Optimized air pollution control

• US 20060045800A1
• Filed: 08/27/2004
• Published: 03/02/2006
• Est. Priority Date: 08/27/2004
• Status: Active Grant

• Current Assignee: General Electric Technology GmbH (GE Aerospace)
• Original Assignee: Alstom Technology Ltd. (Alstom SA)
• Inventors: Piche, Stephen, Boyden, Scott A.
• Granted Patent: US 7,522,963 B2
• Time in Patent Office: Days
• US Class Current: 422/62
• CPC Class Codes: B01D 2257/302 Sulfur oxides
• CPC Class Codes: B01D 2257/404 Nitrogen oxides other than dinitrogen oxide
• CPC Class Codes: B01D 53/346 Controlling the process
• CPC Class Codes: B01D 53/8696 Controlling the catalytic process
• CPC Class Codes: G05B 13/024 in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

A controller directs operation of an air pollution control (APC) system performing a process to control emissions of a pollutant. The process has multiple process parameters (MPPs), one or more of the MPPs being a controllable process parameters (CTPPs) and one of the MPPs being an amount of the pollutant (AOP) emitted by the system. A user input device identifies an optimization objective. A control processor determines a set point for at least one of the one or more CTPPs based on the current values of the MPPs and the identified optimization objective, and directs control of one of the at least one CTPP based on the determined set point for that CTPP.

Claims

1. A controller for directing operation of an air pollution control (APC) system performing a process to control emissions of a pollutant, having multiple process parameters (MPPs), one or more of the MPPs being a controllable process parameters (CTPPs) and one of the MPPs being an amount of the pollutant (AOP) emitted by the system, comprising:

an input device configured to identify an optimization objective; and

a control processor configured with the logic to determine a set point for at least one of the one or more CTPPs based on the current values of the MPPs and the identified optimization objective, and to direct control of one of the at least one CTPP based on the determined set point for that CTPP.

2. The controller according to claim 1, wherein:

the identified optimization objective is to minimize the emitted AOP.

3. The controller according to claim 1, wherein:

the process has a defined AOP value (AOPV) representing an objective or limit on an actual value (AV) of the emitted AOP;

the control processor is configured with the further logic to determine the set point for the at least one CTPP based also on the AOPV; and

the identified optimization objective is to maintain the emitted AOP at a level below the AOPV.

4. The controller according to claim 1, wherein:

the performance of the process by the APC system results in a by-product being produced; and

the identified optimization objective is to control a quality of the produced by-product to a desired value, to maximize the quality of the produced by-product, or to minimize the quality of the produced by-product.

5. The controller according to claim 1, wherein:

the performance of the process by the APC system results in a by-product being produced;

one of the MPPs is a quality of the produced by-product (QPBP);

the process has a defined QPBP value (QPBPV) representing an objective or limit on an actual quality of the QPBP; and

the identified optimization objective is to maintain QPBP at a level either at, above or below the QPBPV.

6. The controller according to claim 1, wherein:

the performance of the process by the APC system results in a by-product being produced;

one of the MPPs is a quality of the produced by-product (QPBP);

the process also has a defined QPBP value (QPBPV) representing an objective or limit on an actual quality of the QPBP; and

the control processor is configured with the further logic to determine the set point for each of the at least one CTPP based also on the QPBPV.

7. The controller according to claim 1, wherein:

the identified optimization objective is to minimize a cost of the operation of the system.

8. The controller according to claim 1, further comprising:

one of a neural network process model and a non-neural network process model representing relationships between each of the CTPPs and the emitted AOP; and

wherein the control processor is configured with the further logic to determine the set point for each of the at least one CTPP based also on the one model.

9. The controller according to claim 8, wherein:

the one model includes one of a first principle model, a hybrid model, and a regression model.

10. The controller according to claim 1, wherein:

the system is a wet flue gas desulfurization (WFGD) system that receives SO₂ laden wet flue gas, applies limestone slurry to remove SO₂ from the received SO₂ laden wet flue gas and thereby control emissions of SO₂, and exhausts desulfurized flue gas;

the at least one CTPP includes one or more of a first parameter corresponding to a pH of the limestone slurry applied and a second parameter corresponding to a distribution of the limestone slurry applied;

the AOP is an amount of SO₂ in the exhausted desulfurized flue gas; and

the control processor determines the set point for one of the first and the second parameters based on (i) the current value of that parameter, (ii) the amount of SO₂ in the exhausted desulfurized flue gas and (iii) the identified optimization objective, and directs control of the one parameter based on the determined set point for that parameter to optimize the WFGD system operations for the identified optimization objective.

11. The controller according to claim 10, wherein:

the WFGD system also applies oxidation air to crystallize the SO₂ removed from the received SO₂ laden wet flue gas and thereby produce gypsum as a by-product of the removal of the SO₂ from the received SO₂ laden wet flue gas; and

the at least one CTPP includes one or more of the first parameter, the second parameter, and a third parameter corresponding to an amount of the oxidation air applied; and

the control processor also determines the set point for one of the first, the second and the third parameters based on (i) the current value of that parameter, (ii) the amount of SO₂ in the exhausted desulfurized flue gas, and (iii) the identified optimization objective, and also directs control of the one parameter based on the determined set point for that parameter to optimize the WFGD system operations for the identified optimization objective.

