A novel method for the determination of black liquor viscosity by multiple headspace extraction gas chromatography

A novel method for the determination of black liquor viscosity by multiple headspace extraction gas chromatography

Journal of Chromatography A, 1320 (2013) 125–129 Contents lists available at ScienceDirect Journal of Chromatography A journal homepage: www.elsevie...

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Journal of Chromatography A, 1320 (2013) 125–129

Contents lists available at ScienceDirect

Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma

A novel method for the determination of black liquor viscosity by multiple headspace extraction gas chromatography Hui-Chao Hu a , Xin-Sheng Chai a,b,∗ a b

State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou, China Institute of Paper Science and Technology, Georgia Institute of Technology, Atlanta, USA

a r t i c l e

i n f o

Article history: Received 10 September 2013 Received in revised form 15 October 2013 Accepted 15 October 2013 Available online 24 October 2013 Keywords: Black liquor Viscosity Methanol Multiple headspace extraction Gas chromatography

a b s t r a c t This work demonstrates a novel method for the determination of viscosity in the concentrated black liquors from pulp mill recovery process. The method is based on the kinetic release of methanol (a vapor tracer) to the headspace in a sample closed vial by a multiple headspace extraction gas chromatographic technique. Both theoretical and empirical models were proposed for establishing the correlation with the reference method. The results showed that the correlation using either of the models is excellent for the tested black liquor samples (at 110 ◦ C). The presented method is simple and practical and can be a valuable tool for black liquor viscosity related research and applications. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Because of the great demand for energy resources in the world, lignocellulosic biomass, as a potential feed stock, has been investigated worldwide for the production of biofuel for many years [1]. Until now, the economic feasibility is still a big challenge in the commercialization of fuel production based on biomass based materials [2]. On the other hand, the energy utilization by combusting biomass materials, e.g., the bagasse from sugar mills and black liquor in pulp mills, is still an important approach to compensate the energy consumption in many industries [3,4]. Therefore, besides the effort to develop new technologies for biofuel production, it is also necessary to conduct an optimization study on the operation parameters in the existing industrial processes, aiming at a cost-effective production in the energy conversion from biobased materials [3,4]. Black liquor, containing lignins, carbohydrates, and inorganic chemicals (e.g., sodium salts), is the spent cooking liquor from alkaline pulping process when digesting lignocellulosic materials into paper pulp [5]. The direct discharging of black liquors to surface water not only causes serious environmental pollution, but also results in immense waste for organic materials and inorganic chemicals [6]. Therefore, the chemical recovery (including evaporation, combustion, and re-caustization) in pulp mills is an important process for achieving energy conversion from lignins/carbohydrates

∗ Corresponding author. Tel.: +86 20 87113713; fax: +86 20 87113713. E-mail address: [email protected] (X.-S. Chai). 0021-9673/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.chroma.2013.10.051

and chemical recycle from inorganic compounds in black liquors [7]. The viscosity of the concentrated black liquor from evaporators is one of the important parameters that not only affect black liquor delivery but also combustion efficiency in the recovery boiler [8]. The drop size of the liquor and its distribution as it sprays into the boiler is highly related to the liquor viscosity (for a given nozzle), which is traditionally adjusted by controlling the liquor temperature [9]. Clearly, as the viscosity of black liquor is crucial for the effective liquor combustion, it is an important parameter to the optimization of combustion boiler performance in mill operation. Because of the harsh conditions such as corrosive nature of black liquor, as well as the high solid content (>65%), and the high operating temperature (>100 ◦ C) [10,11], the laboratory bench type viscometers cannot be applied to measure black liquor viscosity. Currently, the pilot-scale in-line viscometer (e.g., Nameter) is commercially available and could be used in the black liquor application. However, such system requires a significant amount of liquor to run a test and the measurement operation is costly, it is impractical to be used for the lab-scale testing. Headspace gas chromatography (HS-GC) is a powerful tool to determine volatile organic compounds (VOCs) in the samples having a complicated matrix [12]. In the previous studies, we have demonstrated the techniques using toluene as a tracer for the determination of amount of microstickies (adhesives) in paper machine whitewater by HS-GC [13] or using pentane as a tracer to monitor the monomer conversion during methyl methacrylate emulsion polymerization process by a multiple headspace extraction (MHE) GC technique [14].


