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Operational Study of a Monoethylene Glycol (MEG) Desalination Pilot Plant. Part I: Development of a New Method for the Estimation of MEG
Content in the Presence of NaCl Solid Particles
Ruozhou Houa, Rafael A. Lopez Rodrigueza, Simon A. Crawley-Boeveyb, Brian E.
Messengerb and Peter J. Martina
a School of Chemical Engineering and Analytical Science, The University of Manchester,
Manchester, M13 9PL, UKb Schlumberger, Buckingham Gate, Gatwick Airport, West Sussex, RH6 0NZ
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Abstract
Conductivity measurements at different temperatures were undertaken to investigate the
variation of conductivity as a function of sodium chloride solid particle concentrations in
sodium chloride-saturated aqueous monoethylene glycol (MEG) solutions. A methodology
has been developed that is capable of quantifying the relationship between the conductivity
measurement, sodium chloride particle concentration, temperature, and MEG concentration
in the brine-saturated aqueous solution. The results indicate that the conductivity decreases
exponentially as the solid sodium chloride particle concentration increases from 0 to 30 wt%,
whereas in the absence solid particles, the conductivity of sodium chloride-saturated aqueous
MEG solutions is a polynomial function of temperature and MEG concentration. It is
demonstrated that a single, universal empirical model can be developed to quantify the
relationship between the conductivity and relevant process parameters across the whole
experimental range. The methodology can be readily adopted for in situ monitoring of solid
salt particle concentration and estimation of MEG loss in a typical industrial MEG
reclamation process, leading to the establishment of more effective process control and
operation strategies.
Keywords:
Monoethylene glycol (MEG); Desalination; Pilot plant; Conductivity; Sodium chloride;
Sedimentation.
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1. Introduction
Monoethylene glycol (MEG) is a chemical that is widely used by the oil and gas industry to
thermodynamically inhibit gas hydrate formation in production wellheads and pipelines
(Mokhatab et al., 2007; Sloan and Koh, 2008). Gas hydrates are solid crystalline compounds,
composed of natural gas components and water that may form readily under typical deep-sea
pipeline operating conditions of high pressure and low temperature. They have a strong
tendency to agglomerate and plug the pipeline, leading to production disruptions. Gas hydrate
formation has long been considered a major risk in offshore deep-water gas production
operations. One economical solution to address this problem is to inject a large quantity of
MEG into wellheads and pipelines. This can depress the hydrate formation threshold to well
below the operating temperature, thus inhibiting the formation of hydrates in the gas
production process. The MEG concentration required to achieve gas hydrate inhibition can be
determined by the Hammerschmidt equation (Hammerschmidt, 1939). Typically, MEG is
added into the production pipeline in excess of the quantities required by the equation and the
MEG streams in gas production flowlines typically contain 30 to 60 wt% MEG.
MEG that has been in contact with the wet natural gas stream is often referred to as (water-)
rich MEG, and may contain a variety of contaminants, including dissolved salts from the rock
formations and corrosion products from the pipelines, as well as production chemicals and
dissolved and free hydrocarbons. Natural gas extracted from deep-water reservoirs, in
particular, often comes with a large quantity of produced water that may have salinity close to
saturation. Those concentrated brines, once mixed into the MEG stream in which the mineral
salts are generally much less soluble, carry a higher risk of scale formation and deposition in
the production line, which may also present a serious risk to flow assurance if left untreated
(Tomson et al., 2006).
The large volumes of MEG required to ensure effective inhibition of gas hydrate formation
throughout the entire length of the production pipeline necessitate recovery of the valuable
glycol components for re-injection. The production pipeline can be several hundred
kilometres in length and the MEG inventory of the systems can be in the order of several
thousand metric tonnes. The contaminants in the MEG stream must be removed before the
MEG can be recycled and injected back into the wellheads and pipelines. This is usually done
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in a MEG Recovery Unit (MRU). To date, there are more than 30 industrial-scale MRUs in
operation in the world.
