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Delignification Kinetics Models H Factor Model

Delignification Kinetics Models H Factor Model. Provides mills with the ability to handle common disturbance such as inconsistent digester heating and cooking time variation. 170. 900. 700. 130. Relative Reaction Rate. 500. H factor equal to area under this curve. Temperature °C. 300.

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Delignification Kinetics Models H Factor Model

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

  2. 170 900 700 130 Relative Reaction Rate 500 H factor equal to area under this curve Temperature °C 300 90 100 1 2 Hours from Start Delignification Kinetics ModelsH Factor/Temperature

  3. Delignification Kinetics ModelsH Factor Model k0 is such that H(1 hr, 373°K) = 1 Relative reaction rate

  4. Delignification Kinetics ModelsH Factor Model • Uses only bulk delignification kinetics k = Function of [HS-] and [OH-] R = T [=] °K

  5. Kraft Pulping KineticsH Factor/Temperature

  6. Kappa orYield 15% EA 15% EA 15% EA 18% EA 20% EA H-factor 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

  7. 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!

  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 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

  13. Delignification Kinetics ModelsUW model • Initial • dL/dt = k1L • E ≈ 9,500 cal/mole • Bulk • dL/dt = (k2[OH-] + k3[OH-]0.5[HS-]0.4)L • E ≈ 30,000 cal/mole • Residual • dL/dt = k4[OH-]0.7L • E ≈ 21,000 cal/mole

  14. Model PerformanceUW model Pulping data for thin chips – Gullichsen’s data

  15. Model PerformanceUW model Pulping data for mill chips - Gullichsen’s data

  16. Model PerformanceUW model Virkola data on mill chips

  17. Model Performance (Andersson)UW Model Model works well until very low lignin content

  18. Carbohydrate Loss Models Modeling yield prediction – A Very Difficult Modeling Problem

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

  20. UW: Model I Basic Structure: dc/dt=k*dL/dt Some physical justification for this is given by carbohydrate-lignin linkages. Carbohydrates lumped into a single group.

  21. 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.

  22. Model PerformanceUW model Virkola data on mill chips

  23. 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

  24. Prediction of pulp viscosity

  25. Gullichsen’s viscosity data

  26. Virkola’s viscosity data

  27. Virkola’s viscosity data

  28. [OH-] & [HS-] Predictions • Calculated by stoichiometry in all models as follows:

  29. Model PerformanceUW model Gullichsen data on mill chips

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