Delignification Kinetics Models H Factor Model

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Delignification Kinetics ModelsH Factor Model

• Provides mills with the ability to handle common
  disturbance such as inconsistent digester heating
  and cooking time variation.

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Delignification Kinetics ModelsH
Factor/Temperature
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Delignification Kinetics ModelsH Factor Model
Relative reaction rate

• k0 is such that H(1 hr, 373K) 1

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Delignification Kinetics ModelsH Factor Model

• Uses only bulk delignification kinetics

k Function of HS- and OH-
R
T K
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Kraft Pulping KineticsH Factor/Temperature
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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

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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!
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Delignification Kinetics ModelsKerr model 1970

• H factor to handle temperature
• 1st order in OH-
• Bulk delignification kinetics w/out HS-
  dependence

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Delignification Kinetics ModelsKerr model 1970

• Integrated form

H-Factor
Functional relationship between L and OH-
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Delignification Kinetics ModelsKerr model 1970
Slopes of lines are not a function of EA charge
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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

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Delignification Kinetics ModelsUW 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
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Delignification Kinetics ModelsUW 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

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Model PerformanceUW model
Pulping data for thin chips Gullichsens data
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Model PerformanceUW model
Pulping data for mill chips - Gullichsens data
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Model PerformanceUW model
Virkola data on mill chips
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Model Performance (Andersson)UW Model
Model works well until very low lignin content
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Carbohydrate Loss Models

• Modeling yield prediction A Very Difficult
  Modeling Problem

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UW Model

• Two methods have been tested, but since both have
  the same accuracy, the simplest has been retained.

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UW Model I
Basic Structure dc/dtkdL/dt
Some physical justification for this is given by
carbohydrate-lignin linkages. Carbohydrates
lumped into a single group.
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Gustafson Model I

• Carbohydrate/lignin relation is assumed to be
  stable and not a strong function of pulping
  conditions.
• Selectivity of reactions assumed to be slightly
  dependent on OH- but independent of temperature.
• Yield/kappa relationship can be improved by using
  both lower pulping temperature and less alkali.

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Model PerformanceUW model
Virkola data on mill chips
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Prediction of pulp viscosity

• Dependence of viscosity on pulping conditions was
  modeled
• Viscosity is a measure of degradation of
  cellulose chains
• Effect of temperature, alkalinity, initial DP,
  and time on viscosity is modeled
• Model is compared with experimental data from two
  sources

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Prediction of pulp viscosity
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Gullichsens viscosity data
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Virkolas viscosity data
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Virkolas viscosity data
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OH- HS- Predictions

• Calculated by stoichiometry in all models as
  follows

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Model PerformanceUW model
Gullichsen data on mill chips