CAUSES OF POOR DREGS SETTLING IN A GREEN LIQUOR CLARIFIER Torsten Meyer1, Heather Munn2, and Honghi Tran1

1University of Toronto, Toronto, Ontario, CANADA 2Irving Pulp and Paper Ltd., Saint John, NB, CANADA ABSTRACT A study was conducted to examine the most likely parameters responsible for poor dregs settling at a Canadian mill over a 2.5-year period, using a multivariate data analysis (MVDA) program. Also, compositions of dregs, black liquor and lime mud were used to identify correlations with impaired dregs settling. The results show that poor dregs settling strongly correlates with high silicone (Si) content in dregs and moderately correlates with high iron (Fe) and high aluminum (Al) contents, and with low bulk density in dregs. The dregs settling behavior seems to be seasonally influenced, implying that wood quality variation can be a factor. The results from the MVDA show that poor dregs settling is correlated to incomplete combustion and/or low load conditions in the recovery boiler, and low sulfidity in the causticizing plant. For mills that experience dregs settling or green liquor filtering issues, regular compositional analyses of dregs, green liquor, weak wash, and black liquor are recommended in order to monitor the dynamics of silicon and other constituents in the recovery cycle. INTRODUCTION Poor dregs settling in green liquor clarifiers is a persistent problem in many kraft pulp mills. It can lead to increased requirements of make-up lime, higher non-process element (NPE) levels in the lime mud, impaired liquor and lime mud filtering, and higher dust formation in the lime kiln. While the root causes of poor settling are not known, a variety of possible reasons have been proposed. Taylor [1] attributed the problem to the presence of an inorganic gelatinous material in the dregs with a high silicon-to-aluminum molar ratio. During normal settling this ratio was about 2:1, whereas during an episode of deteriorated settling, it increased to 5:1. The gel appeared to be an amorphous magnesium silicate with a density only slightly higher than that of the green liquor. Also, during normal settling periods, the dregs contained more calcite (presumably lime mud) and thus had a higher density which helped improve settling. In a study on black liquor gasification, poor dregs settling was reported to be caused by decreased air-to-fuel ratio in the recovery boiler, leading to an increase in char content and the associated decrease in dregs density [2]. In another study, the presence of small magnesium hydroxide particles has been reported to cause poor dregs settling [3]. Lidén [4] suggested that smelt – water interactions affect the settling behavior of the dregs in the clarifier. Also, mills that apply

magnesium sulfate during oxygen delignification may experience temporarily elevated magnesium levels in the black liquor, and as a result, deteriorated dregs settling [4]. This study is the first attempt to investigate the root causes of poor dregs settling using multivariate data analysis methods. The objective was to identify the most likely operating parameters responsible for episodes of poor settling in a green liquor clarifier at the Irving Pulp and Paper (IPP) mill in Saint John, NB, Canada. The paper discusses the experience at IPP, the methodology used in this work, the key results and implications for mill operation. EXPERIENCE AT IPP The recovery cycle at IPP consists of an evaporation plant with six rising-film effects and a high-solids concentrator (HSC), a recovery boiler fueled in average with 4.1 million lb/day of as-fired black liquor dry solids (BL d.s.), and the causticizing plant. Tall oil is not separated, but incinerated along with the black liquor. The raw green liquor flows from the dissolving tank to the surge tank, and then to the green liquor clarifier. The underflow of the clarifier, containing the settled dregs, is pumped to the dregs centrifuge for pre-dewatering. Afterwards, the dregs are washed and dewatered on a pre-coat filter. The most significant adverse effects of poor dregs settling are enhanced growth of lime kiln rings, and deterioration of the filtering, i.e. the pre-coat builds up faster and has to be replaced more often. METHODOLOGY Mill Process Data Daily averaged data of 200+ operating parameters over a 2.5-year period (Apr 2016 – Aug 2018) were collected from the evaporation plant, the recovery boiler and the causticizing plant at IPP. Compositions of dregs, lime mud and black liquor were used to identify correlations to poor dregs settling. The mill is using softwood, mixed hardwood and maple for pulping. The pulping cycle usually involves 14 days of softwood pulping, followed by 5 days of mixed hardwood pulping, and 9 days of maple pulping. Numerous recovery boiler parameters such as air flow rates and air pressures operate at different levels depending on the type of wood used for pulping. This makes it difficult to identify correlations between those parameters and the dregs settling behavior. Therefore, additional sub-parameters were created that only contain data related to pulping of one specific type of wood, i.e. these parameters contain either data generated during softwood pulping, mixed hardwood pulping, or maple pulping. Dregs Settling Parameter The settling performance of the dregs in the clarifier was measured on a daily basis by means of the Settleable Solids Test using an Imhoff cone [5]. The test involves measuring the volume of solids in one litre of sample that settles to the bottom of the Imhoff cone during a specified time period. The samples were taken at the green liquor clarifier exit. If the volume of settled dregs after the clarifier is small, the settling performance is good. A high volume indicates poor settling. Multivariate Data Analysis

