Subscriber access provided by UNIV OF SOUTHERN INDIANA
Fossil Fuels
HYDRATE INDUCTION TIME WITH TEMPERATURE STEPS: A NOVEL METHOD FOR THE DETERMINATION OF KINETIC PARAMETERS Valentino Canale, Antonella Fontana, Gabriella Siani, and Pietro Di Profio Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.9b00875 • Publication Date (Web): 11 Jun 2019 Downloaded from http://pubs.acs.org on June 12, 2019
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
HYDRATE
INDUCTION
TIME
WITH
TEMPERATURE STEPS: A NOVEL METHOD FOR THE
DETERMINATION
OF
KINETIC
PARAMETERS Valentino Canale,a Antonella Fontana,a Gabriella Siani,a Pietro Di Profioa,b* a
Department of Pharmacy, University of Chieti-Pescara "G. D'Annunzio", Via dei Vestini 31 - I-
66013 Chieti, Italy b
Center of Excellence on Innovative Nanostructured Materials (CEMIN), Department of Chemistry,
University of Perugia Via Elce di Sotto 8, I-06123 Perugia, Italy
Keywords: gas hydrates, kinetics, induction times, clathrates, constant cooling, inhibitors.
ABSTRACT
Gas hydrate formation usually occurs with a certain delay after a system composed of water and a hydrate-forming gas is put under suitable thermodynamic conditions of pressure and temperature. This delay period is called the “induction time”, and due to its large variability within a single experimental setting, hydrate formation is often referred to as a stochastic process. The evaluation of induction times, together with other measurements, are taken as an indication for the efficiency of hydrate inhibitors, and they are usually carried out by simply putting the experimental system under ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 2 of 19
chosen P/T conditions, then waiting for the hydrate to form and measuring the time elapsed. In this paper, we present an improved procedure by which the variability of hydrate induction times can be remarkably reduced, while keeping a good correlation of measured induction times with the respective temperatures as obtained by a constant-cooling method. In this procedure, temperatures are lowered by 0.5°C after each time span of 3 hrs with no hydrate formation. Induction times obtained in this way show a remarkably lower coefficient of variation as compared to a standard induction time measurement.
1. Introduction Several gas species form the so-called gas (clathrate) hydrates when interacting with water under high pressure and low temperature conditions.1 Natural gas hydrate formation causes plugs in oil and gas pipelines, representing a major problem in the gas and oil industry. To inhibit formation of hydrates within pipes, choke valves, and wellheads, several kinds of additives are used: thermodynamic inhibitors such as alcohols and glycols,2–4 and Low Dosage Hydrate Inhibitors (LDHIs). The latter category may be divided into kinetic inhibitors (KHIs) 5 and antiagglomerants (AAs).6–8 The efficiency of hydrate inhibitors is evaluated in the laboratory by means of several experimental approaches, which are based on the determination of such relevant parameters as the time elapsed for a particular experimental setup to start forming hydrates, the sub-cooling with respect to the thermodynamic pressure/temperature formation curve, etc. In particular, the Isothermal Induction Time (IT) method and the Constant Cooling (CC) method are used to characterize LDHIs. In the former (IT), the experimental system (usually, a pressurized reactor containing water and an inhibitor) is put under suitable pressure and temperature conditions within the hydrate formation region, then the time until hydrate formation starts is taken as a direct measure of inhibition ability. In the CC method, an inhibition test starts at a temperature slightly above the boundary of the hydrate formation region, then the system is cooled down at a constant rate until hydrate starts to form, with ACS Paragon Plus Environment
Page 3 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
a lower formation temperature indicating a more efficient inhibitor. Another way for evaluating the ability of LDHIs is the Crystal Growth Inhibition (CGI) method.9 This method is reportedly repeatable and can be adapted to different experimental setups. Furthermore, the CGI allows to distinguish among several inhibition regions as a function of subcooling, ranging from complete inhibition to rapid growth. While being a smart process for evaluating LDHIs, this method starts form a pre-formed hydrate, which is a condition known to reduce the intrinsic stochasticity of hydrate formation. Finally, some groups have studied temperature ramping, but the experimental setups were quite different from ours.10,11 A detailed overview of those and other methods for evaluating LDHIs can be found in the recent literature.12–15 However, it is a widely recognized problem that the time and temperature parameters measured with the above