back pressure regulator; 3, mass flow controller; 4, bypass valve; 5 and 6, sampling valves; 8, online GC instrument; 9, wet flow meter; 10 and 11, valves for flow rate control of tail gas; 12, cold water inlet; 13, cold water outlet. In the sampling process, some of the oil sample or wax sample vaporized, and flashed into air from the beakers.

in a mechanically stirred salt bath container. Before entering the reactor, H2 (>99.99% purity) and CO (>99.99% purity) passed through a series of purifiers to remove tiny amounts of oxygen, water, and other impurities, and their flow rates were controlled by two Brooks 5850E mass flow controllers. The outlet of reactor was directly connected to a hot trap (typically 160 °C) and then a cold trap (2 °C), both at reaction pressure. After these product collectors, the system pressure was released to near the ambient pressure through a backpressure regulator. Therefore, the pressure of tail gas entering the gas chromatography (GC) instrument was constant for every reaction condition. The liquid products accumulated in hot and cold traps were periodically collected (opening valve 6 in Figure 2) and were analyzed by an off-line GC instrument. To collect liquid sample, taking hot trap as an example, valve 5 was first opened. As the product flowed into the secondary trap, valve 5 was closed and valve 6 was opened, and the sample was released into the beaker below it. When collecting sample from hot trap, some water was frequently found in the beaker sampler. As its weight was measured, it was mixed with the 11619



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Figure 3. Schematic diagram of product analysis procedure for Fischer−Tropsch synthesis. The whole carbon number distribution was obtained through summing the mass flow rate of every component in every phase. Since the online analysis result of tail gas could be obtained immediately, the mass flow rate of tail gas, combined with the mass of wax, oil, and aqueous phase product, was used to calculate the mass balance for every run. As the off-line analysis of other product phases was done, detailed carbon, hydrogen, oxygen balances were computed.

capillary column much more quickly than all hydrocarbons products in the wax phase; therefore, it was a good solvent to dilute the wax sample. The aqueous phase product typically includes some oxygenates and water. Oxygenates were measured by an Agilent 6890N GC instrument with DB-wax capillary column (FID, N2 carrier), while water was quantified according to an external standard method. An industrial Fe/Mn catalyst, prepared by Synfuels China Co. Ltd., was used in this study. The catalyst’s stability has been tested in a pilot plant for more than 2000 h, and its catalytic performance can be found in the literature.13,23 The fresh catalyst was crushed and sieved to 150−180 μm (80−100 mesh) in size. Typically, 3.00 mL (3.75 g) catalyst was loaded and diluted 1:8 with quartz sand of the same mesh size range. The remaining volume of reactor was charged with the same size quartz granules. Before reaction, the catalyst was reduced with syngas (H2/CO = 2.0) at 280 °C, 0.10 MPa, and 1000 h−1 for 32 h. Following activation, the reactor was cooled to 200 °C and then pressurized to 3.0 MPa. The other operating parameters (H2/CO, temperature) were deliberately adjusted to their given values for Fischer−Tropsch reaction. Before collecting experimental data, a transient run of more than 200 h on stream was conducted to ensure that the stable catalytic phases were established. For each run of different reaction condition, at least 36 h (longer time was employed for some cases, i.e., lower reactor temperature or lower GHSV) was required to achieve steady-state reaction. Then, another 12−24 h was required for experimental data acquisition. The sampling frequency was determined by the trap volume and the product flow rate. For lower product flow rate, the time period of material balance should be longer. The mass balance for each run could be immediately obtained after the measurement of the product mass in every phase and the online GC analysis of tail-gas composition; therefore, it was used to check the reliability of experimental data. The mass balance was controlled in the range of 97−102% by repeating the same reaction conditions. When evaluating product distribution, the