H.-C. Hu, X.-S. Chai / J. Chromatogr. A 1320 (2013) 125–129

Table 1 Total dissolved solid contents (TDS), density and viscosities in the concentrated black liquors. Sample ID

TDS (%)

Density (g/mL)

Viscositya (cP)

1# 2# 3# 4#

47.0 52.8 69.9 74.3

1.30 1.34 1.45 1.48

2.53 6.63 210 490


Measurement using Nameter in-line viscometer.

The methods are based either on the VOC tracer compound adsorption on the surface of microstickies (hydrophobic) particles or on the dissolving of the monomer droplets, which affects the tracer concentrations in the headspace, from which the amount of microstickies or monomer in the liquid medium can be calculated through calibrations. As the viscosity of liquid could affect the diffusion of VOC species in liquid medium [15] and thus the emission to the vapor phase, we believe that the VOC tracer based HS-GC technique can also be applied to the determination of the viscosity in black liquor. The objective of the present study is to demonstrate VOC tracer based MHE-GC technique for the determination of black liquor viscosity at a temperature of interest. The work focuses on introducing the methodology of the present method and establishing a correlation between the vapor tracer signal measured by MHE-GC and the viscosities of the liquors measured by the Nameter in-line viscometer on a set of concentrated black liquor samples. The other issues such as the selection of VOC tracer, sample size, and effect of sample matrix on the viscosity measurement in the present method were also discussed. 2. Experimental

Table 2 Symbols and definitions. Symbol

Definition and unit

t C Ci

mass transfer time, min concentration of VOC tracer in gas phase at time t, mol/m3 concentration of VOC tracer in gas phase at the ith extraction, mol/m3 concentration of VOC tracer in gas phase at the time of 0 min, mol/m3 concentration of VOC tracer in gas phase at equilibrium, mol/m3 mass transfer resistance, min liquid-film mass transfer resistance, min gas-film mass transfer resistance, min Henry Law’s constant of VOC tracer diffusion coefficient, m2 /min diffusion coefficient of VOC tracer in liquid phase, m2 /min diffusion coefficient of VOC tracer in gaseous phase, m2 /min coefficient in Eq. (3-2), m−2 coefficient in Eq. (3-3), m−2 Boltzmann constant, 1.38E-23 J/K equilibrium temperature, K molecular radius of VOC tracer, m viscosity of gas or liquid, Pa s viscosity of gas phase, Pa s viscosity of black liquor, Pa s coefficient in Eq. (7), min coefficient in Eq. (3-2), min/(cP) extraction factor response of HS-GC, mol/m3 coefficient in Eq. (9) slope of Eq. (13) intercept in Eq. (13) intercept in Eq. (16) uncertainty of the intercept in Eq. (16) slope 10 Eq. (16)

C0 CE R RL RG H D DL DG k1 k2 kB T r  G L a b ϕ f  s ␧ e e k

allowed to proceed with a MHE-GC measurement at an interval time of 10 min.

