Control of rolling or moving average values of air pollution control emissions to a desired value

• US 7,113,835 B2
• Filed: 08/27/2004
• Issued: 09/26/2006
• Est. Priority Date: 08/27/2004
• Status: Active Grant

• Current Assignee: General Electric Technology GmbH (General Electric Company)
• Original Assignee: Alstom Technology Ltd. (Alstom SA)
• Inventors: Piche, Stephen, Boyden, Scott A.
• Primary Examiner(s): BARNES-BULLOCK, CRYSTAL JOY
• Application Number:
  US10/927,201
• Publication Number:
  US 20060047347A1
• Time in Patent Office: 760 Days
• Field of Search: 700/28, 700/29, 700 31- 33, 700/37, 700/44, 700/51, 700/52, 700108-110, 700/117, 700/196, 700174-178, 702/22, 702/23, 702/31.32, 702/81, 702/84, 702/182, 702/189, 702/199, 706/16, 706/21, 706/23, 422/129, 422/172, 422168-170, 422/234, 422/243, 422/255, 422/261, 422/262, 423/210, 423/555, 423/242.1, 423/243.08, 423/243.01, 423/243.11
• US Class Current: 700/52
• CPC Class Codes: G05B 13/048 using a predictor

Abstract

A controller directs performance of a process having multiple process parameters (MPPs), including a controllable process parameter (CTPP), a targeted process parameter (TPP), a defined target value (DTV) representing a limit on an actual average value (AAV) of the TPP over a defined moving time period of length TPL_AAV. A storage device stores historical data representing the AVs of the TPP at various times over a prior time period (PTP) having a length of at least TPL_AAV. A processor predicts future average values (FAVs) of the TPP over a future time period (FTP) based on the stored historical data and the current values of the MPPs. The processor also determines a target set point for each CTPP based on the predicted FAVs, the current values of the MPPs and the DTV, and directs control of each CTPP in accordance with the determined target set point for that CTPP.

Claims

1. A controller for directing operation of a system performing a process, having multiple process parameters (MPPs), at least one of the MPPs being a controllable process parameter (CTPP) and one of the MPPs being a targeted process parameter (TPP), and having a defined target value (DTV) representing a limit on an actual average value (AAV) of the TPP over a defined moving time period of length TPL_AAV, with the AAV computed based on actual values (AVs) of the TPP over the defined period, comprising:

a storage medium configured to store historical data representing the AVs of the TPP at various times over a prior time period (PTP) having a length of at least TPL_AAV and extending from a prior time of T<sub>−

AAV1</sub> to a current time T₀; and

a processor configured to (a) predict future average values (FAVs) of the TPP over a future time period (FTP) having a length of at least TPL_AAV and extending from the current time T₀ to a future time T_AAV1, on or after which the TPP will move to steady state, wherein the FAVs of the TPP over the FTP are predicted based on (i) the stored historical data representing the AVs of the TPP at various times over the PTP and (ii) the current values of the MPPs, (b) determine a target set point for each CTPP based on (i) the predicted FAVs of the TPP over the FTP, (ii) the current values of the MPPs, and (iii) the DTV, and (c) direct control of each CTPP in accordance with the determined target set point for that CTPP.

2. The controller according to claim 1, wherein:

the processor is further configured to predict the FAVs of the TPP at various times over the FTP based on (i) the stored historical data, (ii) the current values of the MPPs, and (iii) the determined target set point for each CTPP.

3. The controller according to claim 1, wherein:

the processor is further configured to determine the target set point for each CTPP such that the AAV of the TPP over each of a plurality of moving time periods (MTPs), each having a different start time and each having an end time after the current time T₀ will comply with the DTV.

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

an input device configured to input, at or before the current time T₀, an event which is to occur at or after the current time T₀;

wherein the processor is further configured to predict the FAVs of the TPP over the FTP based also on the input event.

5. The controller according to claim 4, wherein:

the input event is indicative of a change in at least one of the MPPs or at least one non-process parameter (NPP) associated with operation of the system to perform the process which is planned to occur after the end of the FTP.

6. The controller according to claim 5, wherein:

the at least one of the MPPs includes a load on the system; and

the at least one NPP includes at least one of a cost of electrical power, a value of a regulatory credit and a value of a byproduct of the process.

7. 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 exhausts desulfurized flue gas;

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

the TPP is an amount of SO₂ in the exhausted desulfurized flue gas.

8. 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 to remove NO_x from the received NO_x laden flue gas, thereby controlling 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; and

the TPP is an amount of NO_x in the exhausted flue gas.

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

one of a neural network and a non-neural network process model;

wherein the one model represents a relationship between the TPP and the at least one CTPP;

wherein the processor predicts the FAVs and determines the target set point for each CTPP in accordance with the one model.

10. The controller according to claim 9, wherein:

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

11. A method for directing operation of a process having multiple process parameters (MPPs), at least one of the MPPs being a controllable process parameter (CTPP) and one of the MPPs being a targeted process parameter (TPP), and having a defined target value (DTV) representing a regulatory limit on an actual average value (AAV) of the TPP over a defined moving time period of length TPL_AAV, with the AAV computed based on actual values (AVs) of the TPP over the defined period, comprising:

storing historical data representing the AVs of the TPP at various times over a prior time period (PTP) having the length TPL_AAV and extending from a prior time of T<sub>−

AAV1</sub> to a current time T₀;

predicting future average values (FAVs) of the TPP over a future time period (FTP) having the length of at least TPL_AAV and extending from the current time T₀ to a future time T_AAV1, on or after which the TPP will move to steady state, wherein the FAVs of the TPP over the FTP are predicted based on (i) historical data representing the AVs of the TPP at various times over the PTP and (ii) the current values of the MPPs;

determining a target set point for each CTPP based on (i) the predicted FAVs of the TPP over the FTP, (ii) the current values of the MPPs, and (iii) the DTV; and

directing control of each CTPP in accordance with the determined target set point for that CTPP.

12. The method according to claim 11, further comprising:

predicting the FAVs of the TPP at various times over the FTP based on (i) the historical data, (ii) the current values of the MPPs, and (iii) the determined target set point for each CTPP.

13. The method according to claim 11, wherein:

the target set point for each CTPP is determined such that the AAV of the TPP over each of a plurality of moving time periods (MTPs), each having a different start time and each having an end time after the current time T₀ will comply with the DTV.

14. The method according to claim 11, further comprising:

receiving, at or before the current time T₀, notification of an event which is to occur at or after the current time T₀;

wherein the FAVs of the TPP over the FTP are predicted based also on the input event.

15. The method according to claim 11, wherein:

the process is a wet flue gas desulfurization (WFGD) process that applies limestone slurry to remove SO₂ from SO₂ laden wet flue gas, and exhausts desulfurized flue gas;

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

the TPP is an amount of SO₂ in the exhausted desulfurized flue gas.

16. The method according to claim 11, wherein:

the process is a selective catalytic reduction (SCR) process that applies ammonia to remove NO_x from NO_x laden flue gas, thereby controlling 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; and

the TPP is an amount of NOx in the exhausted flue gas.

17. The method according to claim 11, wherein:

the FAVs are predicted in accordance with one of a neural network process model and neural network process model;

the target set point for each CTPP is determined in accordance with the one model; and

the one model represents a relationship between the TPP and the at least one CTPP.

18. The method according to claim 17, wherein:

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