aqueous product from cold trap for GC analysis. Along with the product collection from hot or cold trap, as shown in Figure 2, there were always some gases released into air. Some of them were unreacted syngas and gaseous product; the others were the vaporized liquid-phase product due to the change of their pressure and temperature. This would lead to the loss of liquidphase product (named flash loss in this paper) in the sampling process. The flash loss might be dependent on the sampling frequency, and the higher the sampling frequency, the more flash loss. Therefore, lower sampling frequency should be used. The flow rate of the tail gas was measured by a wet gas flow meter, and its composition was monitored with an online GC instrument. For every GC analysis under various reaction conditions, valves 10 and 11 in Figure 2 were adjusted to obtain constant flow rate of tail gas entering the GC instrument. By employing the two-trap scheme, FTS product was separated into four portions: tail gas, oil phase, wax phase, and aqueous phase. The analyzing procedures are illustrated in Figure 3. The tail gas was composed of C1−C9 hydrocarbons, CO2, CO, H2, N2, and little O2, of which the C1−C 9 hydrocarbons were analyzed by an Agilent 6890N GC instrument on an HP-Plot Al2O3 capillary column (50 m × 530 μm ID) with a flame ionization detector (FID) and N2 carrier, while CH4, CO, H2, N2, and O2 were analyzed by the same GC with a different column: MoleSieve 5A capillary column (30 m × 530 μm ID, Ar carrier flow). CO2 was measured using an Agilent 4890D GC instrument equipped with TCD (H2 carrier) and quantified through an external standard method. The oil product from cold trap was analyzed using a GC instrument (Agilent 6890N) with a DB-1 capillary column (60 m × 320 μm ID, N2 carrier, FID) with the temperature programmed from 333 K (maintained for 16 min) to 563 K at the rate of 3 K/min. The wax product from the hot trap was first dissolved in CS2 (0.5−1.0 wt % of wax) and then analyzed using an Agilent 6890N GC instrument with OV-101 capillary column (FID, N2 carrier) with the temperature programmed (2 K/min) from 323 to 573 K. CS2 can leave the 11620



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reactor, since there were temperature and concentration gradients in the catalyst bed. Irregularity Due to Nonsteady State. For a new reaction condition, the time required to obtain new steady state is crucial for the accuracy of the experimental data. Figure 5

carbon, hydrogen, and oxygen balances were also checked for every reaction condition based on detailed product composition (obtained through off-line GC) and flow rate data. In all of our tests, no blocking of the pipe between the hot and cold trap by the heavy hydrocarbon were observed. However, the temperature of hot trap was set to not higher than 160 °C in most cases to avoid the occurrence of pipe blocking. Ideal ASF carbon number distribution follows the following ASF equation.3,18 log(X n) = n log α + log[(1 − α)/α]

(1)

Plotting log(Xn) against carbon number n gives a straight line The slope of this straight line can be used to calculate the chain growth factor (α). For instance, the three chain growth factors in Figure 7a were estimated from the total hydrocarbon distributions in corresponding carbon number range.

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RESULTS AND DISCUSSION Irregularities Due to GC Analysis. An accurate GC analyzing procedure is the premise for obtaining reliable experimental data. The product distribution obtained in our laboratory occasionally presents obvious positive deviation in the high carbon number range, as shown in Figure 4 (GC

Figure 5. Temporal evolution of total hydrocarbon distribution after the reaction condition is changed: (a) from condition 1 (GHSV = 2000 h−1, XCO = 89.54%) to condition 2 (GHSV = 15000 h−1, XCO = 22.8%); (b) from condition 3 (GHSV = 6000 h−1, XCO = 48.95%) to condition 4 (GHSV = 2000 h−1, XCO = 89.54%). Other reaction conditions: 280 °C, 3.00 MPa, H2/CO = 1.45.

Figure 4. Irregularity in hydrocarbon distribution due to the enrichment of heavier hydrocarbon in the GC syringe. 1, syringe; 2, sample ampule; 3, constant temperature water bath (60 °C). Reaction conditions: 270 °C, 3.00 MPa, H2/CO = 1.45, 15000 h−1, XCO = 25.11%.

exhibits the temporal evolution of the product distribution after an increase or decrease of feed space velocity (GHSV). When the GHSV shifts from a lower value (2000 h−1) to a higher one (15 000 h−1), as shown in Figure 5a, the hydrocarbon product with carbon number less than 35 can reach steady state quickly within 24 h, whereas the C35+ product present negative deviation during this transient period. Figure 5b shows that positive deviation appears as the higher GHSV (6000 h−1) is adjusted to a lower one (2000 h−1). By comparing parts a and b of Figure 5, one can find that the time required to obtain the steady state of product distribution is dependent on feed GHSV: the lower the GHSV, the more time needed. In general, the catalyst pores are considered to be filled with heavy hydrocarbon.24 Higher syngas space velocity facilitates the removal of these heavy hydrocarbons from the catalyst pores and thus requires less time to obtain reaction steady state. Positive Deviation around the Breaking Carbon Number. Figure 6 shows a series of FTS hydrocarbon distribution under various reaction conditions. The positive deviation (bump) in C10−C25 range is obvious for some reaction condition, while it disappears for other conditions. This indicates that the bump is dependent on reaction conditions. Lower reaction temperature, lower syngas space