methods are heavily dependent on a number of experimental details, such as the geometry of the reactor, stirring speed, cooling rate, volume and exposed water surface of the reactor, and so on, thus rendering a comparison among different experimental setups hardly significant.12 Another well known problem is that, particularly for IT measurements, the variance within a single experimental setup is intrinsically high, thus leading to helplessly define hydrate nucleation as a “stochastic” phenomenon.16 The above problems – especially the latter - have been recently addressed on the basis that a stochastic process essentially only needs more data to be fully characterized. For example, several works have been done with a novel High-Pressure Automated Lag Time Apparatus (HP-ALTA), by which a much larger number of hydrate formation events could be followed in a certain time span.17,18 This approach led to statistically significant measurements of formation probability distributions for gas hydrates, which could be then compared to nucleation theory predictions.19–21 Another recent work aimed at circumventing the above problems is based on a novel multi-test tube rocking cell, which allowed the Authors to obtain large amounts of data within reasonable times, and conclude that the beginning of hydrate growth is logarithmically related to the sub-cooling value.12 Finally, some recent studies adopted “survival curves” of water droplets placed under hydrate formation conditions as an indication of the stochasticity of hydrate nucleation.22,23 ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 4 of 19
In the present work, we propose a relatively simple method for the determination of KHI performance, which method consists of performing non-isothermal induction time measurements within a given time span with a step gradient of temperatures. This Step Induction Time (SIT) method gave less variable results in terms of measured induction times of several low-molecular weight hydrate inhibitors and promoters, and also showed a higher correlation with inhibitor performances as determined by constant cooling than a usual IT method. To test our method under the worst conditions, pure (>99.5%) methane was used as a sI-forming gas, being this a hydrate former which is usually characterized by larger variance/stochasticity than sII formers (e.g., natural gas). 2. Experimental 2.1 Materials The following materials were purchased from Sigma-Aldrich, and used as received: PVP, Polyvinylpyrrolidone, powder, average Mw ~55,000; CTABr, Hexadecyltrimethylammonium bromide ≥98%; DoTABr, Dodecyltrimethylammonium bromide ≥98%; SDS, Sodium dodecyl sulfate ≥98.0% (GC); L-Serine, ReagentPlus®, ≥99% (HPLC); PVA, Poly(vinyl alcohol) Mw 85,000124,000, 99+%; L-Aspartic acid, reagent grade, ≥98% (HPLC); L-Phenylalanine, reagent grade, ≥98%. The surfactant p-dodecyloxybenzyl-N,N-dimethyl-N-ethanolammonium bromide (pDoDEtOHABr) was synthesized as described in a previous paper,24 with the exception that an ether solution of N,N-dimethyl ethanolamine was used instead of trimethyl amine in the last step. A white solid precipitated, which was filtered out, and crystallized from acetone-methanol. NMR was consistent with the target structure. CH4 (2.5 grade, >99.5 methane) was purchased from SOL S.p.A. Ultra-pure water was from a Direct-Q device (Millipore).
2.2 Apparatus Hydrate formation tests were performed with HM1 (Hydrate Machine 1). For a detailed description, refer to previous works by Di Profio et al.25,26 A high-power thermoelectric Peltier module was used in this apparatus to achieve a temperature range from -20 °C to 80 °C. This system, together with ACS Paragon Plus Environment
Page 5 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
dedicated electronics, allows for an accurate control of temperature. Due to a custom PID algorithm, the instrument is able to accurately follow temperature ramps with a wide range of heating/cooling rates starting from 4°C/min. A schematics of the HM1 is shown in Figure 1.
Figure 1. Schematics of the HM1.
2.3 Procedure Induction time: The water-filled (150 mL) reactor was flushed with methane for 5 min, then charged at 6 MPa with methane at r.t. while stirring. It was then cooled down to 10.55°C (2°C above hydrate curve) in 10 min, left for 30 min at that temperature, then rapidly cooled to setpoint temperature (4.55°C; 4°C of subcooling) with a cooling rate of 1°C/min. Pressure was kept constant through the electro-pneumatic device (see 2.2, above). Induction time measurement started as soon as the setpoint temperature was reached, and ended with the combined occurrence of (i) a substantial temperature increase, and (ii) a measurable gas flow through the flow-meter. After each loop (5 in total for each experiment), the temperature was raised to 32°C over 1 hr and kept there for 1 hr for history removal.
ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 6 of 19
Constant cooling: Same as above up to the resting phase at 10.55°C, then the cooling ramp was started at the reported rate (0.25-1.50°C/hour). Formation temperature was taken as the value under which both temperature and gas flow showed hydrate formation (see above). Step Induction Time: Same as above up to the resting phase at 10.55°C, then the reactor was cooled down to a first temperature T1 (8.05°C; 0.5°C of subcooling), and left there for the reported time. If hydrate did not form during this first isothermal step, then the temperature was lowered to a second temperature T2, with a mild cooling rate of 0.1°C/min and left for the reported time. This procedure was iterated for as many steps (T3, T4, etc.) until hydrate formation was observed. After each loop, the temperature was raised to 32°C, as above for IT. The following Figure 2 shows a schematics of the three methods.
Figure 2. Schematics showing temperature ramps vs. time for constant cooling (CC), induction time (IT), and step induction time (SIT) methods, respectively.
ACS Paragon Plus Environment
Page 7 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
3. Results and Discussion 3.1 Optimization of experimental conditions While a few novel, high throughput approaches have been recently proposed for reducing the stochasticity of induction time measurements,12,17,18 they all rely on specific, multi-reactor designs aimed at obtaining a large amount of experimental data, whose averaging can then be reasonably said statistically significant, even in the face of large variances. On the other hand, there is still lacking a method for intrinsically reducing the variability of induction times (or their proxies), which means obtaining a relatively reliable value of inhibitor (or promoter) performance with few replicates in a single reactor. As an example of a typical scattering of IT data, we carried out preliminary induction time measurements of methane hydrates with our apparatus by running five replicates per experiment at three temperatures and 6 MPa of pressure, thus obtaining the results shown in Figure 3:
Figure 3. Average induction times measured for methane hydrate formation in ultra-pure water (no inhibitors/promoters added) at 6 MPa and the indicated temperatures. Bars represent standard deviations.
It comes with no surprise that deviations from average are huge, and worse than that, simple averaging of such a small number of replicates is not statistically correct in terms of confidence intervals/limits. In addition, IT averages are not correlated with the subcooling degree. Therefore, the basic idea behind this work is that the induction time method can be improved by introducing a step-wise, suitably chosen temperature gradient, which corresponds to a step-wise ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 8 of 19
increase of the “driving force”. This should lead to IT values having a lower variance with respect to replicated runs, and a possibly correlated to the inhibition performances as obtained with constant cooling methods. Time spans thus measured can then be regarded as “proxies” of induction times and, consequently, useful as a measure of inhibitor (or promoter) performances. The first issue was determining a suitable pair of temperature drop/step time values which gave a lower deviation among replicate experiments. Starting with pure water under methane hydrate formation conditions (6 MPa, 8.05°C, i.e., 0.5°C of subcooling with respect to the equilibrium curve, as calculated with the CSMHYD software),27 we explored three temperature drop values (0.5, 1.0, 1.5°C), and three isothermal step times (3, 6, 9 hours) by running five experiments for each temperature/time pair. As a result, Figure 4 shows the relative standard deviations for each experiment.
Figure 4. Relative standard deviations of measured induction times as a function of (i) temperature differences between two successive cooling steps, and (ii) step times.
We opted for a combination of 0.5°C and 3 hours, because it gave a low RSD value combined with the lowest increase of the driving force for each step. Also, the shorter time span was preferable in terms of experimental output. Since another goal was to determine whether there was a correlation between inhibitor performances as measured with IT (SIT) and CC methods, we also performed a screening of the standard deviations among hydrate formation temperatures obtained with the constant cooling routine under different cooling rates (0.25, 0.5, 1.0, 1.5°C/h). Figure 5 shows that ACS Paragon Plus Environment
Page 9 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
formation temperatures are consistently around 2.3-2.4°C of subcooling (except for runs at 0.25°C/h), whereas standard deviations are quite low for the second slowest rate (0.5°C/h), and measurably higher for the two fastest. This led us to choose 0.5°C/h as the cooling rate for performing CC cycles to be compared with IT (SIT) measurements.
