Control of Batch Kraft Digesters

1. Control of Batch Kraft Digesters

2. H-factor ControlVroom • Manipulate time and/or temperature to reach desired kappa endpoint. • Works well if there are no variations in raw materials or chemicals. Kappa orYield 15% EA 18% EA 20% EA H-factor

3. H-factor ControlVroom

4. H 2000 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 Kappa Batch ControlNoreus 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). • Where H is H-factor, EA is effective alkali, K is kappa number, and a are model constants.

5. Kappa BatchSensors • Effective Alkali Analyzer - Conductivity Titration • Temperature and pressure sensors

6. Kappa BatchLaboratory Tests • Effective alkali – compared against titration • End of cook kappa to check prediction

7. Kappa BatchDisturbances/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

8. Kappa BatchOperations 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.

9. Kappa BatchMill Results • Lowered final kappa standard deviation.

10. Kappa BatchControl Benefits • Bleached Pulp • Lower chemical usage and effluent loading in bleach plant • Unbleached Pulp • Higher yield

11. Batch ControlKerr • 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. • 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 properties).

12. Batch ControlKerr

13. Inferential ControlSutinen 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)

14. Inferential ControlSutinen 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

15. Inferential ControlModel Results • Using model final kappa variation reported to be reduced by 50%.

16. Inferential ControlKrishnagopalan 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).

17. Inferential Batch ControlSensors • 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.

18. Inferential Batch ControlOperations 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

19. Inferential Batch ControlOperations and Objectives • Model based control adjusts both end time and temperature in optimal fashion. • Temperature main manipulated variable

20. Inferential Batch ControlSimulated Results • Adaptive strategy performs better. Handles non-linearity between manipulated variables and end kappa more efficiently.