analysis 1), especially the results acquired in winter or at lower room temperature. After checking all the GC analysis procedures, we found that the previous analyzing method of wax sample was improper for these cases. Generally, it is necessary to draw the wax sample from sample ampule by a syringe many times in order to replace the previous sample cleanly. Under lower ambient temperature, when we repetitively drew wax from the sample ampule by a syringe, as shown in Figure 4, heavier hydrocarbons might accumulate and enrich in the syringe due to their higher viscosity. This might lead to inaccurate composition of wax sample entering GC equipment. To avoid this phenomenon, one method is to increase the fluidity of heavy hydrocarbon in the syringe, which can be realized by heating the sample ampule with a constant temperature water bath, as illustrated in Figure 4 (GC analysis 2). In this case, the product distribution approximately obeys typical the double-α ASF pattern. The slightly continuous change of the curve slope in C15+ range (Figure 4, GC analysis 2) might be attributed to the integral effect of the fixed-bed 11621



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hydrocarbon, n-paraffin, and α-olefin exhibit bump in C10−C18 range. Although the n-alcohol (the main oxygenated product) distribution curve presents no positive deviation, it has little impact on the total product distribution, since the curve shape of total hydrocarbon + oxygenate (■ in Figure 7a) resembles that of total hydrocarbon (○ in Figure 7a). The local chain growth factor calculated from total hydrocarbon distribution in Figure 7a reveals that α3 is larger than 1, which is hard to explain by the well-known polymerization mechanism or olefin readsorption theory.3,9,10 The distribution curves of total hydrocarbon in gas phase, oil phase, and wax phase are shown in Figure 7b. The total product distribution curve in the C10+ range is largely dominated by the curve of the wax phase, indicating that the positive deviation in C10+ range is related to the wax-phase product. If there was a positive deviation of the measured wax-phase mass, it would lead to similar irregular phenomenon observed in Figure 7b. However, the material balance for this run is about 99%, suggesting that there was no measurement error leading to the possible “larger” wax amount. To clarify this problem, further experiments were conducted, and the results are shown in Table 1 and Figure 8. The temperature of the hot trap is an important parameter affecting the partition of FTS product among hot trap, cold trap, and tail gas. Therefore, the amount of wax can be adjusted by controlling the temperature of the hot trap. Figure 8a shows that an increase of the hot trap temperature (THot) from 140 to 160 °C can result in a significant change in product distribution. At first, when THot equals to 140 °C (A1), the product distribution curve shows a positive deviation (bump) in C7+ range. As the hot trap temperature rises to 160 °C (A-2), the positive deviation disappears and the product distribution approximately follows a typical double-α ASF pattern. When the hot trap temperature is turned back to 140 °C (A-3), the bump appears again, although it does not overlap with the curve of A-1 completely. This phenomenon indicates that the bump in product distribution depends strongly on the temperature of hot trap. It is also found that for the three cases, the product distribution curves in C15+ range (Figure 8a) are parallel or overlapped, while the α-olefin to n-paraffin ratios in the whole carbon number range are basically similar (Figure 8b), suggesting that the change of the hot trap temperature has no influence on the FTS reaction itself. Furthermore, the results under other reaction conditions (Table 1 B, C, D) also confirm that standard double-α ASF distribution can be obtained by elevating the hot trap temperature.

Figure 6. Irregularities in total hydrocarbon distribution under various FTS reaction conditions: (a) different reaction temperature (T = 220 °C, XCO = 10.12%; T = 280 °C, XCO = 52.29%), other conditions: 3.00 MPa, H2/CO = 2.00, GHSV = 6000 h−1; (b) different gas phase space velocity (GHSV = 2000 h−1, XCO = 87.56%; GHSV = 15 000 h−1, XCO = 20.95%), other conditions: 280 °C, 3.00 MPa, H2/CO = 2.00; (c) different H2/CO ratio (H2/CO = 1.45, XCO = 41.34%; H2/CO = 2.00, XCO = 46.55%), other conditions: 260 °C, 3.00 MPa, GHSV = 6000 h−1.

velocity, and lower H2/CO feed ratio favor the occurrence of these irregularities. For the cases with positive deviation, enough reaction time was adopted to make sure steady state was achieved; at the same time, more than one set of material balance data was measured. However, the same trend in product distribution was observed. Taking one product distribution with positive deviation around the breaking carbon number as an example, its detailed product distributions of different components are shown in Figure 7a. It is obvious that all the distribution curves of total

