Kraft Pulping
Modeling
& Control
Control of Batch Kraft Digesters
1
Kraft Pulping
Modeling
& Control
H-factor Control
Vroom
• Manipulate time and/or temperature to reach
desired kappa endpoint.
• Works well if there are no variations in raw
materials or chemicals.
Kappa or
Yield
15% EA
18% EA
20% EA
2
H-factor
Kraft Pulping
Modeling
& Control
H-factor Control
Vroom
Lignin (% of Pulp)
30
25
150°C
160°C
170°C
20
15
10
5
0
0
500
1000
1500
2000
2500
H Factor
3
Kraft Pulping
Modeling
& Control
Kappa Batch Control
Noreus et al.
• Control strategy uses empirical model that predicts
kappa number from effective alkali concentration of
liquor sample at beginning of bulk delignification
(~150 ºC).
H
2000
n m
1
  aij EAi K j
H i 0 j 0
K=32
1500
1000
500
EA
12 14 16 18 20 22 24
Necessary H-factor for obtaining K = 32 vs. EA
concentration in liquor sample
• Where H is H-factor, EA is effective alkali, K is
kappa number, and a are model constants.
4
Kraft Pulping
Modeling
& Control
Kappa Batch
Sensors
• Effective Alkali Analyzer - Conductivity Titration
• Temperature and pressure sensors
5
Kraft Pulping
Modeling
& Control
Kappa Batch
Laboratory Tests
• Effective alkali – compared against titration
• End of cook kappa to check prediction
6
Kraft Pulping
Modeling
& Control
Kappa Batch
Disturbances/Upsets
• Chip Supply
» Moisture content, size distribution, chemical content
• Pulping Liquor
» White liquor EA and sulfidity
» Black liquor EA and sulfidity
• Digester Temperature Profile
» Time to temperature and maximum temperature
7
Kraft Pulping
Modeling
& Control
Kappa Batch
Operations and Objectives
• Operator Setpoint(s)
» End of cook kappa number
• Manipulated Variables
» Temperature profile
» Cooking time
• Control Objective
» Decrease standard deviation in final kappa target.
8
Kraft Pulping
Modeling
& Control
Kappa Batch
Mill Results
• Lowered final kappa standard deviation.
9
Kraft Pulping
Modeling
& Control
Kappa Batch
Control Benefits
• Bleached Pulp
» Lower chemical usage and effluent loading in bleach plant
• Unbleached Pulp
0.25
» Higher yield
Bleached Pulp
Unbleached Pulp
m = 22
s = 2.0
m = 60
s = 2.0
Frequency
0.20
0.15
m = 25
s = 3.5
0.10
m = 57
s = 3.5
Limit
Limit
0.05
0.00
0
10
20
30
40
50
60
70
Kappa
10
80
Kraft Pulping
Modeling
& Control
Batch Control
Kerr
• Control strategy uses semi-empirical model that
predicts kappa number from effective alkali
concentration of liquor sample taken at two points
in the bulk delignification phase.
Li
1

L
 ln
  a3 H  a 4
 b2 L  b2 / a2  L f
• Where H is H-factor, a2 and b2 are slope and
intercept of lignin to EA relationship, a3 and a4 are
constants (a3 can incorporate sulfidity and chip
11
properties).
Kraft Pulping
Modeling
& Control
Batch Control
Kerr
12
Kraft Pulping
Modeling
& Control
Inferential Control
Sutinen et al.
• Control techniques use liquor measurements (CLA
2000) for control of final kappa number
» EA – conductivity
» Lignin – UV adsorption
» Total dissolved solids – Refractive Index (RI)
13
Kraft Pulping
Modeling
& Control
Inferential Control
Sutinen et al.
• Statistical model using Partial Least Squares
(PLS) to predict kappa number.
» Past batch information used to formulate current control
model.
• Control Strategies
» Use PLS model to manipulate cooking time or
temperature to achieve final kappa
14
Kraft Pulping
Modeling
& Control
Inferential Control
Model Results
• Using model final kappa variation reported to be
reduced by 50%.
15
Kraft Pulping
Modeling
& Control
Inferential Control
Krishnagopalan et al.
• Statistical model using Partial Least Squares
(PLS) to predict kappa number.
» Past batch information used to formulate current control
model.
• Control Strategies
» Direct – Use PLS model to manipulate input vector
» Indirect (adaptive) – Use PLS model to estimate
parameters of empirical model for control (e.g., Chari,
Vroom)
• Kinetic models developed for lignin,
carbohydrates, and viscosity can be used for
optimization (e.g., liquor profiling).
16
Kraft Pulping
Modeling
& Control
Inferential Batch Control
Sensors
• Continuous in-situ measurements of liquor EA (conductivity), lignin content
(UV), solids content (RI), and sulfide concentration (IC).
• Measurements are also done using near infrared.
17
Kraft Pulping
Modeling
& Control
Inferential Batch Control
Operations and Objectives
• Operator Setpoint(s)
» End of cook kappa number
• Manipulated Variables
» Midpoint temperature
» Cooking time
• Control Objective
» Decrease standard deviation in final kappa target
18
Kraft Pulping
Modeling
& Control
Inferential Batch Control
Operations and Objectives
• Model based control adjusts both end time and
temperature in optimal fashion.
• Temperature main manipulated variable
180
170
160
150
Temp (°C)
140
130
120
110
100
90
80
0
20
40
60
80
100
Tim e (m in)
120
140
160
180
200
19
Kraft Pulping
Modeling
& Control
Inferential Batch Control
Simulated Results
• Adaptive strategy
performs better.
Handles nonlinearity between
manipulated
variables and end
kappa more
efficiently.
20