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Empirical Kraft Pulping Models

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Empirical Kraft Pulping Models. Models developed by regression of pulping study results ... Where Lf = floor lignin level set _at_ 0.5% on wood ... – PowerPoint PPT presentation

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Title: Empirical Kraft Pulping Models


1
Empirical Kraft Pulping Models
  • Models developed by regression of pulping study
    results
  • Excellent for digester operators to have for
    quick reference on relation between kappa and
    operating conditions
  • Hatton models are excellent examples of these

2
Emperical Kraft Pulping Models
Hatton Equation
Kappa (or yield) ?-?(log(H)EAn) ?,?, and n
are parameters that must be fit to the data.
Values of ?,?, and n for kappa prediction are
shown in the table below.
Warning These are empirical equations and apply
only over the specified kappa range.
Extrapolation out of this range is dangerous!
3
Delignification Kinetics ModelsH Factor Model
  • Uses only bulk delignification kinetics

k Function of HS- and OH-
R
T K
4
Delignification Kinetics ModelsH Factor Model
Relative reaction rate
  • k0 is such that H(1 hr, 373K) 1

5
Delignification Kinetics ModelsH Factor Model
  • Provides mills with the ability to handle common
    disturbance such as inconsistent digester heating
    and cooking time variation.

6
Delignification Kinetics ModelsH
Factor/Temperature
7
Kraft Pulping KineticsH Factor/Temperature
8
Delignification Kinetics ModelsKerr model 1970
  • H factor to handle temperature
  • 1st order in OH-
  • Bulk delignification kinetics w/out HS-
    dependence

9
Delignification Kinetics ModelsKerr model 1970
  • Integrated form

H-Factor
Functional relationship between L and OH-
10
Delignification Kinetics ModelsKerr model 1970
Slopes of lines are not a function of EA charge
11
Delignification Kinetics ModelsKerr model 1970
Model can handle effect of main disturbances on
pulping kinetics
  • Variations in temperature profile
  • Steam demand
  • Digester scheduling
  • Reaction exotherms
  • Variations in alkali concentration
  • White liquor variability
  • Differential consumption of alkali in initial
    delignification
  • Often caused by use of older, degraded chips
  • Good kinetic model for control

12
Delignification Kinetics ModelsGustafson model
  • Divide lignin into 3 phases, each with their own
    kinetics
  • 1 lignin, 3 kinetics
  • Transition from one kinetics to another at a
    given lignin content that is set by the user.

For softwood Initial to bulk 22.5 on
wood Bulk to residual 2.2 on wood
13
Delignification Kinetics ModelsGustafson model
  • Initial
  • dL/dt k1L
  • E 9,500 cal/mole
  • Bulk
  • dL/dt (k2OH- k3OH-0.5HS-0.4)L
  • E 30,000 cal/mole
  • Residual
  • dL/dt k4OH-0.7L
  • E 21,000 cal/mole

14
Delignification Kinetics ModelsGustafson model
  • Another model was formulated that was of the type
  • dL/dt K(L-Lf)
  • Where Lf floor lignin level set _at_ 0.5 on
    wood
  • Did not result in any better prediction of
    pulping behavior

15
Delignification Kinetics ModelsPurdue Model
  • 2 types of lignin
  • High reactivity
  • Low reactivity

Assumed to react simultaneously
Lf assumed to be zero
16
Delignification Kinetics ModelsPurdue Model
  • Potential difficulties
  • High reactivity lignin (initial lignin)
    dependent on OH- and HS-
  • No residual lignin kinetics

17
Delignification Kinetics ModelsAndersson, 2003
  • 3 types of lignin
  • Fast
  • Medium
  • slow

Assumed to react simultaneously, like Purdue model
1
10
total lignin
Lignin ow
0
10
L3 lignin
L1 lignin
L2 lignin
-1
10
0
50
100
150
200
250
300
time min
18
Delignification Kinetics ModelsAndersson, 2003
  • Fast 9 on wood (all t)
  • dL/dt k1HS-0.06L
  • E 12,000 cal/mole
  • Medium 15 on wood (t0)
  • dL/dt k2OH-0.48HS-0.39L
  • E 31,000 cal/mole
  • Slow 1.5 on wood (t0)
  • dL/dt k3OH-0.2L
  • E 31,000 cal/mole

19
Delignification Kinetics ModelsAndersson, 2003
Model also assumes that medium can become slow
lignin depending on the pulping conditions L
Lignin content where amount of medium lignin
equals the amount of slow lignin Complex
formula to estimate L
20
Delignification Kinetics ModelsAndersson, 2003
21
Model PerformanceGustafson model
Pulping data for thin chips Gullichsens data
22
Model PerformanceGustafson model
Pulping data for mill chips - Gullichsens data
23
Model PerformanceGustafson model
Virkola data on mill chips
24
Model Performance (Andersson)Purdue Model
Purdue model suffers from lack of residual
delignification
25
Model Performance (Andersson)Purdue Model
Purdue model suffers from lack of residual
delignification
26
Model Performance (Andersson)Gustafson Model
Model works well until very low lignin content
27
Model Performance (Andersson)Gustafson Model
Model handles one transition well and the other
poorly
28
Model Performance (Andersson)Andersson Model
Andersson predicts his own data well
29
Model Performance (Andersson)Andersson Model
Model handles transition well
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