Figure 7. Detailed product distribution based on different components (a) or different phase states (b) of Fischer−Tropsch synthesis. Reaction condition: 250 °C, 3.0 MPa, GHSV = 6000 h−1, H2/CO = 1.45, THot (hot trap temperature) = 140 °C, XCO = 20.85%. 11622



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Table 1. Effect of Hot-Trap Temperature on the Reaction Results of Fischer−Tropsch Synthesis mass flow rate, g/day runa

THot, °C

extent of irregularityb

tail gas

oil

wax

water

oxyg.c

mass balance, wt %

A-1 A-2 A-3 B-1 B-2 C-1 C-2 C-3 D-1 D-2

140 160 140 140 200 180 120 200 140 200

high no high high no no low no low no

28.66 29.39 28.92 144.09 144.22 145.18 140.42 143.42 197.28 201.74

0.92 1.45 0.87 1.42 2.81 6.33 3.67 6.68 7.82 10.47

6.92 4.03 6.42 6.70 5.03 9.68 13.50 8.39 11.78 6.48

3.68 5.84 3.89 11.84 12.25 11.75 10.24 12.11 21.03 23.10

0.17 0.21 0.17 0.42 0.45 0.84 0.72 0.90 1.17 1.41

98.63 99.94 98.42 100.96 101.34 97.64 98.89 99.46 98.86 99.87

Reaction conditions: (A), 250 °C, 3.00 MPa, H2/CO = 1.45, GHSV = 1000 h−1, XCO = 51.46%; (B), 250 °C, 3.00 MPa, H2/CO = 1.45, GHSV = 4000 h−1, XCO = 25.85%; (C), 270 °C, 3.00 MPa, H2/CO = 0.67, GHSV = 4000 h−1, XCO = 32.43%; (D), 270 °C, 3.00 MPa, H2/CO = 1.45, GHSV = 6000 h−1, XCO = 38.65%. bThe extent of irregularity on the product distribution. cTotal amount of oxygenates in water phase. a

equal (100.2%, 101.1%, 98.8%, respectively). However, for THot equals to 140 °C (A-1 in Table 1), the carbon balance (102.4%) and hydrogen balance (103.2%) are obviously larger than the oxygen balance (95.2%). This result indicates that the detailed product composition may be inaccurate for the case of lower hot trap temperature. Comparing the flow rate data of oil phase, wax phase, and water phase in Table 1 (A-1, -2, -3), we can find that the reduced amount of wax at higher THot is approximately equal to the increased amount of water and oil. This means that, for THot equals to 140 °C, the wax sample may contain some water, leading to inaccurate measurement of the flow rate of wax sample. In our experiments, the product from hot trap indeed frequently contained certain amount of water for the case of THot of 140 °C. Although this part of water was separated from wax and mixed with water sample from cold trap, it was difficult to separate the water completely from wax sample because some water might dissolve in wax25 or was enwrapped by wax. The remaining water in wax could not be analyzed by the GC instrument with FID detector; thus, its amount was usually omitted in the product distribution calculation. As a result, the calculated weight fraction of wax in total product is larger than its real value, leading to positive deviation in product distribution. This result indicates that, to obtain reliable experimental data, the accumulation of water in the hot trap should be avoided. Model for Product Separation. To gain a comprehensive insight into the effect of product collecting conditions on the amount of water contained in hot trap, a product separation model was established based on Aspen Plus software.22 Taking the total material stream below the fixed-bed reactor in Figure 2 as input, this model can simulate the partition of product among the hot trap, cold trap, and tail gas and give detailed composition in different phases. As shown in Figure 9 (without the parts in the dash-dotted frame), the model inlet stream (‘IN’ in Figure 9) was assumed to be connected to the outlet of the fixed-bed reactor (corresponding to the lines between the fixed-bed reactor and the hot trap in Figure 2). As the ‘IN’ stream passed through a hot trap (HOTTRAP) and a cold trap (COLDTRAP) in sequence, the product was divided into water in hot trap (H2OHT), wax (WAX), water in cold trap (H2O-CT), oil (OIL), and tail gas (TAILGAS). A three-phase (vapor−liquid−liquid) flash module from Aspen Plus module database was utilized to simulate both the hot trap and the cold trap. Components C1− C60 n-paraffins, C2−C30 α-olefins, and C1−C20 n-alcohols were

Figure 8. Effect of the hot-trap temperature (THot) on the total product distribution (a) and α-olefin to n-paraffin ratio (b). Reaction conditions: 250 °C, 3.0 MPa, GHSV = 1000 h−1, H2/CO = 1.45, XCO = 51.46%.