2.1. Samples 3. Results and discussion The black liquor sample was obtained from a kraft pulp mill in North America and the liquor concentrating was conducted in a pilot-scale evaporation system at the Pulp and Paper Research Institute of Canada. The liquors were withdrawn at different stages of evaporation, and their viscosities (see Table 1) were tested by an in-line pilot-scale viscometer (Nameter viscoliner® 500, USA). The total solid content of the liquors was determined by a TAPPI standard method [16]. The density of the liquors was calculated according to its total solid content [17]. 2.2. Apparatus and operations A GC system (Model HP-6890, USA) and an automatic headspace sampler (HP-7694, USA) were used for HS-GC measurement. The GC system was equipped with a flame ionization detector and a HP-5 capillary column (Agilent, USA) operating at a temperature of 30 ◦ C with helium carrier gas (flow rate = 3 mL/min). The headspace operating conditions were as follows: gentle shaking for the sample equilibration at the temperature of interest, vial pressurization time = 0.2 min, and sample loop fill time = 0.2 min. The volume of the headspace sample vials was 21.6 mL. 2.3. Measurement procedures According to the density of the concentrated black liquors, an accurate weight (equivalent to 2 mL) of the liquor was added into an empty headspace vial. The sample vial was immediately sealed with a PTFE/butyl septum and aluminum cap. Then, the vial was equilibrated at 110 ◦ C for 5 min in the headspace sampler and

3.1. Principle of the method 3.1.1. The mass transfer resistance of volatile species and liquid viscosity When adding a liquid sample in a closed container and keep a certain volume of vapor phase (headspace), there is a trend for the volatile species in the liquid sample to transfer to the vapor phase. The mass transfer behavior of the volatile species in the boundary of vapor–liquid phase can be described by the two-film theory [18], i.e., for a given gas–liquid exposed surface and time, the change of volatile species concentration in the vapor phase is proportional to the mass transfer driving force and inversely proportional to the resistance (R), i.e., CE − C dC = R dt


Here, the driving force is the difference between the equilibrium concentration and the concentration at time (t) of the species in the vapor phase. All symbols and their definitions are listed in Table 2. Integrate Eq. (1) and apply the initial condition (t = 0 min, C = C0 ) to obtain

CE − C t = exp − CE − C0 R


The resistance is comprised of the liquid-phase and gas-phase resistances, i.e., R = RL + RG


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According to Fick’s Law [18] and mass transfer balance [15], the resistances of liquid-film and gas-film are inversely proportional to the diffusivity of volatile species in the gas-phase and liquid-phase, respectively, i.e., RL =

k1 DL


RG =

k2 DG


According to Stokes–Einstein equation [18], i.e., kB T D= 6r


the resistance in the gas-phase and liquid-phase can be expressed as RL =

k1 6r L kB T


and RG =


Ai = (n − 1)(1 − )AE + ϕ

k2 6r G , kB T


R = a + bL

or L =

R−a b


where a = G 6k2 r/kB T and b = 6k1 r/kB T, they are constant at the given conditions. 3.1.2. Determination of the mass transfer resistance of volatile species by MHE-GC In a MHE-GC mode, the measurement in a given sample vial is stepwise repeated with an interval time = t (between two adjacent measurements, i.e., i and i − 1). Thus, Eq. (2) can be expressed as

CE − Ci t = exp − CE − ϕCi−1 R


where ϕ is the ratio of two adjacent concentrations measured by MHE-GC, it is a constant at the given operational conditions [19]. For the given sample,


exp −




Thus, Eq. (8) can be written as Ci = (1 − )CE + ϕCi−1


or C2 = (1 − )CE + ϕC1


C3 = (1 − )CE + ϕC2





ϕ  1  Ai = (1 − )AE + Ai n−1 n−1 n







By plotting 1/n − 1 A vs. 1/n − 1 Ai and conduct2 i 1 ing the linear regression, we can obtain the slope (s = ϕ) and intercept (ε = (1 − )AE ) of the straight line. The GC signal at equilibrium can be calculated by ε 1 − s/ϕ