The multivariate data analysis (MVDA) program SIMCA version 15 from MKS Umetrics, Umeå, Sweden was used to perform an orthogonal partial least squares (OPLS) regression analysis. This software has been applied and described in detail in previous studies where operating data related to the recovery cycle in pulp mills were investigated [6-8]. MVDAs have been increasingly used to examine large datasets, identify patterns and correlations while considering all data simultaneously. During the analysis an empirical model is created based on measured process data (independent variables X), and by calculating latent variables that are orthogonal to each other. In the case of OPLS regression, these latent variables are selected to maximize their description of the output (dependent variables Y) variance. The Y-parameter is the target parameter, which in the case of this study is the volume of settled dregs after the clarifier. The X-parameters are the plant operating parameters that may or may not correlate with the target parameters. OPLS regression analysis divides the systematic variation of the X-variables into two parts, one which is correlated to Y, and another part that is not correlated (orthogonal) to Y. In order to quantify and rank the relationships between the operating parameters (X-parameters) and the target parameters (Y-parameters), the so-called predictive component (p) of the variance in X was used, which is part of the software output. The higher the absolute value of (p), the stronger is the relationship between X and Y. In complex systems, the predictive component (p) is often a more suitable parameter to investigate the importance of process parameters than the commonly applied regression coefficient. The regression coefficient can be biased by sources of variation that are not related to the target parameters. In order to confirm the results of the OPLS analysis, a SIMCA discriminant analysis was performed to identify the differences between periods of poor settling and good settling. For this two-class problem, each day of operation was designated either as a good settling day, or a poor settling day, based on the daily measured volume of settled dregs after the clarifier. For this analysis no target parameter was set, instead all parameters were set as X-parameters. The discriminant analysis identifies and ranks the process parameters with the highest discriminatory power, i.e. those that distinguish the days of good settling from the days of poor settling to the largest extent. Dregs Composition Analysis Samples of dregs, lime mud, and grits were analyzed on a monthly basis, and samples of green liquor and black liquor were analyzed on an occasional basis in terms of 30+ elements, bulk density, total carbon, ash and moisture content, chloride, pH, conductivity, percentage loss after combustion at 550°C, and particle size distribution. In the case of black liquor, lignin and tall oil contents were also analyzed. These dregs composition parameters were individually correlated to the volume of settled dregs after the clarifier, as measured with the Settleable Solids Test. Sample extraction and analysis were carried out by the RPC Laboratory, New Brunswick, Canada. Samples were air dried, pulverized and a portion of each was digested according to the EPA Method 3050B. The resulting solutions were analyzed for trace elements using inductively coupled plasma – mass spectrometry (ICP-MS) methods. Silicon was determined by ICP-MS after sodium peroxide fusion of the sample. Total carbon and sulfur were determined applying combustion/infrared analysis.