The data in Table 1 show that independent of reaction condition the wax flow rate decreases while the oil flow rate increases with the increase of hot trap temperature. According to mass conservation law, the water (including the oxygenated product dissolved in it) flow rate should be the same for every hot trap temperature; however, it is unexpected that the water flow rate increases obviously for higher hot trap temperature (see, A-1, -2, -3 in Table 1). The oxygenate flow rate is so small that its change has little effect on the water flow rate. The water−gas shift (WGS) reaction can also hardly contribute to this variation of water flow rate, since there is no catalyst in the hot trap. For THot equals to 160 °C (A-2 in Table 1), the calculated carbon, hydrogen, and oxygen balance are largely 11623



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Figure 9. Schematic diagram of the Aspen Plus-based product separation model. The two modules in the dash dotted frame are used to calculate the flash loss (volatilized product in sampling process) of hot trap and cold trap in the sampling process. The ‘IN’ stream is supposed to be connected to the reactor exit directly and thus obtains its total product composition. Corresponding to a sampling process in practice, the samples collected are TAILGAS, OIL-L, H2O-CT, WAX-L, H2O-HT, respectively. OIL-G and WAX-G represent the amounts of flash loss for oil and wax samples.

process, there was no water existing in the product from the hot trap. Using the same feedstock as in the case of THot = 160 °C while setting the temperature of hot trap to 140 °C in the product separation model, the calculated results in Table 2 presents large difference compared to experimental data, especially the amounts of wax and water. This model predicts the presence of water in hot trap correctly, which is larger than the amount measured. Assuming the simulated flow rate of different phases are correct, they were combined with the experimental product composition in different phases to recompute the experimental product distributions. The result in Figure 10b shows good agreement with the calculated product distribution, which indicates that the experimental data of product flow rate for different phase is inaccurate under lower temperature of hot trap, especially for the wax phase and water phase, consistent with the former prediction of this paper. The calculated water amount in the hot trap is obviously larger than the experimental value, suggesting that, for the experimental data, some water may be hard to separate from wax, thus leading to a larger wax amount measured. As the amount of water in the hot trap strongly depends on the temperature of hot trap, it is necessary to perform a sensitivity analysis for the water content in hot trap based on the product separation model. The effect of the temperature and pressure of the hot trap, the feed GHSV and the product composition on the water content in hot trap (defined as the water amount accumulated in hot trap/the total water amount formed during reaction × 100%) was evaluated in Figure 11 (see the Supporting Information for the details). Figure 11a exhibits that an increase of hot trap temperature or a decrease of hot trap pressure can result in significant reduction of the water content in hot trap, which can be attributed to that higher temperature or lower pressure is

considered in the simulation. For components H2, CO, CO2, H2O, C1−C30 n-paraffins, C2−C20 α-olefins, and C1−C20 nalcohols, their physical property data were obtained from the PURE 11 database in Aspen Plus, while for other components, the required data were achieved by extrapolation through the ABC methods introduced by Marano and Holder.26 Details of the method can be found in refs 26 and 27. A Redlich− Kwong−Soave equation of state22 in Aspen Plus properties method database was chosen for the calculation of vapor− liquid equilibrium. A set of experimental data (experiment A-2 in Table 1) consisted of detailed component flow rate (see Table S1 in the Supporting Information) was used as the basic feed of product separation model. The pressure of both hot trap and cold trap were set to their experimental values. In the experimental product collecting system, the thermocouples measuring the temperature of hot trap and cold trap were fixed in the outer wall of the traps. As a result, there existed a temperature difference between the measured temperature value and the real value inside the traps. To simulate the vapor−liquid equilibrium inside the traps, a temperature difference of −8 °C for hot trap and 8 °C for cold trap were used after trial and error (i.e., when THot equals to 140 °C, the real temperature inside the hot trap was 132 °C). Simulation of the Water Content in Hot Trap. For the case of THot = 160 °C (case A-2 in Table 1), Figure 10a demonstrates that this product separation model can estimate the detailed product distributions in different phases accurately (the simulated and the experimental data are supplied in Tables S1 and S2 in the Supporting Information). The calculated mass flow rate of different phases are compared with experimental data in Table 2 (THot = 160 °C). The results also show good agreement. Similar to the observation in the experimental 11624