The MEG recovery process can be divided into three operating sections (Nazzer and Keogh,
2006; Teixeira et al., 2016):
1. Pre-treatment section, where hydrocarbons and divalent salts are removed first from
the rich MEG stream by heating and by chemical dosing combined with filtration,
respectively.
2. Desalination section, where the liquid components (i.e., MEG and water) are
recovered by contacting the rich MEG stream with a hot high-MEG-content recycle
stream in a flash vaporisation separator. The vaporised MEG and water components
are condensed back into the salt-free liquid form and subsequently fed into the
regeneration section. The remaining monovalent salt content (mainly NaCl), on the
other hand, accumulates and crystallises from the MEG recycle stream and can be
discharged from the flash separator holding the high-MEG-content recycle stream.
3. Regeneration section, where the salt-free MEG and water components are separated
from each other via reflux distillation to produce a (water-) lean MEG stream for
reinjection at wellheads or pipelines.
One of the key targets in the MEG recovery process design and engineering is to achieve high
efficiency in salt removal whilst incurring a minimum loss of MEG. As deep-water gas
exploration is becoming an increasingly important contributor to global gas production,
demand for new and better technologies for more efficient salt removal and MEG recovery
has never been stronger.
In 2010, Cameron Flow Control Technology (UK) Ltd., now Cameron, a Schlumberger
company, donated a 1/100th scale, 100 kg/h feed flow rate MEG desalination pilot plant
system to The University of Manchester. The research presented here investigates some of
the key processes involved in the desalination section operation, aiming to develop more
efficient operation guidance and strategies for implementation at MEG desalination plants.
The donated system focuses on the liquid components recovery and monovalent salt removal
operations. It employs Schlumberger proprietary MEG reclamation technology, in which the
monovalent salt crystals formed in the flash separator settle under gravity through a 6 meter
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high, NaCl-saturated brine filled column (the downcomer) before disposal. The height of the
downcomer facilitates the dis-entrainment of MEG from the settling salt crystals, resulting in
a significant reduction in MEG losses during salt disposal and in a reduced demand for fresh
MEG to replace these MEG losses. Figure 1 shows a schematic illustration of the system.
Figure 1: A schematic
illustration of the pilot plant MEG desalination system.
To minimise MEG losses during salt disposal, it is important that the MEG content in the
downcomer is monitored in situ continuously. This enables the operators to better control the
salt removal process.
However, the downcomer contains a multicomponent dynamic inventory, in which the
crystallised salt particles settle through a sodium chloride-saturated aqueous solution, with
gradients of temperature, dissolved salt content, MEG concentration, and solids concentration
along the whole height of the downcomer. Real-time measurements of MEG concentrations
in such a complex environment are extremely difficult because of this multivariant
complexity.
It has been reported that electrical conductivity measurements coupled with the measurement
of ultrasound velocity (Vajari, 2012; Yang et al., 2012; Yang and Tohidi, 2013) or density
(Sandengen and Kaasa, 2006) could be used to determine both the MEG and salt
concentrations from tertiary solutions consisting of water, MEG, and NaCl. Field trials
showed that the technique required only a few simple operations and was capable of rapid,
near-real-time estimation of MEG and salt contents at offshore platforms (Bonyad et al.,
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Salt-Free Rich MEG
Salt Particles Salt
Pump
Salt Tank
Recycle Pump
Recycle Heater
Condenser
Brine Filled Downcomer
Feed (Rich MEG)
Flash Separator
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2011; Macpherson et al., 2012). Unfortunately, this technique cannot be applied to the in situ
MEG content analysis inside the downcomer, as the presence of the salt crystal particles will
interfere severely with the ultrasound velocity, density, and conductivity measurements,
rendering the approach infeasible.
In this study, we demonstrate that based on conductivity and temperature measurement
combinations, a new and simple method can be developed to estimate both the MEG
concentration and salt solid particle concentrations for NaCl-saturated MEG-water solutions.