RESULTS AND DISCUSSION Figure 1 shows the dregs settling behavior in the clarifier over a 4.5-year period. The plot shows periods of good settling behavior (low volume of settled dregs) that lasted several months, as well as time periods of poor settling (large volume of settled dregs) that lasted several weeks or months. Figure 1. Dregs settling performance. High values mean poor settling. Composition Analysis of Dregs, Lime Mud, and Black Liquor During periods of poor dregs settling, the composition of dregs changes significantly (Table 1). The dregs composition and correlation analysis revealed a strong and significant correlation between poor dregs settling and the silicon content in dregs (Figure 2). Poor settling is also moderately correlated to high aluminum content, high iron content, and low bulk density in dregs (Table 2). Furthermore, poor dregs settling is correlated to high aluminum (r=0.59) and iron contents (r=0.58) as well as weakly correlated to high silicon content (r=0.39) in the lime mud. The Si/Al ratios in the dregs in this study are very similar to those reported in Taylor (2013), and range between 1.2:1 and 4.6:1, however there is only a moderate correlation between the Si/Al ratio and poor dregs settling (r = 0.43). Table 1 Average concentrations (out of 29 measurements) and standard deviations of the

main elements during periods of poor vs. good dregs settling [g/kg], and the

 

average Mg/Al and Si/Al ratios. Poor settling was defined by a volume of settled dregs after the clarifier of ≥5 ml, as measured with the Settleable Solids Test.

Settling behavior

Si Al Fe Ca Mg Mn P Na K Mg/Al ratio

Si/Al ratio

Poorly 10.7 (±3.2)

3.7 (±0.8)

3.1 (±0.9)

220 (±45)

37 (±8)

17 (±4)

1.7 (±0.4)

36 (±23)

4.2 (±1.9)

10.2 (±1.7)

3.0 (±0.8)

Good 5.5 (±2.0)

2.7 (±1.0)

2.3 (±0.8)

240 (±41)

42 (±10)

19 (±5)

2.2 (±0.6)

31 (±23)

4.2 (±3.7)

17.1 (±4.6)

2.2 (±0.9)

Figure 2. Poor dregs settling versus silicone content in dregs (r=0.77, Conf.>99.9%). Table 2. Correlations between poor dregs settling, and the composition of the dregs.

Correlations in bold are significant at the confidence level >99% (that of bulk density and Si/Al ratio at >95%) (N = 29).

 

Si Al Fe Bulk density Mg/Al ratio Si/Al ratio

Correlation Values 0.77 0.47 0.58 -0.43 -0.55 0.43 Table 3 shows correlations between concentrations of Si, Al, Fe, lignin, and tall oil in black liquor on the one hand, and poor dregs settling (volume of settled dregs after the clarifier) on the other hand. Using the SIMCA software the parameters were lagged by 1 to 8 days, meaning that the correlations involve a time shift between the composition of black liquor and the impaired dregs settling. For example, there is a strong correlation (r=0.90) between the tall oil content in black liquor and poor dregs settling three days later. Apparently, prior to and during periods of poor dregs settling, concentrations of Si, Al, Fe and tall oil in black liquor are higher, and concentrations of lignin are lower. Partially soluble elements such as Si and Al can accumulate in the green liquor / weak wash cycle until sufficiently high concentrations lead to precipitation and purging along with the dregs and the lime mud [9]. Higher tall oil and lower lignin contents may refer to temporary changes in wood quality and/or cooking conditions. Table 3 Correlations between black liquor parameters and poor dregs settling, several

days later. Correlations in bold are significant at the confidence level of 95% (N = 11). Correlations related to time delays of 0 and 2 days were not deemed meaningful and therefore not included in Table 3. In these cases, the black liquor concentrations are correlated only to days of good settling, i.e. the assigned volumes of settled dregs (Figure 1) are either zero, or very small (

investigated years. On the other hand, during late summer/early fall (August to November), the settling is good for most of the time. This annual pattern implies that the dregs settling may be to some extent influenced by the wood species used for pulping or by operational changes at the mill as a result of wood variability. Increased use of silicon-based defoamer additives in the brownstock washing area is a good example. The Si content in wood can range over three orders of magnitude, depending on the type of the tree, the part of the tree, and the geological conditions [10]. Also, the content of resin-like compounds or pitch, affecting the tall oil content in black liquor, is seasonally influenced. These compounds can influence processes in the recovery cycle, such as black liquor combustion [8].