Figure 5. Subcooling temperatures under different cooling rates. Values on the ordinate axis represent T’s below the equilibrium temperature. Bars represent standard deviations.
With this combination of operating parameters for SIT (0.5°C/step; 3h/step) and CC (0.5°C/h), we performed hydrate formation experiments in the presence of chemical low-dosage inhibitors and promoters (modulators).
3.2 Testing of modulators Methane hydrate formation experiments were conducted according to each of the above methods (IT, SIT, CC) in the presence of several chemicals, some of which are known hydrate inhibitors (e.g., PVP as KHI, tetraalkyl ammonium surfactants as AAs or synergistics)28,29 or promoters (SDS).25 Other less known modulators are several amino acids, which are being studied both as promoters 30 and inhibitors.31,32 Here, we also tested the amino acids serine, aspartic acid and phenylalanine.
ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 10 of 19
Finally, 5 wt% ethanol was tested as a thermodynamic inhibitor. Table 1 reports the induction times (IT, SIT) and formation temperatures (CC) observed, together with the relevant deviations.
Table 1. Values of modulator performances CONSTANT COOLING STEP INDUCTION TIME INDUCTION TIME SAMPLE T (°C) subcooling st. dev.
* Not done;
§
min
st. dev %RSD
min
st. dev %RSD
water
6.86
1.69
0.49
531.75
56.35
10.60
146.60 195.67 133.47
PVP 3000 ppm
3.54
5.01
1.02
1510.00 170.00
11.26
CTABr 1 mM
6.22
2.33
0.47
640.67
83.29
13.00
46.00 41.43
90.07
DoTABr 1 mM
6.33
2.22
0.19
661.87
58.74
8.88
5.43
6.99
128.71
p_DoDEtOHABr 1 mM 5.97
2.58
0.33
756.50
28.50
3.77
*
*
*
242
176.20 72.81
PVP 500 ppm
4.76
3.79
0.31
1001.25
61.92
6.18
38.57 41.89 108.61
SDS 300 ppm
6.50
2.05
0.28
561.75
66.89
11.91
20.00
Serine 400 ppm
7.36
1.19
0.17
254.20
76.79
30.21
6.50
PVA 1000 ppm
6.99
1.56
0.36
220.00
14.17
6.44
7
§
§
Aspartic Acid 300 ppm
5.53
3.03
0.23
781.00
188.17
24.09
*
*
*
Phenylalanine 300 ppm
6.42
2.13
0.19
515.00
73.75
14.32
*
*
*
EtOH 5% wt
3.61
4.94
0.42
1005.50 180.50
17.95
*
*
*
§
§
14.53 223.61
standard deviation and %RSD could not be calculated due to a single experimental
value obtained under the reported conditions (4 out of 5 experiments resulted in hydrate formation during the cooling ramp).
Table 1 shows an intrinsic limitation of induction time experiments, in that a particular subcooling value (4°C in this case) chosen in order to avoid unfeasibly long lag times with strong inhibitors (e.g., PVP), was too much for some molecules, where hydrate formed during the cooling ramp. This fact inherently limits the capability of this method in comparing differently performing modulators.
ACS Paragon Plus Environment
Page 11 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
Table 1 also clearly shows that “induction” times measured according to the SIT method have much lower variances (%RSD column) as compared to standard IT measurements. SIT times also show a much better correlation with constant cooling values, as shown in Figures 6 and 7.
Figure 6. Plot of constant cooling (CC) values vs. SIT values. Bars represent standard deviations (R2 = 0.954)
Figure 7. Plot of constant cooling (CC) values vs. IT values. Bars represent standard deviations.
ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 12 of 19
Figure 6 shows that SIT and CC values are linearly correlated (r = 0.95), while no correlation exists for CC vs. IT values (Fig. 7). There is an intrinsic discrepancy between a purely kinetic approach (i.e., IT) and a mixed thermodynamic/kinetic method (CC), as what the latter essentially does is continuously changing the thermodynamic conditions under which a hydrate-forming system is set. The kinetic part of CC methods is clearly dependent on the cooling rate, and a few papers investigate how changing those rates affects the outcome.33,34 In the SIT method, a thermodynamic “drive” (i.e., a small, stepwise decrease in T) is added, while keeping an induction time as the measured parameter. At each step, the driving force (subcooling) is increased by a small increment (0.5°C in this work). The shift from T to (T – 0.5) is conducted over 5 minutes with a relatively gentle slope of 0.1°C/min, in order to prevent that an abrupt increase of driving force might prime a sudden crystallization. After each temperature decrease, the water + methane system is left under this new isobaric/isothermal condition, and observed for signs of detectable hydrate formation (temperature increase and gas inflow). If nothing happens within the set time (3 hours in this work), a further decrease in T is operated, and the cycle is repeated until hydrate formation is observed. At this point, the elapsed time since the system reached 8.05°C (i.e., the T value of the first isothermal step) is taken as a surrogate of the induction period. It should be noted that, while in most instances hydrate formation was observed within the same temperature step among replicates of a same experiment (i.e., same tested compound), in a few cases hydrates formed at different temperatures. Also then, however, induction times showed a low variance, and correlated well with CC temperatures (Fig. 6). As a first approximation, the correlation between formation temperatures measured with constant cooling, and induction time “proxies” as measured with the present SIT method should be regarded simply as a macroscopic index supporting performance evaluations of a particular molecule (especially, an inhibitor). On a more fundamental level, the SIT method could be integrated within a quantitative frame of equations relating subcooling temperatures with induction times for a certain hydrate former in the absence and presence of inhibitors. For example, resort could be made to the theoretical framework developed by Kashchiev ACS Paragon Plus Environment
Page 13 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
and Firoozabadi.35 With this integration in place, one should be able to enhance the “surrogate” time values as measured by a SIT method, to obtain “proper” induction times as they are cleaned up from the spurious increase of driving force as provided by the cooling steps. More recently, Mali et al.12 found that the subcooling value correlates logarithmically with the mean induction time, but this relation does not fit our data, clearly owing to (i) the difference in the hydrate-forming gas (natural gas in Mali’s paper vs. pure methane in the present work), and (ii) the difference between experimental setups (i.e., rocking cells vs. stirred reactor), among other things. The recent, interesting papers by Maeda 36,37 and Kwak 38 are a vigorous attempt at finding a modelindependent relation between induction times and subcooling values. One of the concerns in those works is that the choice of a suitable cooling rate should take into account the issue of undersaturation of methane in water if cooling is too fast. Reportedly,37 constant cooling results approximately converge at rates of 3.6 and 7.2°C/h. This should be compared with the 0.5°C/h cooling rate in the present work, which should be sufficiently slow to reasonably assume that no undersaturation barrier is acting in our method, even if our experimental system (i.e., water volume) is much larger than the HP-ALTA. Another issue recognized by Maeda is that heat transfer limitations could potentially lead to cooling rate dependence of the nucleation rate. This problem is expected to be important in large-sized reactors, while Maeda’s HP-ALTA should be small enough to be relatively unaffected by thermal lag effects. Again, our system is much larger, but it is vigorously stirred (450 rpm +/- 1%) to minimize heat transfer limitation and, also, methane undersaturation effects. Maeda also set out to derive empirical relations between subcooling temperatures and mean induction times, but his approach seems subject to the condition of having access to a statistically large number of experimental values, which is not the case for our system/procedure.
ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 14 of 19
4. Conclusions We have proposed a variant of classical induction time experiments, whereby the driving force is mildly increased (0.5°C) in a step-wise manner among isothermal induction periods. It is shown that a limited number of induction time-like experiments gives surprisingly low variances of measured times, which are much lower than those for standard induction times as measured according to a usual, purely isothermal procedure. Moreover, SIT times were highly linearly correlated with hydrate formation temperatures obtained from constant cooling experiments, whereas those measured from IT experiments were not. Another difference between IT and SIT relates to the intrinsic difficulty of the former in comparing inhibitors having quite different inhibition performances. Indeed, in order for such a comparison to be strictly valid, induction time experiments should be carried out under the same subcooling conditions (i.e., same T from the equilibrium curve) for all tested inhibitors. This condition, however, often leads to either (i) hydrate formation during the cooling ramp while bringing the system to the target (subcooling) temperature when the compound under evaluation is a poor inhibitor; or (ii) almost indefinite (or at least very long) lag periods before hydrate occurrence in case of strong inhibitors. With the SIT method, it is possible to start from a very moderate subcooling value in order to avoid unwanted, early formation during the cooling process, with the working approximation that the following isothermal steps will give formation time values that can be considered as proxies or surrogates of induction times or, similarly, inhibition performances. Support to that approximation appears to stem from the very low variances observed with a limited number of replicates, which render this method ideally suitable for non-high throughput devices. Moreover, a very good correlation between SIT times and constant cooling values also supports the potential applicability of this method. As mentioned in the Discussion above, no attempt has been made so far to integrate time values as obtained with a SIT method into an empirical/theoretical framework such as those developed by Firoozabadi, Chapoy, Maeda and other researchers. Success in this attempt could give rise to more refined time data sets, which should be comparable with even higher reliability. ACS Paragon Plus Environment
Page 15 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
Corresponding author. E-mail address:
[email protected]; tel.: +39-0871-355 4791
Abbreviations: IT, induction time; SIT, step induction time; CC, constant cooling. REFERENCES (1)
Sloan, E. D. Clathrate Hydrates of Natural Gases, Third Ed., Chemical Industries Series. CRC Press (2007). 752 P. Fuel 2008. https://doi.org/http://dx.doi.org/10.1016/j.fuel.2008.03.028.
(2)
Carroll, J. J. Natural Gas Hydrates; Elsevier, 2014. https://doi.org/10.1016/C2013-0-13425-3.
(3)
Arvoh, B. K.; Hoffmann, R.; Valle, A.; Halstensen, M. Estimation of Volume Fraction and Flow Regime Identification in Inclined Pipes Based on Gamma Measurements and Multivariate
Calibration.
J.
Chemom.
2012,
26
(8–9),
425–434.
https://doi.org/10.1002/cem.2437. (4)
Dixit, S.; Soper, A. K.; Finney, J. L.; Crain, J. Water Structure and Solute Association in Dilute Aqueous Methanol. Europhys. Lett. 2002, 59 (3), 377–383. https://doi.org/10.1209/epl/i200200205-7.
(5)
Bloys, B.; Lacey, C. LaboratoryTesting and Field Trial of a New Kinetic Hydrate Inhibitor. In Offshore Technology Conference; Offshore Technology Conference, Richardson, TX (United States): United States, 1995; Vol. 2, pp 691–700. https://doi.org/10.4043/7772-MS.
(6)
Kelland, M. A. History of the Development of Low Dosage Hydrate Inhibitors. Energy and Fuels 2006, 20 (3), 825–847. https://doi.org/10.1021/ef050427x.
(7)
Kelland, M. A.; Svartaas, T. M.; Øvsthus, J.; Tomita, T.; Mizuta, K. Studies on Some Alkylamide Surfactant Gas Hydrate Anti-Agglomerants. Chem. Eng. Sci. 2006, 61 (13), 4290– 4298. https://doi.org/10.1016/j.ces.2006.02.016. ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
(8)
Page 16 of 19
Mokhatab, S.; Wilkens, R. J.; Leontaritis, K. J. A Review of Strategies for Solving GasHydrate Problems in Subsea Pipelines. Energy Sources, Part A Recover. Util. Environ. Eff. 2007, 29 (1), 39–45. https://doi.org/10.1080/009083190933988.
(9)
Mozaffar, H.; Anderson, R.; Tohidi, B. Reliable and Repeatable Evaluation of Kinetic Hydrate Inhibitors Using a Method Based on Crystal Growth Inhibition. Energy and Fuels 2016, 30 (12), 10055–10063. https://doi.org/10.1021/acs.energyfuels.6b00382.
(10)
Villano, L. Del; Kommedal, R.; Fijten, M. W. M.; Schubert, U. S.; Hoogenboom, R.; Kelland, M. A. A Study of the Kinetic Hydrate Inhibitor Performance and Seawater Biodegradability of a Series of Poly(2-Alkyl-2-Oxazoline)S. Energy and Fuels 2009, 23 (7), 3665–3673. https://doi.org/10.1021/ef900172f.
(11)
Colle, K. S.; Kelland, M. A.; Oelfke, R. H. Method for Inhibiting Hydrate Formation. EP0809619B1, 1999.
(12)
Mali, G. A.; Chapoy, A.; Tohidi, B. Investigation into the Effect of Subcooling on the Kinetics of
Hydrate
Formation.