The method can be readily adapted for in situ MEG concentration analysis in the downcomer
section, thereby enabling more effective downcomer operation and minimising MEG losses.
The paper is divided into two parts:
Part I focuses on the methodology development. Conductivity measurements at different
temperatures and different NaCl solid particle concentrations in NaCl-saturated aqueous
MEG solutions were conducted. The measurements enable establishing an empirical
relationship describing the variation of conductivity as functions of temperature, MEG
concentration, and NaCl particle concentration in NaCl-saturated aqueous solutions. The
inverse solutions to the functions lead to the estimation of the MEG and salt solid
concentrations from the measured conductivity data.
Part II focuses on the MEG loss control strategy. In situ MEG concentration data inside the
downcomer was collected using the methodology developed herein. The data were then
utilised to determine how the downcomer could best be operated to control and minimise the
MEG losses in the pilot plant MEG desalination system.
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2. Experimental
2.1 Materials
The MEG was industrial grade, minimum 99% purity. It was purchased from Hydra
Technologies Ltd., U.K., and used as received. NaCl salt used to make saturated solutions
was a vacuum dried, off-the-shelf product, also of industrial grade. The salt used to make
solid concentrations was recrystallized NaCl product from the pilot plant MEG desalination
system, such that the particle size distribution was representative of that in the operating
process. This was collected from the salt tank, washed by the saturated solution, filtered,
dried in an oven at 70 °C, and finally sieved into the 40 to 125 µm size range for
experimental use. Figure 2 confirms that the averaged NaCl particle size within the
downcomer ranged from 38.4 (D10) to 124.9 (D90) µm and that the feed flowrate had no
effect on the particle size distribution. Appendix A1 details how the samples were taken from
the downcomer and particle size distributions were measured.
Ordinary tap water was used throughout the experiments.
Figure 2: NaCl solid particle size distributions along the height of the downcomer at a salt
influx of (a) 300, (b) 500, (c) 700, and (d) 900 kg/h·m², respectively. D10, D50 and D90 refer
to the particle sizes where 10%, 50% and 90% of the distribution lie below the respective
diameters.
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D10 D50 D90
(c) (d)
(b)(a)
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2.2 Procedure
The experiments were conducted in a factorial design fashion, with five levels of MEG
concentration (in salt-free terms), five levels of temperature, and seven levels of NaCl solid
particle concentration investigated. Table 1 lists the detailed experimental conditions.
Table 1: Detailed experimental condition matrix.
MEG Concentration(wt% in salt-free terms)
Temperature(°C)
NaCl Solid Particle Concentration (wt%)
100 24, 45, 55, 75, 89 0, 5, 10, 15, 20, 25, 3075 23, 36, 50, 69, 79 0, 5, 10, 15, 20, 25, 3050 22, 42, 52, 61, 70 0, 5, 10, 15, 20, 25, 3025 29, 40, 49, 59, 69 0, 5, 10, 15, 20, 25, 300 22, 41, 52, 62, 72 0, 5, 10, 15, 20, 25, 30
Following industry convention, in this paper the MEG contents are expressed in salt-free
terms. The real MEG content CMEG,R in wt% and salt-free MEG content CMEG,SaF in wt% are
connected via the following equation:
CMEG , R=CMEG , SaF (100−CNaCl ,L
100) (1)
where CNaCl,L denotes the salt concentration in wt% in solution.
Each experiment set started with the preparation of a MEG + water solution of different MEG
concentration (e.g., 100% MEG + 0% water, or 75% MEG + 25% water). An excess of
vacuum-dried NaCl was then added to the prepared solution, and the mixture was heated to
the required temperature under constant stirring on an IKA C-Mag HS 7 magnetic stirrer with
ceramic heating plate to make the NaCl-saturated MEG + water solution of different MEG
concentration. After the solution was fully saturated, stirring was stopped. Allowing time for
the NaCl particles to fully settle, approximately 200 ml of the NaCl-saturated supernatant
liquid was then poured into a pre-weighed 250 ml beaker.
