MODEL PREDICTIVE CONTROL OF FERMENTATION TEMPERATURE IN BIOFUEL PRODUCTION

• US 20080108048A1
• Filed: 10/30/2007
• Published: 05/08/2008
• Est. Priority Date: 10/31/2006
• Status: Active Grant

• Current Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Original Assignee: Rockwell Automation Technologies Incorporated (Allen-Bradley Co., Inc.)
• Inventors: Bartee, James, Macharia, Maina, Noll, Patrick, Tay, Michael
• Granted Patent: US 7,831,318 B2
• Time in Patent Office: Days
• US Class Current: 435/3
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

System and method for controlling temperature of a batch fermenter in a biofuel production process. A nonlinear predictive integrating temperature model for a batch fermentation process is provided that is a function of fermenter level. An objective for the batch fermentation process specifying a target fermenter temperature for the batch fermentation process is received, as is process information for the batch fermentation process, including fermenter level and fermenter temperature. The nonlinear predictive integrating temperature model is executed in accordance with the objective using the process information as input to determine target values for manipulated variables for controlling fermenter temperature of the batch fermentation process. The fermenter temperature for the batch fermentation process is controlled in accordance with the target values to produce biofuel in accordance with the objective, to substantially optimize the end of batch biofuel yield.

Claims

1. A method for controlling temperature of a batch fermenter in a biofuel production process, comprising:

providing a nonlinear predictive integrating temperature model for a batch fermentation process, wherein the nonlinear predictive integrating temperature model is a function of fermenter level;

receiving an objective for the batch fermentation process specifying a target fermenter temperature for the batch fermentation process;

receiving process information for the batch fermentation process, comprising fermenter level and fermenter temperature;

executing the nonlinear predictive integrating temperature model in accordance with the objective using the process information as input to determine target values for manipulated variables for controlling fermenter temperature of the batch fermentation process; and

controlling fermenter temperature for the batch fermentation process in accordance with the target values to produce biofuel in accordance with the objective, to substantially optimize the end of batch biofuel yield.

2. The method of claim 1, wherein the nonlinear predictive integrating temperature model is based on heat balance of the fermentation process, comprising a balance between available cooling and current fermentation heat generation.

3. The method of claim 1,

wherein the nonlinear predictive integrating temperature model models dependence of fermenter temperature on cooling water valve settings of a fermentation cooling exchanger for the fermenter, wherein the target values comprise a target cooling water valve setting for the fermentation cooling exchanger;

and

wherein said controlling fermenter temperature comprises adjusting the cooling water value in accordance with the target cooling water valve setting to substantially maintain target fermenter temperature for the fermentation batch process.

4. The method of claim 1,

wherein the nonlinear predictive integrating temperature model models dependence of fermenter temperature on fermentation cooling exchanger return temperature of a fermentation cooling exchanger for the fermenter, wherein the target values comprise a target fermentation cooling exchanger return temperature;

and

wherein said controlling fermenter temperature comprises;

the integrating nonlinear predictive integrating temperature model providing the target fermentation cooling exchanger return temperature to a cascaded controller of fermentation cooling exchanger return temperature, wherein the cascaded controller of fermentation cooling exchanger return temperature comprises a control model that models dependence of fermentation cooling exchanger return temperature on cooling water valve settings for the fermentation cooling exchanger;

the cascaded controller executing the control model in accordance with the target fermentation cooling exchanger return temperature to determine a target cooling water valve setting to achieve the target fermentation cooling exchanger return temperature; and

the cascaded controller adjusting the cooling water valve in accordance with the target cooling water valve setting to substantially maintain target fermenter temperature for the fermentation batch process.

5. The method of claim 1,

wherein the nonlinear predictive integrating temperature model further models a relationship between cooling water supply temperature and batch fermenter temperature, wherein the nonlinear predictive integrating temperature model is operable to predict the impact of cooling water supply temperature on fermenter temperature;

and

wherein the target values comprise a change in a cooling water valve setting to preemptively maintain fermentation batch temperature against disturbances in cooling water supply temperature.

6. The method of claim 1,

wherein the nonlinear predictive integrating temperature model models dependence of fermenter temperature on fermentation feed temperature, wherein the target values comprise a target fermentation feed temperature;

and

wherein said controlling fermenter temperature comprises controlling cooling of the fermentation feed in accordance with the target fermentation feed temperature to substantially maintain target fermenter temperature for the fermentation batch process.

7. The method of claim 1,

wherein the fermenter has a first level below which accurate fermenter temperature is not available, and above which accurate fermenter temperature is available;

wherein the nonlinear predictive integrating temperature model being a function of fermenter level comprises;

for fermenter levels below the first level,

the nonlinear predictive integrating temperature model modeling dependence of fermenter temperature on fermentation feed temperature, wherein the target values comprise a target fermenter feed temperature; and

wherein said controlling fermenter temperature comprises controlling the fermentation feed temperature in accordance with the target fermentation feed temperature to substantially maintain target fermenter temperature for the fermentation batch process; and

for fermenter levels above the first level;

the nonlinear predictive integrating temperature model modeling dependence of fermenter temperature on cooling water valve settings of a fermentation cooling exchanger for the fermenter, wherein the target values comprise a target cooling water valve setting for the fermentation cooling exchanger;

wherein said controlling fermenter temperature comprises adjusting the cooling water value in accordance with the target cooling water valve setting to substantially maintain target fermenter temperature for the fermentation batch process.

