Kraft Pulping Modeling & Control 1 Control of Batch Kraft Digesters

Kraft Pulping Modeling & Control 2 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. 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 H-factor 15% EA 18% EA 20% EA

Kraft Pulping Modeling & Control 3 H-factor Control Vroom

Kraft Pulping Modeling & Control 4 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). Where H is H-factor, EA is effective alkali, K is kappa number, and a are model constants. 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 K=32 Necessary H-factor for obtaining K = 32 vs. EA concentration in liquor sample EA H

Kraft Pulping Modeling & Control 5 Effective Alkali Analyzer - Conductivity Titration Temperature and pressure sensors Effective Alkali Analyzer - Conductivity Titration Temperature and pressure sensors Kappa Batch Sensors

Kraft Pulping Modeling & Control 6 Kappa Batch Laboratory Tests Effective alkali – compared against titration End of cook kappa to check prediction Effective alkali – compared against titration End of cook kappa to check prediction

Kraft Pulping Modeling & Control 7 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 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

Kraft Pulping Modeling & Control 8 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. Operator Setpoint(s) »End of cook kappa number Manipulated Variables »Temperature profile »Cooking time Control Objective »Decrease standard deviation in final kappa target.

Kraft Pulping Modeling & Control 9 Kappa Batch Mill Results Lowered final kappa standard deviation.

Kraft Pulping Modeling & Control 10 Kappa Batch Control Benefits Bleached Pulp »Lower chemical usage and effluent loading in bleach plant Unbleached Pulp »Higher yield Bleached Pulp »Lower chemical usage and effluent loading in bleach plant Unbleached Pulp »Higher yield

Kraft Pulping Modeling & Control 11 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. Where H is H-factor, a 2 and b 2 are slope and intercept of lignin to EA relationship, a 3 and a 4 are constants (a 3 can incorporate sulfidity and chip properties). 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, a 2 and b 2 are slope and intercept of lignin to EA relationship, a 3 and a 4 are constants (a 3 can incorporate sulfidity and chip properties).

Kraft Pulping Modeling & Control 12 Batch Control Kerr

Kraft Pulping Modeling & Control 13 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) Control techniques use liquor measurements (CLA 2000) for control of final kappa number »EA – conductivity »Lignin – UV adsorption »Total dissolved solids – Refractive Index (RI)

Kraft Pulping Modeling & Control 14 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 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

Kraft Pulping Modeling & Control 15 Inferential Control Model Results Using model final kappa variation reported to be reduced by 50%.

Kraft Pulping Modeling & Control 16 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). 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).

Kraft Pulping Modeling & Control 17 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. 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. Inferential Batch Control Sensors

Kraft Pulping Modeling & Control 18 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 Operator Setpoint(s) »End of cook kappa number Manipulated Variables »Midpoint temperature »Cooking time Control Objective »Decrease standard deviation in final kappa target

Kraft Pulping Modeling & Control 19 Inferential Batch Control Operations and Objectives Model based control adjusts both end time and temperature in optimal fashion. Temperature main manipulated variable Model based control adjusts both end time and temperature in optimal fashion. Temperature main manipulated variable

Kraft Pulping Modeling & Control 20 Inferential Batch Control Simulated Results Adaptive strategy performs better. Handles non- linearity between manipulated variables and end kappa more efficiently.