Figure 3. Monthly average volume of settled dregs after the clarifier. High values mean

numerous days within a month with deteriorated dregs settling. Multivariate Data Analysis The MVDAs were conducted to identify operating parameters that are related to the dregs settling behavior in the green liquor clarifier. Two analyses were implemented, one to identify

those parameters that have the strongest relationship to poor dregs settling (OPLS analysis) (Figure 5), and another analysis to identify the parameters that distinguish the days of good settling from the days of poor settling, without setting a target parameter (discriminant analysis) (Figure 6). There is a relatively large degree of consistency between the parameters in both figures, which confirms the parameters’ relationship to the dregs settling behavior. Poor dregs settling is related to low solids content of the dregs cake after the dregs filter, low solids concentration in the dregs cake after centrifuging, and a high amount of polymer added to the dregs centrifuge (Figures 5, 6). Low solids content in the dewatered dregs and increased application of dewatering polymer clearly suggests the difficulty in dewatering the dregs during periods of poor settling. Also, the volumes of settled dregs from samples taken before the clarifier and after the clarifier are correlated. Obviously, if the green liquor entering the clarifier has a high dregs content, a somewhat larger portion of dregs will also exit the clarifier. The sulfidity in the causticizing system is lower during periods of poor dregs settling. The total titratable alkali (TTA) in the green liquor and the slaker temperature are used to adjust the amount of lime added to the slaker. For the same TTA, a lower sulfidity would mean there is a higher sodium carbonate content in the green liquor which may lead to underliming conditions due to an underestimation of the amount of required lime. In this case, there may not be sufficient lime in the weak wash in order to maintain good dregs settling. Poor dregs settling is also correlated to increased steam consumption in the high-solids concentrator (HSC), as well as high black liquor temperature and vapor pressure in the first and second effects within in the evaporation plant. However, it is not completely clear how these correlations can be explained. Increased steam requirement, and increased temperature and vapor pressure implies the difficulty in attaining a sufficiently high black liquor solids content, due to either increased boiling point rise and/or a low organic/inorganic ratio. Also, high tall oil content in the black liquor (Table 3) during times of poor settling can lead to foaming, reduced heat transfer and hence lowering the evaporation efficiency. The high alkali-to-wood charge during poor settling periods, as indicated in Figures 5 and 6, can lead to a boiling point rise and the associated increase in steam requirement. It is worthwhile mentioning that these correlations are not necessarily cause-effect relationships, i.e. the target parameter and the correlated process parameters may only be associated because of their dependency on other, unidentified processes. The results also show that poor dregs settling is correlated with the operation of a low primary air pressure and low steam production in the recovery boiler, particularly during softwood pulping, as well as high excess oxygen in the stack. All of these imply that the boiler was operated at either a low firing load condition and/or under an incomplete combustion condition that results in an increased char content in dregs. No correlation was found between total carbon content in dregs and the dregs settling behavior in the clarifier. However, total carbon content not only includes the unburned carbon (char) but also the carbon from the carbonates. Also, the average total carbon content in this study was 19.0 (±3.7)%, whereas the unburned carbon content in dregs is in most cases not higher than 5 – 10% by weight [4,11]. Poor settling seems also somewhat related to higher flows to the dissolving tank and the green liquor clarifier, as well as associated lower temperatures in the dissolving tank [Figures 5, 6]. The associated increased flow through the clarifier can lead to an enhanced solids carryover, and as a result, a larger volume of settled dregs after the clarifier.

Figure 5 Correlations between poor dregs settling (volume of settled dregs after the dregs

clarifier) and various operating parameters (calculated by SIMCA as relative predictive components). Parameters at the right side of the middle axis of the column chart are positively correlated to poor settling, and parameters to the left are negatively correlated to poor dregs settling. Longer bars mean stronger correlations. SW means only data generated during softwood pulping were used.

 

Figure 6 Parameters with the highest discriminatory power between days of good settling

and days of poor settling. Parameters at the right side of the middle axis of the column chart have high values during days of poor settling, and parameters to the left have low values. Longer bars mean that the parameters distinguish between good and poor settling more strongly. SW means only data generated during softwood pulping were used.