8. The method of claim 1,

wherein the fermenter has a first level below which accurate fermenter temperature is not available, and above which accurate fermenter temperature is available;

wherein the nonlinear predictive integrating temperature model being a function of fermenter level comprises;

for fermenter levels below the first level,

the nonlinear predictive integrating temperature model modeling dependence of fermenter temperature on fermentation feed temperature, wherein the target values comprise a target fermenter feed temperature;

wherein said controlling fermenter temperature comprises controlling the fermentation feed temperature in accordance with the target fermentation feed temperature to substantially maintain target fermenter temperature for the fermentation batch process; and

for fermenter levels above the first level;

the nonlinear predictive integrating temperature model modeling dependence of fermenter temperature on fermenter cooling exchanger return temperature, wherein the target values comprise a target fermentation cooling exchanger return temperature;

wherein said controlling fermenter temperature comprises

the integrating nonlinear predictive integrating temperature model providing the target fermentation cooling exchanger return temperature to a cascaded controller of fermentation cooling exchanger return temperature, wherein the cascaded controller of fermentation cooling exchanger return temperature comprises a control model that models dependence of fermentation cooling exchanger return temperature on cooling water valve settings of a fermentation cooling exchanger for the fermenter;

the cascaded controller executing the control model in accordance with the target fermentation cooling exchanger return temperature to determine a target cooling water valve setting to achieve the target fermentation cooling exchanger return temperature; and

the cascaded controller adjusting the cooling water valve in accordance with the target cooling water valve setting to substantially maintain target fermenter temperature for the fermentation batch process.

9. The method of claim 1, wherein the nonlinear predictive integrating temperature model further models metabolic activity of yeast in the batch fermentation process as a function of one or more of:

ethanol concentration;

sugar concentration;

enzyme concentration;

batch time;

or

integrated cooling quantity; and

wherein the target values are determined based at least in part on the modeled metabolic activity of yeast in the batch fermentation process.

10. The method of claim 1, further comprising:

repeating said receiving process information, said executing the nonlinear predictive integrating temperature model, and said controlling in an iterative manner to substantially optimize biofuel production over a fermentation batch.

11. The method of claim 1, wherein the target temperature comprises a target temperature trajectory for the fermentation batch.

12. The method of claim 1,

receiving constraint information specifying one or more constraints for the biofuel production process, wherein said executing the nonlinear predictive integrating temperature model comprises determining the target values subject to the one or more constraints.

20. The memory medium of claim 1,

wherein the fermenter has a first level below which accurate fermenter temperature is not available, and above which accurate fermenter temperature is available;

wherein the nonlinear predictive integrating temperature model being a function of fermenter level comprises;

for fermenter levels below the first level,

the nonlinear predictive integrating temperature model modeling dependence of fermenter temperature on fermentation feed temperature, wherein the target values comprise a target fermenter feed temperature;

wherein said controlling fermenter temperature comprises controlling the fermentation feed temperature in accordance with the target fermentation feed temperature to substantially maintain target fermenter temperature for the fermentation batch process; and

for fermenter levels above the first level;

the nonlinear predictive integrating temperature model modeling dependence of fermenter temperature on fermenter cooling exchanger return temperature, wherein the target values comprise a target fermentation cooling exchanger return temperature, and wherein the memory medium further stores a control model that models dependence of fermentation cooling exchanger return temperature on cooling water valve settings for a fermentation cooling exchanger for the fermenter;

wherein said controlling fermenter temperature comprises

the integrating nonlinear predictive integrating temperature model providing the target fermentation cooling exchanger return temperature to the control model;

executing the control model in accordance with the target fermentation cooling exchanger return temperature to determine a target cooling water valve setting to achieve the target fermentation cooling exchanger return temperature; and

adjusting the cooling water valve in accordance with the target cooling water valve setting to substantially maintain target fermenter temperature for the fermentation batch process.

13. A computer-accessible memory medium that stores program instructions for controlling temperature of a batch fermenter in a biofuel production process, wherein the memory medium stores:

a nonlinear predictive integrating temperature model for a batch fermentation process, wherein the nonlinear predictive integrating temperature model is a function of fermenter level; and

program instructions executable to perform;

receiving an objective for the batch fermentation process specifying a target fermenter temperature for the batch fermentation process;

receiving process information for the batch fermentation process, comprising fermenter level and fermenter temperature;

executing the nonlinear predictive integrating temperature model in accordance with the objective using the process information as input to determine target values for manipulated variables for controlling fermenter temperature of the batch fermentation process; and

controlling fermenter temperature for the batch fermentation process in accordance with the target values to produce biofuel in accordance with the objective, to substantially optimize the end of batch biofuel yield.