J.
Chem.
Thermodyn.
2018,
117,
91–96.
https://doi.org/10.1016/j.jct.2017.08.014. (13)
Chua, P. C.; Kelland, M. A. Study of the Gas Hydrate Antiagglomerant Performance of a Series of Mono- and Bis-Amine Oxides: Dual Antiagglomerant and Kinetic Hydrate Inhibition Behavior.
Energy
and
Fuels
2018,
32
(2),
1674–1684.
https://doi.org/10.1021/acs.energyfuels.7b03789. (14)
Kelland, M. A. Designing Kinetic Hydrate Inhibitors - Eight Projects with Only Partial Success, but Some Lessons Learnt. Energy and Fuels 2017, 31 (5), 5046–5054. https://doi.org/10.1021/acs.energyfuels.7b00710.
(15)
Hase, A.; Cadger, S.; Meiklejohn, T.; Smith, R. Comparison of Different Testing Techniques
ACS Paragon Plus Environment
Page 17 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Energy & Fuels
for the Evaluation of Low Dosage Hydrate Inhibitor Performance. Proc. 8th Int. Conf. Gas Hydrates 2014, 12. (16)
Tohidi, B.; Anderson, R.; Mozaffar, H.; Tohidi, F. The Return of Kinetic Hydrate Inhibitors. Energy
and
Fuels
2015,
29
(12),
8254–8260.
https://doi.org/10.1021/acs.energyfuels.5b01794. (17)
May, E. F.; Wu, R.; Kelland, M. A.; Aman, Z. M.; Kozielski, K. A.; Hartley, P. G.; Maeda, N. Quantitative Kinetic Inhibitor Comparisons and Memory Effect Measurements from Hydrate Formation
Probability
Distributions.
Chem.
Eng.
Sci.
2014,
107,
1–12.
https://doi.org/10.1016/j.ces.2013.11.048. (18)
May, E. F.; Lim, V. W.; Metaxas, P. J.; Du, J.; Stanwix, P. L.; Rowland, D.; Johns, M. L.; Haandrikman, G.; Crosby, D.; Aman, Z. M. Gas Hydrate Formation Probability Distributions: The Effect of Shear and Comparisons with Nucleation Theory. Langmuir 2018, 34 (10), 3186– 3196. https://doi.org/10.1021/acs.langmuir.7b03901.
(19)
Dimo Kashchiev. Nucleation; Elsevier, 2000. https://doi.org/10.1016/B978-0-7506-46826.X5000-8.
(20)
Kashchiev, D.; Firoozabadi, A. Nucleation of Gas Hydrates. J. Cryst. Growth 2002, 243 (3– 4), 476–489. https://doi.org/10.1016/S0022-0248(02)01576-2.
(21)
Svartaas, T. M.; Ke, W.; Tantciura, S.; Bratland, A. U. Maximum Likelihood Estimation-A Reliable Statistical Method for Hydrate Nucleation Data Analysis. Energy and Fuels 2015, 29 (12), 8195–8207. https://doi.org/10.1021/acs.energyfuels.5b02056.
(22)
Martinez de Baños, M. L.; Carrier, O.; Bouriat, P.; Broseta, D. Droplet-Based Millifluidics as a New Tool to Investigate Hydrate Crystallization: Insights into the Memory Effect. Chem. Eng. Sci. 2015, 123, 564–572. https://doi.org/10.1016/j.ces.2014.11.018.
ACS Paragon Plus Environment
Energy & Fuels 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
(23)
Page 18 of 19
Atig, D.; Touil, A.; Ildefonso, M.; Marlin, L.; Bouriat, P.; Broseta, D. A Droplet-Based Millifluidic Method for Studying Ice and Gas Hydrate Nucleation. Chem. Eng. Sci. 2018, 192, 1189–1197. https://doi.org/10.1016/j.ces.2018.08.003.
(24)
Di Crescenzo, A.; Germani, R.; Del Canto, E.; Giordani, S.; Savelli, G.; Fontana, A. Effect of Surfactant Structure on Carbon Nanotube Sidewall Adsorption. European J. Org. Chem. 2011, 2011 (28), 5641–5648. https://doi.org/10.1002/ejoc.201100720.