According to Eq. (9) and the slope of Eq. (13), the mass transfer resistance and the GC signal at equilibrium can be calculated by R=−

t t  =−  ln () ln s/ϕ


Therefore, the mass transfer resistance of volatile species can be measured by MHE-GC and thus the liquid viscosity can be calculated according to Eq. (7). 3.2. Selection of the tracer compound As the present method is based on measuring the content of volatile species (as a tracer) released to the headspace (vapor phase) in a closed sample vial in a MHE mode, it is very crucial to choose a suitable volatile compound in the experiment. Compared to volatile inorganic compounds, volatile organic compounds (VOCs) have advantages of the wide availability and soluble in various liquid medium, the latter minimize the amount of VOC emission to the headspace during the sample preparation and in each headspace extraction measurement. Moreover, the GC detector’s sensitivity for VOC measurement is much higher than that of the volatile inorganic species. Therefore, we can purposely mix a very small volume of a VOC tracer solution with the tested sample in the experiment. The VOC species with lower vapor–liquid partitioning constants, e.g., methanol or benzyl alcohol [20], are the good tracer used for aqueous samples in the present method. The amount of VOC loss or release to the headspace during the sample preparation can be neglected [21]. It should be pointed out that there are some VOCs originally remaining in the process liquors. If it is the case, we can select a suitable VOC species (as the tracer) from them, which makes the testing much easier. 3.3. Selection of sample size

.. . Cn = (1 − )CE + ϕCn−1


Add Eqs. (10-2) to (10-n) to have Ci = (n − 1)(1 − )CE + ϕ


To eliminate variable n in the intercept of Eq. (12), we can multiply 1/(n − 1) on the two sides of the equation. Thus, the Eq. (12) can be rewritten as

AE =

respectively. From Eqs. (3-1) to (6), the relationship between the mass transfer resistance of volatile species and the viscosity of the liquid medium can be written as


As the GC signal (peak area) for volatile species is linearly proportional to its concentration in the vapor phase (if the concentration is not too high), i.e., Ci = fAi , Eq. (11) can be expressed as





n−1  1



In general, a larger sample size is helpful in improving the detection sensitivity in HS-GC method [12]. However, the detection sensitivity is not a problem in the present method because a sufficient amount of VOC tracer in the tested sample could be doped if desired. The main consideration for the sample size used in the testing is the temperature issue, as the viscosity of liquid is temperature-dependent. Therefore, it is theoretically important to ensure that a desired temperature has been achieved at the first headspace measurement. As reported in our previous work [22,23],


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Fig. 1. Time-dependent release profiles of methanol from the tested black liquors measured by MHE-GC technique. Fig. 3. The linear relationships between

small size of sample can significantly reduce the time to reach the temperature equilibrium. However, if the sample size is too small, the surface area of the liquid exposure to the headspace in the vial will not be unified. Therefore, as a compromise, we used a 2 mL of sample in the testing. 3.4. Effect of sample matrix As observed in our previous work [24], there are some interactions between VOCs and sample matrices, e.g., the salting-out effect, which affect the vapor–liquid partitioning constants of VOCs. Therefore, it is impossible to obtain the same equilibrium concentration, i.e., CE in Eq. (2), in the different samples even if the amounts of VOC tracer doped are identical. Fig. 1 shows the time-dependent methanol (as the tracer) release profiles measured by MHE-GC at 110 ◦ C, a typically temperature in mill operation [6], in which the viscosities data in these tested liquors are listed in Table 1. As studied in the previous work [25,26], methanol is generated during alkaline pulping processes because of the de-methoxylation reaction on lignins and hemicelluloses. Due to the low volatility of methanol, its concentration in the concentrated black liquor after evaporation is still significant. Thus, we can use the original methanol in these black liquors as the VOC tracer in the present testing. It can be seen from Fig. 1 that the maximum signals at the equilibrium for these samples are different, which are caused by the effects of the methanol concentration in the liquor and sample matrix. However, the effects can be corrected by normalization treatment,


1/n − 1


Ai and


1/n − 1



in the tested black liquors.