14. The memory medium of claim 13, wherein the nonlinear predictive integrating temperature model is based on heat balance of the fermentation process, comprising a balance between available cooling and current fermentation heat generation.

15. The memory medium of claim 13, wherein the nonlinear predictive integrating temperature model models dependence of fermenter temperature on cooling water valve settings of a fermentation cooling exchanger for the fermenter, wherein the target values comprise a target cooling water valve setting for the fermentation cooling exchanger;

and

wherein said controlling fermenter temperature comprises adjusting the cooling water value in accordance with the target cooling water valve setting to substantially maintain target fermenter temperature for the fermentation batch process.

16. The memory medium of claim 13,

wherein the nonlinear predictive integrating temperature model models dependence of fermenter temperature on fermentation cooling exchanger return temperature of a fermentation cooling exchanger for the fermenter, wherein the target values comprise a target fermentation cooling exchanger return temperature;

wherein the memory medium further stores a control model that models dependence of fermentation cooling exchanger return temperature on cooling water valve settings for the fermentation cooling exchanger;

wherein said controlling fermenter temperature comprises;

the integrating nonlinear predictive integrating temperature model providing the target fermentation cooling exchanger return temperature to the control model;

executing the control model in accordance with the target fermentation cooling exchanger return temperature to determine a target cooling water valve setting to achieve the target fermentation cooling exchanger return temperature; and

adjusting the cooling water valve in accordance with the target cooling water valve setting to substantially maintain target fermenter temperature for the fermentation batch process.

17. The memory medium of claim 13,

wherein the nonlinear predictive integrating temperature model further models a relationship between cooling water supply temperature and batch fermenter temperature, wherein the nonlinear predictive integrating temperature model is operable to predict the impact of cooling water supply temperature on fermenter temperature;

and

wherein the target values comprise a change in a cooling water valve setting to preemptively maintain fermentation batch temperature against disturbances in cooling water supply temperature.

18. The memory medium of claim 13,

wherein the nonlinear predictive integrating temperature model models dependence of fermenter temperature on fermentation feed temperature, wherein the target values comprise a target fermentation feed temperature;

and

wherein said controlling fermenter temperature comprises controlling cooling of the fermentation feed in accordance with the target fermentation feed temperature to substantially maintain target fermenter temperature for the fermentation batch process.

19. The memory medium of claim 13,

wherein the fermenter has a first level below which accurate fermenter temperature is not available, and above which accurate fermenter temperature is available;

wherein the nonlinear predictive integrating temperature model being a function of fermenter level comprises;

for fermenter levels below the first level,

the nonlinear predictive integrating temperature model modeling dependence of fermenter temperature on fermentation feed temperature, wherein the target values comprise a target fermenter feed temperature; and

wherein said controlling fermenter temperature comprises controlling the fermentation feed temperature in accordance with the target fermentation feed temperature to substantially maintain target fermenter temperature for the fermentation batch process; and

for fermenter levels above the first level;

the nonlinear predictive integrating temperature model modeling dependence of fermenter temperature on cooling water valve settings of a fermentation cooling exchanger for the fermenter, wherein the target values comprise a target cooling water valve setting for the fermentation cooling exchanger;

wherein said controlling fermenter temperature comprises adjusting the cooling water value in accordance with the target cooling water valve setting to substantially maintain target fermenter temperature for the fermentation batch process.

21. The memory medium of claim 13, wherein the nonlinear predictive integrating temperature model further models metabolic activity of yeast in the batch fermentation process as a function of one or more of:

ethanol concentration;

sugar concentration;

enzyme concentration;

batch time;

or

integrated cooling quantity; and

wherein the target values are determined based at least in part on the modeled metabolic activity of yeast in the batch fermentation process.

22. The memory medium of claim 13, further comprising:

repeating said receiving process information, said executing the nonlinear predictive integrating temperature model, and said controlling in an iterative manner to substantially optimize biofuel production over a fermentation batch.

23. The memory medium of claim 13, wherein the target temperature comprises a target temperature trajectory for the fermentation batch.

24. The memory medium of claim 13,

receiving constraint information specifying one or more constraints for the biofuel production process, wherein said executing the nonlinear predictive integrating temperature model comprises determining the target values subject to the one or more constraints.

25. A system for controlling temperature of a batch fermenter in a biofuel production process, comprising:

at least one processor; and

at least one memory medium, coupled to the at least one processor;

wherein the at least one memory medium stores;

a nonlinear predictive integrating temperature model for a batch fermentation process, wherein the nonlinear predictive integrating temperature model is a function of fermenter level; and

program instructions executable to;

receive an objective for the batch fermentation process specifying a target fermenter temperature for the batch fermentation process;

receive process information for the batch fermentation process, comprising fermenter level and fermenter temperature;

execute the nonlinear predictive integrating temperature model in accordance with the objective using the process information as input to determine target values for manipulated variables for controlling fermenter temperature of the batch fermentation process; and

control fermenter temperature for the batch fermentation process in accordance with the target values to produce biofuel in accordance with the objective, to substantially optimize the end of batch biofuel yield.