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Figure 10. Comparison of the calculated and experimental product distribution at different hot trap temperatures. (a) THot = 160 °C and (b) THot = 140 °C. The calculated product distributions for oil phase and wax phase did not include the vaporized products due to flash (streams ‘OIL-G’ and ‘WAX-G’ in Figure 7). Experimental data are obtained from runs A-1 and A-2 in Table 1 (see Supporting Information for details). For THot = 140 °C, the experimental mole fractions of total hydrocarbon, gas phase, oil phase, and wax phase, were calculated through combining the experimental product composition of every phase, and their corresponding mass flow rate computed by the product separation model (see Table 2).

Table 2. Comparison of Experimental and Calculated Product Mass Flow Rate for Different Phasesa THot = 160 °C

THot = 140 °C

Figure 11. Calculated content of water accumulated in the hot trap as a function of the hot trap temperature under different (a) hot trap pressures, (b) syngas space velocities, and (c) product composition (changing the chain growth factor α2). Basic simulation conditions: THot = 60 − 160 °C, TCold = 2 °C, PHot = 30 bar, PCold = 30 bar. An 8 °C temperature difference was considered.

product flow rate, g/day expt. tail gasc oil wax water in cold trap water in hot trapd

b

1.57 1.10 3.25 5.38 0.00

b

calc.

expt.

1.49 1.17 3.27 5.32 0.00

1.53 0.69 6.00 2.69 0.63

calc. 1.46 0.93 3.83 3.77 1.46

(GHSV). In general, lower syngas GHSV will lead to higher syngas conversion and higher water partial pressure in the product stream, which increase the boiling point of water, and thus result in higher water content in hot trap. Although higher syngas conversion accelerates the water−gas shift reaction for an iron-based catalyst, the partial pressure of water still increases significantly in our study due to the much reduced syngas GHSV and hydrocarbon product flow rate under higher syngas conversion. This result provides a possible explanation for the irregularities reported by Bukur et al.14 In their study, irregularity emerged in two pretreatment conditions but not in the third condition. Carefully comparing the syngas conversions they reported, one can find that the former two cases have

The cases of THot = 160 °C and THot = 140 °C correspond to runs A2 and A-1 in Table 1, respectively. The mass flow rate of a single phase was calculated by summing up all the mass flow rate of the components in that phase. bIsomeric hydrocarbons are not included in the experimental data; therefore, these data are different from those in Table 1. cThe flow rate of tail gas includes only hydrocarbon product. dWater in hot trap is the water obtained from hot trap. a

favorable to the evaporation of water in hot trap. This result can explain why many reported product distributions conducted under lower reaction pressure reveals regular double-α ASF pattern.11,12 Figure 11b illustrates that the water amount increases remarkably with the decrease of syngas space velocity 11625



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higher conversions than the third one. Therefore, the water content in hot trap for the former two cases might be larger than for the third case, leading to the appearance of bump in product distribution. Figure 11c illustrates that the product composition has little influence on the water content in the hot trap, since it will not affect the water partial pressure in hot trap. In the experimental process, however, product with higher selectivity toward wax (greater chain growth parameter α2) may lead to irregularity in product distribution because more water will be enclosed inside the wax. Both lower inlet H2/CO ratio and lower reaction temperature favor the production of wax, therefore, the probability of irregularity in product distribution increases prominently (Figure 6a and c). Accordingly, to obtain accurate experimental data for mechanism study, relatively higher temperature and lower pressure of the hot trap, as well as a relatively lower syngas conversion level, should be chosen to avoid the effect of water on FTS product distribution. Nevertheless, the temperature of the hot trap should be not too high to avoid the vaporization of some heavy hydrocarbons into the cold trap, reducing the possibility of wax plugging in unheated lines between hot trap and cold trap. The pressure of the hot trap can be adjusted by adding two backpressure regulators before and after the hot trap in Figure 2. The former pressure regulator should be high temperature resistant and can work under the temperature of hot trap; otherwise, the regulator may be malfunction quickly. Flash Effect on Product Distribution. When collecting a wax phase or oil phase product, some of the products with lower boiling point will vaporize because of a decrease of pressure or an increase of temperature, which leads to the flash loss of FTS product and the irregularity in product distribution. Based on the product separation model established in Figure 9, a new model considering flash loss was constructed through adding two vapor−liquid flash modules (FLASH1 and FLASH2 in Figure 9 in the dash dotted frame). With these two modules, the oil and wax streams are divided into two streams respectively. The streams of OIL-L and WAX-L are the oilphase and wax-phase products collected in the experiment, while the streams of OIL-G and WAX-G represent the flash loss for oil and wax samples. The relative error of flash loss, defined as the lost amount of one component due to flash/its total amount formed ×100, is used to indicate the extent of flash loss for every component. The outlet temperature and pressure for the two flash modules are 25 °C and 1 atm, respectively. The model input can be found in the Supporting Information. Figure 12a shows that the flash loss can lead to a slight decrease in the chain growth probability α and an appearance of dip in product distribution. The total relative error due to flash loss, as shown in Figure 12b, concentrates in C1−C10 range and gives a maximal value of about 20% at the carbon number of five. It is also notable that the relative error is derived mostly from the oil-phase product. The flash loss from wax phase appears to be negligible since these long chain hydrocarbons have a higher boiling point and lower volatilization degree at room temperature and ambient pressure. The volcano-like relative error as a function of carbon number may be attributed to a combined effect of the reducing component flow rate in tail gas and its rising boiling point of the component with the increase of carbon number. The lower the carbon number, the higher its content in the tail gas. Since there is no flash loss for tail gas, the flash loss of the hydrocarbon with lower carbon number will decrease. For the hydrocarbon in C5−C10 range,