Table 3 The results from linear regression using Eq. (13). Sample ID

ε (errora )

s (errora )


1 2 3 4

606(11) 584(5) 466(6) 179(2)

0.262(0.024) 0.288(0.011) 0.594(0.009) 0.736(0.010)

0.984 0.996 0.999 0.999


Error is expressed as the standard deviation at the confidence level of 95%.

as shown in Fig. 2. The normalized methanol release curves clearly indicate that the amount of methanol release is disproportional to the liquor viscosity at a given equilibration time. 3.5. Establishment of the correlation 3.5.1. Theoretical approach Fig. 1 and Eq. (13), we plot  According  n to the  data  in n−1 1/n − 1 A vs. 1/n − 1 Ai and conduct a linear regres2 i 1 sion to get a straight line, as shown in Fig. 3. The excellent results from these linear regressions (r2 > 0.980) further prove that Eq. (13), which was derived based on the theoretical assumptions, is justifiable. Table 3 shows the detail information from the linear regression on the data. According to the slope and intercept listed in Table 3 and Eqs. (14) and (15), we can calculate the mass transfer resistances and the GC peak areas at equilibrium (listed in Table 4). Using the data in Table 4 and Eq. (7), we established an equation for describing the relationship between the mass transfer resistance of methanol and the viscosity of the liquid medium, i.e., R = 7.89(±0.10) + 0.0639(±0.0012)L

r 2 = 0.999


Thus, the viscosity of the liquid can be obtained after the mass transfer resistance of methanol is determined by the present MHEGC method. Table 4 Calculated mass transfer resistances (R) and equilibrium signals (GC peak area).

Fig. 2. Normalized time-dependent release profiles of methanol from the tested black liquors based on the data shown in Fig. 1.

Sample ID

R (errora )

AE (errora )

1 2 3 4

7.76(0.56) 8.38(0.27) 21.3(0.7) 39.2(2.2)

837(46) 838(22) 1244(50) 795(51)


Error is expressed as the standard deviation at the confidence level of 95%.

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4. Conclusions This study demonstrated a novel method for the determination of the viscosity of concentrated black liquor using a MHE-GC technique. Both theoretical and empirical models were proposed for the present method correlation with the data measured by the reference method. The method is simple and practical and can be a valuable tool for the investigation of the viscosity of the black liquor for combustion. Acknowledgements

Fig. 4. Relationship between the GC signal ratios (A2 /A1 ) and the viscosities in the tested black liquors at 110 ◦ C.

Differ from the conventional standard curve of the analyzes, the intercept in Eq. (16) was the mass transfer resistance of gas-film that cannot be eliminated even when the viscosity of liquor and mass transfer resistance of liquid-film are close to zero. It should be excluded in the true correlation curve while calculating the limit of quantification (LOQ) of this method. Therefore, by assuming the intercept was zero (e = 0) in Eq. (17) [27], we obtained that the LOQ of this approach was 15.6 cP. LOQ =

  e + 10 × e k


3.5.2. Empirical approach The theoretical approach mentioned above requires a number of data points for the linear regression, which will also extend the time for each sample testing. Clearly, for two adjacent measurements in a given sample tested by MHE-GC, the effects of VOC concentration and sample matrix on the methanol release are the same. Therefore, the effects could be eliminated by using the GC signal ratio from two adjacent measurements. In the present work, we used the GC signals at 15 min (A2 ) and 5 min (A1 ) (see Fig. 1) to calculate the ratios (i.e., A2 /A1 ) for these sample, and then plot the ratios vs. the liquor viscosities, as shown in Fig. 4. Surprisingly, we found that there is an excellent correlation between the ratio and the algorism of the viscosity. Therefore, we can also apply this empirical method to determine the liquor viscosity based on two headspace extraction GC measurements. Obviously, the empirical method is much efficient than the above theoretical approach. Moreover, it can be noticed from Fig. 4 that the empirical method seems good to measure the viscosity at a lower range. The relative standard deviation in the ratio measurement is within 3.0%.

The authors acknowledge the Natural Science Foundation of China (No. 21037001) and Research Fund for the Doctoral Program of Higher Education of China (No. 20110172110026) for sponsoring the research. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

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