kraft pulping modeling & control 1 control of batch kraft digesters

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Kraft PulpingModeling& Control

Control of Batch Kraft Digesters

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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.

• Manipulate time and/or temperature to reach desired kappa endpoint.

• Works well if there are no variations in raw materials or chemicals.

Kappa orYield

H-factor

15% EA18% EA

20% EA

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H Factor

Lig

nin

(%

of

Pu

lp)

150°C

160°C

170°C

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H Factor

Lig

nin

(%

of

Pu

lp)

150°C

160°C

170°C

H-factor ControlVroom

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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.

• 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.

jn

i

m

j

iij KEAa

H

0 0

1

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1500

1000

500

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K=32

Necessary H-factor for obtaining K = 32 vs. EA concentration in liquor sample

EA

H

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• Effective Alkali Analyzer - Conductivity Titration

• Temperature and pressure sensors

• Effective Alkali Analyzer - Conductivity Titration

• Temperature and pressure sensors

Kappa BatchSensors

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Kappa BatchLaboratory 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

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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

• 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

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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.


• Manipulated Variables» Temperature profile

» Cooking time

• Control Objective» Decrease standard deviation in final kappa target.

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Kappa BatchMill Results

• Lowered final kappa standard deviation.• Lowered final kappa standard deviation.

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Kappa BatchControl 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

0.00

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Kappa

Fre

qu

en

cy

Limit Limit

m = 22s = 2.0

m = 25s = 3.5

m = 60s = 2.0

m = 57s = 3.5

Bleached Pulp Unbleached Pulp

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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).

• 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).

43222 /

ln1

aHaabL

L

b

i

f

L

L

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Batch ControlKerr

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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)

• Control techniques use liquor measurements (CLA 2000) for control of final kappa number» EA – conductivity

» Lignin – UV adsorption

» Total dissolved solids – Refractive Index (RI)

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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

• 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

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Inferential ControlModel Results

• Using model final kappa variation reported to be reduced by 50%.

• Using model final kappa variation reported to be reduced by 50%.

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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).

• 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).

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• 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 ControlSensors

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Inferential Batch ControlOperations and Objectives


• Manipulated Variables» Midpoint temperature

» Cooking time

• Control Objective» Decrease standard deviation in final kappa target


• Manipulated Variables» Midpoint temperature

» Cooking time

• Control Objective» Decrease standard deviation in final kappa target

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Inferential Batch ControlOperations 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

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Tim e (m in)

Tem

p (

°C)

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Tim e (m in)

Tem

p (

°C)

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Inferential Batch ControlSimulated Results

• Adaptive strategy performs better. Handles non-linearity between manipulated variables and end kappa more efficiently.

• Adaptive strategy performs better. Handles non-linearity between manipulated variables and end kappa more efficiently.

kraft pulping modeling & control 1 control of batch kraft digesters

Documents

kraft pulping modeling

kappa batch control

kappa batch control

ea slide

kappa batch operations

hfactor control vroom

final kappa target

liquor sample ea h slide