Silicon Sources within the Mill Si is derived mostly from wood, make-up lime and the silicon-based defoamer used in brownstock washers. Concentrations of silicon were measured in three samples of wood chips from the mill, and are on average 300 (±105) mg/kg (dry). The amount of Si entering the mill process via wood was estimated based on available data about the amount of wood pulped on a

 

monthly basis. Approximately 17 tons of silicon per month are introduced into the pulping process via wood chips. Using compositional data and the amounts of the make-up lime delivered to the mill, the estimated amount of silicon entering the recovery cycle via make-up lime is 3 tons per month. Finally, assuming a silicon content of 14% in the applied polydimethylsiloxane defoamer (personal communication with supplier), and by using data about the defoamer application during brownstock washing, the estimated amount of silicon entering the mill via defoamer is 1.5 tons per month. Therefore, by far the largest contribution of silicon introduced into the mill that may affect the dregs settling, originates from the pulp wood. However, the silicon from the make-up lime is directly introduced into the recovery cycle, whereas a major fraction of the silicon from the wood chips is bypassing the recovery cycle while ending up either in the pulp, or in the wastewater treatment system [9]. Therefore, besides the wood chips, the make-up lime is likely also a significant source of silicon that can affect the processes in the recovery cycle. CONCLUSIONS A multivariate data analysis and a composition analysis of dregs were conducted to identify the parameters responsible for impaired settling in the green liquor clarifier. The root causes of the poor settling are not completely clear. However, there is evidence to support several possible reasons that, isolated or in combination, can lead to the described dregs settling issues. The settling of dregs in the clarifier is closely associated with high silicon content in dregs, and the results indicate that low-density silicon compounds in the dregs contribute to the poor settling. Also, the settling behavior is to some extent seasonally influenced which may refer to the wood chip quality as a possible influencing factor. The settling behavior is also related to the operating conditions in the evaporator, the recovery boiler and the causticizing plant. Poor settling is correlated to the low primary air pressure and low steam production, particularly during softwood pulping, and high excess oxygen in the stack, all of which hints at incomplete combustion, and/or temporary low load conditions in the boiler. Based on this study, it is recommended that mills that experience dregs settling problems, should analyze the composition of dregs, black liquor, green liquor and weak wash on a regular basis in order to monitor the dynamics of silicon, and other compounds in the causticizing system. This will help identifying the causes of poor dregs settling and dregs carryover in the green liquor clarifier.

References (1) Taylor, K. (2013) Detailed characterization of poor settling green liquor dregs. Tappi Journal

12(9), 29-36. (2) U.S. DOE (2008) Advancement of high temperature black liquor gasification technology. U.S.

Department of Energy, Final Technical Report, P.I. Brown, C., Contributing authors: Landälv, I., Stare, R., Yuan, J., DeMartini, N., Ashgriz, N., Submitted by Weyerhaeuser Company.

(3) Empie, H.J., Ellis, M., Amundsen, M. (1999) Fundamentals of dregs removal. Project F01707, final report. Institute of Paper Sciene and Technology, Atlanta, Georgia.EPA, U.S.

Environmental Protection Agency (1971) Laboratory procedures – analysis for wastewater treatment plant operators. EPA, Water Programs – Region VII, Kansas City, MO, U.S.

(4) Lidén, J. (1995) Green liquor dregs: its origin, and effects in the lime cycle. Proceedings of the TAPPI 1995 International Chemical Recovery Conference, A291-A298, Atlanta, GA.

(5) EPA – Environmental Protection Agency (1971) Laboratory procedures: analysis for wastewater treatment plant operators. Author: David Vietti, Kansas City, MO.

(6) Versteeg, P. and Tran, H.N. (2009) Monitoring kraft recovery boiler fouling using principal component analysis. TAPPI Journal, November, p.22-28.

(7) Jones, A.K., Wagoner, J., Michaelson, T., and Tran, H.N. (2014) Use of multivariate analysis to understand the root causes of premature smelt spout failures on recovery boilers. International Chemical Recovery Conference, Tampere, Finland, June 9-12, 2014.

(8) Meyer, T., Goel, R., Chowdhury, A., Tran, H.N. (2018) Operating parameters affecting black liquor combustibility. J-FOR, 7(2), 53-60.

(9) Doldán, J, Poukka, O., Salmenoja, K., Battegazzore, M., Fernandez, V., Eluén, I. (2011) Evaluation of sources and routes of non-process elements in a modern eucalyptus kraft pulp mill. O Papel 72(7), 47-52.