Figure 12. (a) Calculated product distribution for light hydrocarbon; (b) relative error due to flash loss as a function of carbon number. Basic simulation conditions: THot = 160 °C, TCold = 2 °C, PHot = 30 bar, PCold = 30 bar, GHSV = 200 h−1. A temperature difference of 8 °C was used. The temperature and pressure for both the FLASH1 and FLASH2 were 25 °C and 1 atm. Model input was supplied in section S4.1 of the Supporting Information.

the higher the carbon number, the higher its boiling point, as a result, the less the flash loss. Figure 13 indicates that the relative error of flash loss depends on the condition of the product collecting system, the syngas space velocity, and the product composition. In the simulation, two back-pressure regulators were assumed to be installed before and after the hot trap. Therefore, the pressure of both the hot trap and the cold trap could be controlled freely. The relative error grows up significantly with the increase of cold trap pressure, the decrease of syngas GHSV as well as the reduction of the heavy hydrocarbon (lower α2). However, it is insensitive to the change of the temperature or the pressure of hot trap because the main products of hot trap are heavy hydrocarbons. Therefore, it is suggested that lower cold trap pressure should be selected in order to eliminate the flash loss of oil-phase product. In refs 10 and 14, the backpressure regulator was located before the cold trap, so that the pressure of cold trap could be as low as ambient pressure. As a result, their experimental product distribution showed no dip in the lower carbon number range. This study also indicates that lower syngas conversion level (higher GHSV) should be chosen for accurate data acquisition. As the GHSV reduces to below 2000 h−1 (Figure 13b), the flash loss becomes obvious, consistent with experimental results in Figure 8a. Since the GHSV in other reaction conditions are equaling to or greater than 2000 h−1, the flash loss in those product distributions (Figure 4−7) seems to be negligible.

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CONCLUSIONS Based on the experimental results from a fixed-bed reactor over a stable Fe/Mn catalyst and the simulation analysis through an Aspen Plus based product separation model, the present study demonstrates that some frequently observed irregularities in the 11626



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material is available free of charge via the Internet at http:// pubs.acs.org/.

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AUTHOR INFORMATION

Corresponding Author

* Tel.:+86-10-88845052. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The financial support from National Outstanding Young Scientists Foundation of China (20625620), Chinese Academy of Sciences Knowledge innovation Project (KJCX2-YW-N41), and Synfuels China Co. Ltd. are greatly acknowledged. Valuable discussions with Ying Li and his co-workers in the product analyzing department of Synfuels China are also much appreciated.