12. The controller according to claim 1, wherein:

the system is a selective catalytic reduction (SCR) system that receives NO_x laden flue gas, applies ammonia and dilution air to remove NO_x from the received NO_x laden flue gas and thereby control emissions of NO_x, and exhausts reduced NO_x flue gas;

the at least one CTPP includes a parameter corresponding to an amount of the ammonia applied;

the AOP is an amount of NO_x in the exhausted flue gas; and

the control processor determines the set point for the parameter based on (i) the current value of the parameter, (ii) the amount of NO_x in the exhausted flue gas and (ii) the identified optimization objective, and directs control of the parameter based on the determined set point to optimize the SCR system operations for the identified optimization objective.

13. The controller according to claim 12, wherein:

the MPPs include an amount of ammonia in the exhausted flue gas; and

the control processor determines the set point for the parameter based also on a current value of the amount of ammonia in the exhausted flue gas values.

14. A method for performing an air pollution control (APC) process to control emissions of a pollutant, having multiple process parameters (MPPs), one or more of the MPPs being a controllable process parameters (CTPPs) and one of the MPPs being an amount of the pollutant (AOP) emitted by the system, comprising:

identifying an optimization objective;

determining a set point for at least one of the one or more CTPPs based on the current values of the MPPs and the identified optimization objective; and

directing control of at least one of the at least one CTPP based on the determined set point for that CTPP.

15. The method according to claim 14, wherein:

the process has a defined AOP value (AOPV) representing an objective or limit on an actual value (AV) of the emitted AOP;

the set point for each CTPP is determined based also on the AOPV; and

the identified optimization objective is to maintain the emitted AOP at a level below the AOPV.

16. The method according to claim 14, wherein:

the performance of the process results in a by-product being produced; and

the identified optimization objective is to control a quality of the produced by-product to a desired value, to maximize the quality of the produced by-product, or to minimize the quality of the produced by-product.

17. The method according to claim 14, wherein:

the performance of the process results in a by-product being produced;

one of the MPPs is a quality of the produced by-product (QPBP);

the process has a defined QPBP value (QPBPV) representing an objective or limit on an actual quality of the QPBP; and

the identified optimization objective is to maintain QPBP at a level either above or below the QPBPV.

18. The method according to claim 14, wherein:

the performance of the process results in a by-product being produced;

one of the MPPs is a quality of the produced by-product (QPBP);

the process also has a defined QPBP value (QPBPV) representing an objective or limit on an actual quality of the QPBP; and

the control processor is configured with the further logic to determine the set point for each CTPP based also on the QPBPV.

19. The method according to claim 14, wherein:

the identified optimization objective is to minimize a cost of the performance of the process.

20. The method according to claim 14, wherein:

The set point for the at least one CTPP is determined based also on one of a neural network process model and a non-neural network process

model representing relationships between each of the CTPPs and the emitted AOP.

21. The method according to claim 20, wherein:

the one model includes one of a first principle model, a hybrid model, and a regression model.

22. The method according to claim 14, wherein:

the process is a wet flue gas desulfurization process that applies limestone slurry to remove SO₂ from SO₂ laden wet flue gas and thereby control emissions of SO₂, and to exhaust desulfurized flue gas;

the at least one CTPP includes one or more of a first parameter corresponding to a pH of the limestone slurry applied and a second parameter corresponding to a distribution of the limestone slurry applied;

the AOP is an amount of SO₂ in the exhausted desulfurized flue gas;

the set point for one of the first and the second parameters is determined based on (i) the current value of that parameter, and the amount of SO₂ in the exhausted desulfurized flue gas and (ii) the identified optimization objective; and

the control of the one parameter is directed based on the determined set point for that parameter to optimize the WFGD process for the identified optimization objective.

23. The method according to claim 22, wherein:

the WFGD process also applies oxidation air to crystallize the SO₂ removed from the SO₂ laden wet flue gas and thereby produce gypsum as a by-product of the removal of the SO₂ from the SO₂ laden wet flue gas; and

the at least one CTPP includes one or more of the first parameter corresponding to the pH of the limestone slurry applied, the second parameter corresponding to the distribution of the limestone slurry applied, and a third parameter corresponding to an amount of the oxidation air applied;

the set point for one of the first, the second and the third parameters is determined based on (i) the current value of that parameter, and the amount of SO₂ in the exhausted desulfurized flue gas, and (ii) the identified optimization objective; and

the control of the one parameter is directed based on the determined set point for that parameter to optimize the WFGD process for the identified optimization objective.

24. The method according to claim 14, wherein:

the process is a selective catalytic reduction (SCR) process that applies ammonia and dilution air to remove NO_x from NO_x laden flue gas and thereby control emissions of NO_x, and to exhaust reduced NO_x flue gas;

the at least one CTPP includes a parameter corresponding to an amount of the ammonia applied;

the AOP is an amount of NO_x in the exhausted flue gas;

the set point for the parameter is determined based on (i) the current value of the parameter, and the amount of NO_x in the exhausted flue gas and (ii) the identified optimization objective; and

the control of the parameter is directed based on the determined set point to optimize the SCR process for the identified optimization objective.

25. The method according to claim 24, wherein:

the MPPs include an amount of ammonia in the exhausted flue gas; and

the set point for the parameter is determined based also on a current value of the amount of ammonia in the exhausted flue gas values.