(10) Cornelius, J.-T., Ranger, J., Iserentant, A., Delvaux, B. (2010) Tree species impact the terrestrial cycle of silicon through various uptakes. Biogeochemistry 97, 231-245.

(11) Thacker, W.E. (2007) Beneficial use of by-product solids from the kraft recovery cycle. National Council for Air and Stream Improvement (NCASI), Technical Bulletin No. 931, April 2007, Research Triangle Park, NC.  

 

Gateway to the Future

Causes of Poor Dregs Settling in a Green Liquor Clarifier

Torsten Meyer, Heather Munn, and Honghi TranOctober 29, 2019

Poor green liquor dregs settling

• Persistent problem in many kraft pulp mills• Poor dregs settling can lead to:

• increased make‐up lime requirements• higher NPE levels in lime mud• impaired liquor and lime mud filtering

• Causes of impaired dregs settling (and filtering) not well understood

2

Background

• Recurrent episodes of poor settling in the green liquor clarifier at Irving Pulp and Paper, NB, Canada

• Main adverse effects: • enhanced growth of lime kiln rings• deterioration of dregs filtering

• Investigating dregs settling as part of the work on mill waste management at the Pulp & Paper Centre of the University of Toronto

Methods

• Correlation analysis:• Monthly analysis of multiple elements in dregs

• Correlations with poor dregs settling

• Multivariate data analysis (MVDA):• 200+ operating parameters (Apr 2016 – Aug 2018)

• MVDA software SIMCA v.15

4

Target parameter for multivariate analysis

• Volume of settled dregs after the green liquor clarifier (Imhoff test)

• High values mean poor settling

5

Imhoff cones

Dregs settling behavior over the course of five years

6

Volume of se

ttled dregs [ml]

Date

Poor and good dregs settling

7

Volume of se

ttled dregs [ml]

Date

Period of poor settling

Period of good settling

0

12

24

36

48

60

0

4

8

12

16

20

Dregs settling & silicon content in dregsstrong, positive, and highly significant correlation (r=0.79, Conf.>99.9%)

8

Concentration in dregs [g

/kg]

Volume of se

ttled dregs [mL]

Dregs settling

Silicon

0

10

20

30

40

50

0

1

2

3

4

5

Dregs settling & iron content in dregs moderate, positive, and highly significant correlation (r=0.52, Conf.>99%)

9

Dregs settling

Iron

Volume of se

ttled dregs [mL]

Concentration in dregs [g

/kg]

0

10

20

30

40

50

60

0

1

2

3

4

5

10

Dregs settling & aluminum content in dregs moderate, positive, and significant correlation (r=0.47, Conf.>95%)

Dregs settling

Aluminum

Volume of se

ttled dregs [mL]

Concentration in dregs [g

/kg]

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

0

10

20

30

40

50

60

Dregs settling & bulk density of dregsmoderate, negative, and significant correlation (r = ‐0.43, Conf.>95%)

11

Density

 [g/cm

3 ]

Volume of se

ttled dregs [mL]

Dregs settling

Bulk density

Silicon & phosphorous concentrations in dregsstrong, negative, and highly significant correlation (r = ‐0.69, Conf. > 99.9%)

Silicon

 concentration [g/kg]

Phosph

orou

s con

centratio

n [g/kg]

Phosphorous

Silicon

0

1

2

3

4

5

Iron and aluminum concentrations in dregsvery strong, positive, and highly significant correlation (r = 0.91, Conf.>99.9%)

13

Concentration in dregs [g

/kg]

Aluminum

Iron

Cross correlation analysis

• Parameter correlations as function of time difference (time lag)

• For example, high silicon content in black liquor correlated to poor dregs settling, several days later

14

Cross correlation between black liquor composition & poor dregs settling, several days later 

Correlation coefficient

Parameter lag [days]

SiliconAluminumIron

Lignin

Tall oil

‐1.00

‐0.60

‐0.20

0.20

0.60

1.00

1 2 3 4 5 6 7 8

Silicon Aluminium

Iron

Tall Oil

Lignin

Positive correlation to poor settling

Negative correlation to poor settling

Multivariate Data Analysis

• Partial least square regression analysis with target parameter (volume of settled dregs after clarifier)