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Figure 13. Calculated relative errors due to flash loss as a function of carbon number under different conditions. Basic simulation conditions: THot = 160 °C, TCold = 2 °C, PHot = 30 bar, PCold = 30 bar, GHSV = 200 h−1. A temperature difference of 8 °C was used. The temperature and pressure for both FLASH1 and FLASH2 were 25 °C and 1 atm. Model input was supplied in section S4.2 of the Supporting Information.

product distribution of Fischer−Tropsch synthesis can be attributed to experimental artifacts. First, an incorrect GC analysis method and the non-steadystate of the reaction process can lead to positive or negative deviations at higher carbon number range of product distribution. Second, improper operation parameters employed in the hot trap may lead to the accumulation of water, which may be enwrapped by wax and result in inaccurate measurement of the wax sample. The amount of wax would be greater than its real value, causing a bump in the double-α ASF product distribution around the breaking carbon number. This irregularity can be avoided by selecting either lower pressure or higher temperature for the hot trap. Third, the flash loss that occurred during sampling can lead to a dip in product distribution in C1−C10 range, especially when the syngas conversion is very high or the chain growth probability is small. An increase of cold trap pressure can also enhance the flash loss remarkably. Therefore, a lower pressure for cold trap should be chosen for accurate experimental data acquisition.

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REFERENCES

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ASSOCIATED CONTENT

S Supporting Information *

Input data for product separation model (Table S1), comparison between experimental results and model prediction (Table S2), input data for sensitivity analysis (Table S3), tuning scheme of product distribution for sensitivity analysis (Figure S1). The calculation methods of the data in Table 2 and the input data for flash loss simulation were also given. This 11627



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(17) Madon, R. J.; Taylor, W. F. Fischer−Tropsch synthesis on a precipitated iron catalyst. J. Catal. 1981, 69, 32. (18) Gao, J. H. Thermodynamics, kinetics studies and product selectivity control of Fischer−Tropsch synthesis. Doctoral Thesis, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan, 2011. (19) Pinna, D.; Tronconi, E.; Lietti, L.; Zennaro, R.; Forzatti, P. Wax composition transients during Fischer−Tropsch synthesis. J. Catal. 2003, 214, 251. (20) Gormley, R. J.; Zarochak, M. F.; Deffenbaugh, P. W.; Rao, K. Effect of initial wax medium on the Fischer−Tropsch slurry reaction. Appl. Catal., A 1997, 161, 263. (21) Raje, A. P.; Davis, B. H. Effect of vapor−liquid equilibrium on Fischer−Tropsch hydrocarbon selectivity for a deactivating catalyst in a slurry reactor. Energy Fuels 1996, 10, 552. (22) Aspen Plus User Manual, Aspen Plus version 11.1; Aspen Technologies Inc.: Cambridge, MA, 2001. (23) (a) Liu, Y.; Teng, B. T.; Guo, X. H.; Li, Y.; Chang, J.; Tian, L.; Hao, X.; Wang, Y.; Xiang, H. W.; Xu, Y. Y.; Li, Y. W. Effect of reaction conditions on the catalytic performance of Fe-Mn catalyst for Fischer− Tropsch synthesis. J. Mol. Catal. A 2007, 272, 182. (b) Zhong B.; Wang Q.; Peng S. Y. A method of producing gasoline from synthesis gas. Chinese Patent No. ZL 95/106156.9, 1995. (24) Gao, J. H.; Wu, B. S.; Zhou, L. P.; Yang, Y; Hao, X; Xu, J; Xu, Y. Y.; Cao, L. R.; Li, Y. W. Effective control of α-olefin selectivity during Fischer−Tropsch synthesis over polyethylene glycol enwrapped porous catalyst. Catal. Commun. 2011, 12, 1466. (25) Breman, B. B.; Beenackers, A. A. C. M.; Rietjens, E. W. J.; Stege, R. J. H. Gas−liquid solubilities of carbon monoxide, carbon dioxide, hydrogen, water, 1-alcohols (1 ≤ n ≤ 6), and n-paraffins (2 ≤ n ≤ 6) in hexadecane, octacosane, 1-hexadecanol, phenanthrene, and tetraethylene glycol at pressures up to 5.5 MPa and temperatures from 293 to 553 K. J. Chem. Eng. Data 1994, 39, 647. (26) Marano, J. J.; Holder, G. D. General equation for correlating the thermophysical properties of n-paraffins, n-olefins, and other homologous series. 2. Asymptotic behavior correlations for PVT properties. Ind. Eng. Chem. Res. 1997, 36, 1895. (27) Wang, G.; Wang, Y. N.; Yang, J.; Xu, Y. Y.; Bai, L.; Xiang, H. W.; Li, Y. W. Modeling analysis of the Fischer−Tropsch synthesis in a stirred-tank slurry reactor. Ind. Eng. Chem. Res. 2004, 43, 2330.

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