The NaCl-saturated supernatant liquid was kept under constant stirring, and the temperature
was maintained at the target value. Drops of fresh MEG + water solution of the same MEG
concentration were added as necessary for fine adjustment of the saturation level. After the
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beaker containing the saturated solution was weighed, from which the net weight of the
saturated solution could be extracted, the conductivity probe was lowered into the solution to
start the conductivity and temperature measurements. The measurements continued under
constant stirring, with sieved and recrystallized NaCl solid particles added in steps to make
5%, 10%, 15%, 20%, 25%, and 30% of solid concentration by weight in the NaCl-saturated
MEG + water solution.
Both the conductivity and temperature measurements were collected using a pre-calibrated
Hamilton Conducell 4USF-PG 120 four-electrode conductivity probe. Data was logged onto
a computer through dedicated software at a rate of 12 data points per minute. Typically for
each experimental condition set, about 60 conductivity/temperature measurements were
collected over a 5-minute period. The averages of these measurements were taken as the
measured conductivity and temperature values for the experimental condition set. All the raw
conductivity data sets showed good consistency. The typical standard deviation for any
randomly chosen data set was around 0.3% of the mean value.
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3. Results and Discussions
3.1 Conductivity measurement results
Figure 3 shows the conductivity variation as a function of the solid NaCl concentrations in
different concentrations of NaCl-saturated MEG solutions and at different temperatures. Note
that the measured conductivity data are presented in the logarithmic scale. In most cases, the
logarithmic conductivity value decreased linearly as the solid NaCl concentration increased.
The slope and intercept of each linear data set depended on the temperature and MEG
concentration in the NaCl-saturated solution. Mathematically this is equivalent to the
following expression:
log κS+L=logκ L (T ,CMEG , SaF )+α ( T , CMEG , SaF ) .CNaCl , S (2)
in which S+L denotes the conductivities at different concentrations of NaCl solids in mS/cm
and L denotes the conductivities, in mS/cm, of solid-free NaCl-saturated solutions of
different MEG concentrations at different temperatures, which also represents the intercepts
of the fitted lines on the vertical axis; CNaCl,S denotes the solid NaCl concentration in wt%; α
is the slope coefficient, to be determined through data fitting; and T is the temperature in °C.
L(T, CMEG,SaF) and (T, CMEG,SaF) mean that these two parameters are a function of
temperature and salt-free MEG concentration. Note that (T, CMEG,SaF) will be negative, which
makes S+L converge as CNaCl,S increases.
To build a valid predicative model to account for the variation of S+L as a function of CNaCl,S,
a separate model describing the L as a function of T and CMEG,SaF also has to be developed.
One of the approaches to achieve this is to use the thermodynamic equilibrium model that is
based on an extended form of the mean-spherical approximation (MSA) theory coupled with
a mixing rule (Wang et al., 2004) to calculate the conductivities of saturated MEG + water +
NaCl solutions at different temperatures. Unfortunately, for our specific system, very limited
number of parameter data can be found in literature (Wang et al., 2013). Thus, in this work
we decided to adopt an empirical data fitting approach for the L predictions instead.
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Figure 3: Conductivity variations versus solid NaCl concentrations at different temperatures
in NaCl-saturated solutions of (a) 100% MEG, (b) 75% MEG, (c) 50% MEG, (d) 25% MEG,
and (e) 0% MEG. Discrete markers represent the measured conductivity data, whereas dotted
lines are calculated conductivity values using equation (7).