(25)
Di Profio, P.; Canale, V.; D’Alessandro, N.; Germani, R.; Di Crescenzo, A.; Fontana, A. Separation of CO2and CH4from Biogas by Formation of Clathrate Hydrates: Importance of the Driving Force and Kinetic Promoters. ACS Sustain. Chem. Eng. 2017, 5 (2), 1990–1997. https://doi.org/10.1021/acssuschemeng.6b02832.
(26)
Di Profio, P.; Canale, V.; Germani, R.; Arca, S.; Fontana, A. Reverse Micelles Enhance the Formation of Clathrate Hydrates of Hydrogen. J. Colloid Interface Sci. 2018, 516, 224–231. https://doi.org/10.1016/j.jcis.2018.01.059.
(27)
CSMHYD http://hydrates.mines.edu/CHR/Software.html.
(28)
Norland, A. K.; Kelland, M. A. Crystal Growth Inhibition of Tetrahydrofuran Hydrate with Bis- and Polyquaternary Ammonium Salts. Chem. Eng. Sci. 2012, 69 (1), 483–491. https://doi.org/10.1016/j.ces.2011.11.003.
(29)
Di Profio, P.; Canale, V.; Marvulli, F.; Zappacosta, R.; Fontana, A.; Siani, G.; Germani, R. Chemoinformatic Design of Amphiphilic Molecules for Methane Hydrate Inhibition. J. Chemom. 2018, 32 (6). https://doi.org/10.1002/cem.3008.
(30)
Veluswamy, H. P.; Lee, P. Y.; Premasinghe, K.; Linga, P. Effect of Biofriendly Amino Acids on the Kinetics of Methane Hydrate Formation and Dissociation. Ind. Eng. Chem. Res. 2017, 56 (21), 6145–6154. https://doi.org/10.1021/acs.iecr.7b00427.
ACS Paragon Plus Environment
Page 19 of 19 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
(31)
Energy & Fuels
Sa, J. H.; Lee, B. R.; Park, D. H.; Han, K.; Chun, H. D.; Lee, K. H. Amino Acids as Natural Inhibitors for Hydrate Formation in CO2 Sequestration. Environ. Sci. Technol. 2011, 45 (13), 5885–5891. https://doi.org/10.1021/es200552c.
(32)
Sa, J.-H.; Kwak, G.-H.; Lee, B. R.; Park, D.-H.; Han, K.; Lee, K.-H. Hydrophobic Amino Acids as a New Class of Kinetic Inhibitors for Gas Hydrate Formation. Sci. Rep. 2013, 3 (1), 2428. https://doi.org/10.1038/srep02428.
(33)
Arjmandi, M.; Tohidi, B.; Danesh, A.; Todd, A. C. Is Subcooling the Right Driving Force for Testing Low-Dosage Hydrate Inhibitors? Chem. Eng. Sci. 2005, 60 (5), 1313–1321. https://doi.org/10.1016/j.ces.2004.10.005.
(34)
Lone, A.; Kelland, M. A. Exploring Kinetic Hydrate Inhibitor Test Methods and Conditions Using a Multicell Steel Rocker Rig. Energy and Fuels 2013, 27 (5), 2536–2547. https://doi.org/10.1021/ef400321z.
(35)
Kashchiev, D.; Firoozabadi, A. Induction Time in Crystallization of Gas Hydrates. J. Cryst. Growth 2003, 250 (3–4), 499–515. https://doi.org/10.1016/S0022-0248(02)02461-2.
(36)
Maeda, N. Nucleation Curves of Methane Hydrate from Constant Cooling Ramp Methods. Fuel 2018, 223 (February), 286–293. https://doi.org/10.1016/j.fuel.2018.02.099.
(37)
Maeda, N. Nucleation Curves of Model Natural Gas Hydrates on a Quasi-Free Water Droplet. AIChE J. 2015, 61 (8), 2611–2617. https://doi.org/10.1002/aic.14898.
(38)
Kwak, G. H.; Lee, K. H.; Lee, B. R.; Sum, A. K. Quantification of the Risk for Hydrate Formation during Cool down in a Dispersed Oil-Water System. Korean J. Chem. Eng. 2017, 34 (7), 2043–2048. https://doi.org/10.1007/s11814-017-0112-3.
ACS Paragon Plus Environment