• Discriminant analysis without target parameter (identifying differences between days of good settling vs. days of poor settling)

16

Multivariate data analysis: parameters related to poor dregs settling

17

Prim

ary Air P

ressure (SW)

Rec Bo

iler D

rum Steam

 Flow (SW)

Caust W

L Sulfidity

Dregs F

ilter Cake Solids C

ontent

Dregs C

entrifu

ge Cake TSS

Tertiary Air Temp

Rec Bo

iler D

rum Steam

 Flow

Dissolving

 Tank Temp

Econ

omizer Gas Outlet T

emp

Prim

ary Air P

ressure

Steam to

 Eff 1 Temp

Eff 1

C BL

 Out Tem

p

Eff 2

 Droplet Tem

p

Eff 1

C BL

  In Tem

p

Eff 1

C Va

por P

ressure

50% Flash Tan

k BL

 Out Tem

p

Weak Wash Flow

 to Diss. Tank

Eff 2

 BL In Temp

EA to

 WBL Tank per V

olum

e Woo

d

Eff 3

 Vapor Pressure

Excess O2 in Stack

Total Steam

 to HSC

Vol. of Settle

d Dregs b

efore Clarif.

Centrifuge Po

lymer‐to

‐Dregs Ratio

Positive correlation

Negative correlation

Multivariate data analysis: parameters that distinguish days of good settling from days of poor settling

Dregs F

ilter Cake Solids C

ontent

Dregs C

entrifu

ge Cake TSS

Prim

ary Air P

ressure (SW)

Rec B

oiler D

rum Steam

 Flow (SW)

Caust W

L Sulfid

ity

Supe

rheater P

ressure Diff.

Air to Fuel Ratio

Rec B

oiler D

rum Steam

 Flow

Prim

ary Air P

ressure

Dissolving

 Tank Temp

Tertiary Air Temp

Weak W

ash Flow

 to Diss. Tank

Evaporator BL R

EA

Steam to

 Eff 1 Temp

GL Flow from

 Surge Tank

 to Clarifier

GL Clarifier T

orqu

e

GL Flow to

 Clarifier

Total Flow to

 Diss

olving

 Tank

EA to

 WBL Tank per V

olum

e Woo

d

Excess O2 in Stack

Total Steam

 to HSC

Vol. of Settle

d Dregs b

efore Clarif.

Centrifuge Po

lymer‐to

‐Dregs Ratio

Vol. of Settle

d Dregs a

fter Clarif.

High values during poor settling periods

Low values during poor settling

Evaporation plant

Poor settling related to:

• Increased steam consumption in the high solids concentrator

• Higher black liquor temperature and vapor pressure in the 1stand 2nd effects

• Higher alkali‐to‐wood charge at weak black liquor tank

Recovery boiler

Poor settling related to:

• Lower primary air pressure

• Lower steam production

• Higher excess oxygen in the stack 

Causticizing plant

Poor settling related to:• Low solids content of dregs cake after dewatering• Higher polymer addition to dregs centrifuge• Lower sulfidity in causticizing plant• Larger flow to the dissolving tank and the green liquor clarifier• Lower temperature in the dissolving tank

Seasonal influence on dregs settling: monthly average volume of settled dregs 

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Volume of se

ttled dregs [ml]

Month

0

5

10

15

20

25

30

35

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2014 2015 2016 2017 2018

Seasonal dregs settling behavior

• Poor settling in late spring / early summer

• Good settling in fall

• Silicon content in wood varies over three orders of magnitude

• Dregs settling may be influenced by the pulp wood, or by operational changes as a result of wood variability 

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Main silicon sources & daily input

24

Wood Chips 570 kg

Make‐up Lime 100 kg

Defoamer 50 kg

Summary

• The poor settling has likely several contributing causes• Poorly settling dregs clearly associated with high silicon content (possibly gel‐like Si compounds)

• Seasonal pattern of dregs settling issues may be related to the varying wood quality

• Poor settling associated with incomplete combustion and/or low load in recovery boiler, and difficulties to evaporate the liquor

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Acknowledgements