Figure 4 plots the change of L as a function of temperature at different salt-free MEG
concentrations in the NaCl-saturated MEG solutions. In the absence of solid particles, the
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Cond
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S/cm
)
NaCl Solid Concentration (wt%)
100% MEG at 24 °C 100% MEG at 45 °C 100% MEG at 55 °C 100% MEG at 75 °C 100% MEG at 89 °C
10
100
0 5 10 15 20 25 30
Cond
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S/cm
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NaCl Solid Concentration (wt%)
75% MEG at 23 °C 75% MEG at 36 °C 75% MEG at 50 °C 75% MEG at 69 °C 75% MEG at 79 °C
30
300
0 5 10 15 20 25 30
Cond
uctiv
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NaCl Solid Concentration (wt%)
50% MEG at 22 °C 50% MEG at 42 °C 50% MEG at 52 °C 50% MEG at 61 °C 50% MEG at 70 °C
70
700
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Cond
uctiv
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S/cm
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NaCl Solid Concentration (wt%)
25% MEG at 29 °C 25% MEG at 40 °C 25% MEG at 49 °C 25% MEG at 59 °C 25% MEG at 69 °C
100
1000
0 5 10 15 20 25 30
Cond
uctiv
ity (m
S/cm
)
NaCl Solid Concentration (wt%)
0% MEG at 22 °C 0% MEG at 41 °C 0% MEG at 52 °C 0% MEG at 62 °C 0% MEG at 72 °C
(e)
(d)(c)
(b)(a)
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averaged conductivity of NaCl-saturated aqueous MEG solutions increases with either
decreasing MEG concentration or increasing temperature. Close inspection of the graphs in
Figure 4 indicates that the conductivity data seem to fit well into a polynomial function of the
temperature with MEG concentration-dependent coefficients:
κL=∑n
βn (CMEG , SaF ) .T n(3)
where n = 1, 2, 3, and n(CMEG,SaF) denote coefficients, which are a function of the salt-free
MEG concentration and can be determined through data fitting.
The same could also be true to fit L into a polynomial function of the MEG concentration
with temperature-dependent coefficients:
κL=∑m
γm (T ) . (CMEG , SaF )m (4)
where m = 1, 2, 3, and m(T) denote coefficients, which are a function of the temperature.
Figure 4: Variation of L as a function of temperature at different salt-free MEG
concentrations in the NaCl-saturated MEG solutions, with measured data (discrete markers)
and model-fitted function (solid lines).
3.2 Data fitting
All the data fitting work was done on the Matlab platform, using multivariate regression
techniques and least squares analysis (Draper and Smith, 1981). Conductivity L can be
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considered to be a function of temperature and salt-free MEG concentration. This can also be
expressed by applying the separation of the variables as
κL ( T , CMEG , SaF )= f (T ) ∙ g (CMEG , SaF) (5)
where f(T) and g(CMEG,SaF) are functions of T and CMEG,SaF only, respectively. It can be inferred
from Figure 4 that the L data would fit well to a polynomial function of T or CMEG,SaF when
the other variable is fixed. It is thus plausible to put f(T) and g(CMEG,SaF) into a polynomial
function of T and CMEG,SaF, respectively.
After inspection of the experimental data, it was found that an optimal L model takes the
following form:
κL=∑k=0
3
ak T k+∑i=1
3
∑j=1
4
bij Ti(100−C MEG ,SaF)
j (6)
The model coefficients ak and bij are listed in Table 2 and were determined through data
fitting of the raw L data set.
Figure 4 illustrates a comparison of the measured and model-fitted L data. The data cover
the MEG concentration (on a salt-free basis) ranging from 0% to 100% and temperature
ranging from 20 to 90 °C. They are the respective compositional and temperature ranges
expected within the downcomer. It can be seen from Figure 4 that the model is able to
effectively track the variation of measured data within the experimental range. The typical
comparative errors between the measured and calculated conductivity values are well below
5%. The squared Pearson’s correlation coefficient, which is a measure of the linear
correlation between the measured and calculated L data, was calculated to be R2 = 99.89%.
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Table 2: L model coefficients.
a0 to a3 b11 to b14 b21 to b24 b31 to b34
2.862458061 0.013718637 0.000558272 –5.7528E-06
–0.077034183 0.00123537 –7.157E-05 6.93116E-07
0.010410457 –1.57337E-05 1.53244E-06 –1.52117E-08
–6.01435E-05 1.71693E-07 –1.13449E-08 1.06858E-10
For the S+L data, a similar approach was applied to determine the coefficient (T, CMEG,SaF)
shown in equation (2). The following model was found to produce the optimum results:
log( κS+L
κ L)=(c0+c1 T+c2 (100−CMEG ,SaF )) .CNaCl ,S (7)
The fitted values of the coefficients c0, c1, and c2 listed in Table 3 were determined through
the data fitting of the log(S+L/L) versus CNaCl,S values at different temperatures. A
comparison of the measured and model calculated S+L data is illustrated in Figure 3, which
shows that the model fits well to the measured data across all the experimental ranges. The
mean sum of the squares of the residuals is only 10.25 for 175 S+L data points ranging from
4.46 to 440.06 mS/cm. The squared Pearson’s correlation coefficient R2 is 99.94%.
Table 3: S+L model coefficients.
c0 c1 c2
–0.004367837 –1.44819E-05 9.84524E-06
3.3 Inverse algorithm
We have so far developed empirical models to calculate L from temperature T and salt-free
MEG concentration in NaCl-saturated solution CMEG,SaF and S+L from L, T, CMEG,SaF and NaCl
solid concentration CNaCl,S. In practical applications such as a MEG desalination system,
however, it is desirable to deduce CMEG,SaF and CNaCl,S from the measured S+L data.
All conductivity probes were equipped with integrated temperature measurement, thus T can
be considered as a known quantity. Moreover, at any fixed T, the values of L and CMEG,SaF are
both unique. This implies that it is possible to acquire CMEG,SaF and CNaCl,S through a dual
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measurement approach, which, in the current case, includes taking the conductivity
measurements of the solid NaCl suspension (i.e., S+L) and the supernatant liquid phase only
(i.e., L) subsequently. The second measurement leads to a solution for CMEG,SaF. This can then
be used, together with the S+L measurement, to produce CNaCl,S.
Figure 5: Comparisons of the actual and inversely calculated (a) salt free MEG concentration, CMEG,SaF and (b) NaCl solid concentration, CNaCl,S.
We rearranged the L model from equation (6) into the following form:
(100−CMEG , SaF )4+∑i=1
3
bi 3T i
∑i=1
3
bi 4 T 4( 100−CMEG , SaF )3+
∑i=1
3
bi 2T i
∑i=1
3
bi 4 T 4( 100−CMEG , SaF )2
+∑i=1
3
bi 1T i
∑i=1
3
b i 4T 4(100−CMEG , SaF )+
∑k=0
3
ak T k−κ L
∑i=1
3
bi 4 T 4=0 (8)
The roots of this equation are the eigenvalues of its companion matrix (Williams, 2010). Out
of the four roots for (100 – CMEG,SaF), only one root carries realistic physical meaning. With
(100 – CMEG,SaF) determined, CNaCl,S can be calculated from the logarithmic L model:
CNaCl ,S=1
[c0+c1T +c2 (100−CMEG , SaF )]× log( κS+L
κL) (9)
15
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MEG
Con
cent
ratio
n (S
alt F
ree,
wt%
)
Data Point
Actual MEG ConcentrationCalculated MEG Concentration
0
5
10
15
20
25
30
35
40
0 50 100 150
NaC
l Sol
id C
once
ntra
tion
(wt%
)
Data Point
Actual NaCl Solid Concentration Calculated NaCl Solid Concentration
(b)(a)
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We also found that CNaCl,S can be equally well reproduced using the following simpler
polynomial model:
CNaCl ,S=d1( κL
κS+ L−1)+d2( κL
κS+L−1)
2
(10)
where d1 and d2 were determined through data fitting to be 117.8532 and –114.9946,
respectively. Figure 5 illustrates the comparison of the actual and inversely calculated CMEG,SaF
and CNaCl,S values. Accurate MEG concentration predictions are achieved, with the differences
between the calculated and real values at different levels almost negligible. The accuracy of
CNaCl,S predictions is also reasonably satisfactory, considering the complex nature of
particulate slurries (MacTaggart et al., 1993).
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4. Model Validation
To validate the model predictions, six samples were taken from the downcomer during a
randomly chosen operational run of the MEG desalination pilot plant. They were taken from
each of the six conductivity probes installed on the downcomer to map the conductivity
profile changes along the full height of the downcomer. The operation of the pilot plant and
exact locations of the conductivity probes are discussed in detail in Part II of this paper. The
collected samples were stored in 500 ml plastic sample bottles and allowed to cool to room
temperature. They were then poured into 500 ml glass beakers where the conductivity
measurements were conducted. A Hamilton Conducell 4USF ARC 120 conductivity probe
was used to measure the suspension conductivity S+L (under constant mixing using an IKA
C-Mag HS 7 magnetic stirrer) and supernatant conductivity L of each sample. The data were
then fed to equations (8) and (10) to calculate the CMEG,SaF and CNaCl,S, respectively. A small
representative portion of the particle suspension and supernatant liquid were also withdrawn
from each sample. They were used to measure CMEG,SaF and CNaCl,S through an evaporation
method in each sample (see Appendix A2 for descriptions of the method in detail).
Table 4 compares the model-calculated CMEG,SaF and CNaCl,S with the measured data. The
models not only correctly presented the trend of changes but also produced both CMEG,SaF and
CNaCl,S predictions with reasonably good accuracies.
Table 4: Model validation results.
Sample
Location to
Top of the
Downcomer
(m)
Laboratory Conductivity
Measurements
Inverse Algorithm
Predictions
Measured Data by
Evaporation
T
(°C)
S+L
(mS/cm)
L
(mS/cm)
CMEG,SaF
(%)
CNaCl,S
(%)
CMEG,SaF
(%)
CNaCl,S
(%)
0.00 22.8 67.82 80.16 40.4 18 38.1 15
0.85 22.8 66.97 78.95 40.9 17 38.2 16
1.85 22.8 69.46 79.15 40.8 14 36.8 16
2.85 22.8 71.26 81.34 40.0 14 34.5 15
3.85 22.8 85.03 91.26 36.3 8 31.0 13
4.85 22.8 113.23 118.64 27.5 5 25.5 9
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5. Conclusions
In this investigation, electrical conductivity measurements at different temperatures were
undertaken to investigate the variation of conductivity as a function of NaCl solid particle
concentration in sodium chloride-saturated aqueous MEG solutions. Experimental results
show that the conductivity decreases exponentially as the solid NaCl particle concentration
increases from 0 to 30 wt%, whereas, in the absence solid particles, the conductivity of NaCl-
saturated aqueous MEG solutions is a polynomial function of temperature and MEG
concentration. Empirical models, based on multivariate regression analysis of the
experimental conductivity measurement data, were developed to quantify the variation of
conductivity as a function of the solid NaCl concentration, temperature, and MEG
concentration in both the presence and absence of solid NaCl particles. Typical model
prediction errors for conductivities are well below 5% across the whole experimental range
covering solid NaCl concentration changes from 0 to 30%, temperature changes from 20 to
90 °C, and MEG concentration changes in salt-free terms from 0 to 100%.
The models can also be utilised, through inverse algorithms, for the determination of solid
NaCl particle and MEG concentrations or for determination of MEG losses in MEG
reclamation processes. The calculation of solid NaCl particle concentrations, in particular,
requires a dual measurement approach, which includes taking the subsequent conductivity
measurements of both the particle suspension and the supernatant liquid phase only.
As the measurement of conductivity is, in principle, a non-specific technique, it is reasonable
to assert that the same methodology can be readily tailored into a real-time solid particle
concentration monitoring tool for any solid-liquid two-phase particulate suspension system.
For such a tool to be valid, the conductivity of the supernatant liquid phase should be
independent of the presence of solid particulate materials. The resulting model would be
established through robust on-process training and calibrations.
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Acknowledgements
This work was financially supported by The University of Manchester EPSRC Knowledge
Transfer Account (KTA002) and Cameron Flow Control Technology (UK) Limited, now
Cameron, a Schlumberger company. The authors wish to acknowledge Mr Andrew Downes
from Cameron Flow Control Technology (UK) Limited for his assistance in running the
MEG pilot plant.
Declaration of Interests
None
Research Data
http://dx.doi.org/10.17632/bjhc5t86vw.1
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Nomenclature
α Slope coefficient of log S+L vs log L (–)
ak, bij Coefficients of L model (mS/cm °Ck, mS/cm °Ci, respectively)
n Coefficient of L polynomial function as function of CMEG,SaF (mS/cm °Cn)
m Coefficient of L polynomial function as function of T (mS/cm)
c0, c1, c2 Coefficients of S+L/L model (–,°C-1, –)
CMEG,R MEG wt% in MEG-water-NaCl solution based on all solution constituents
CMEG,SaF MEG wt% in MEG-water-NaCl solution based on MEG and water only
CNaCl,L NaCl wt% in MEG-water-NaCl solution wt % based on all solution
constituents
CNaCl,S Solid NaCl particle wt% suspended in solution
d1, d2 Coefficients of simplified polynomial for CNaCl,S
f, g Generic functions
i, j, k Generic indices
L Conductivity of MEG-water-NaCl solution with no suspended solids (mS/cm)
S+L Conductivity of MEG-water-NaCl solution also containing suspended solid
NaCl particles (mS/cm)
T Temperature (°C)
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Appendices
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A1: Measurements of NaCl solid particle size distributions inside the downcomer.
Samples were withdrawn from six sample points at 1 meter intervals in the 6 meter tall
downcomer through the sampling lines. The sampling lines were all made of 3/8 inch OD
stainless steel pipes and were inserted into the downcomer with a 45° downwards angle to
avoid NaCl solid accumulation at the tip of each sampling point. The distances from each
sampling point tip to the top of the downcomer were 0.0, 0.85, 1.85, 2.85, 3.85, and 4.85 m,
respectively. The collected samples were maintained at their original temperatures and
measured on a Malvern Mastersizer 3000 in saturated brines for particle size distributions
within at most 3 hours after the sample collections.
The salt influx and feed flow rate are related via the following equation:
SI NaCl=QM , F CNaCl ,L , F
π4 ( DDown )2 (A1-1)
where SINaCl denotes the salt influx in the downcomer in kg/h·m², QM,F denotes the mass flow
rate of the feed to the flash separator in kg/h, CNaCl,L,F denotes the dissolved NaCl
concentration in the feed stream in wt%, and DDown denotes the inner diameter of the
downcomer in m.
Figure A1: A typical size distribution profile for NaCl solid samples taken from the
Downcomer at different salt influxes.
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2
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6
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10
12
14
1 10 100 1000
Dist
ributi
on (%
)
NaCl Particle Size (µm)
4.85 m to top of the downcomer,salt influx 700 kg/h
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A2: Description of the evaporation method to measure the CMEG,SaF and CNaCl,S in a sample.
The method also took a dual measurement approach. A small quantity of NaCl-saturated
supernatant liquid (~0.5 ml) was taken from each sample and weighed in pre-weighed Petri
dishes. The sample was heated in a vacuum oven (Thermo Scientific, Vacutherm) to 120 °C
at 0.1 bar until all the liquid content was evaporated. The Petri dishes were weighed again,
from which the dissolved NaCl content in each NaCl-saturated supernatant liquid sample,
CNaCl,L, could be calculated. The MEG content, CMEG,R, or in salt-free terms CMEG,SaF, could be
subsequently deduced using the linear interpolation in the NaCl in water + MEG saturation
charts. Then the same procedure was followed with the solid suspension samples, from which
the total NaCl content in each sample was calculated. The solid NaCl concentration, CNaCl,S,
was obtained by determining the dissolved NaCl content from the total NaCl content.
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