Cascaded control of an average value of a process parameter to a desired value

1. A multi-tier 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 first limit on an actual average value (AAV) of the TPP over a defined time period of length TPL AAV2 , with the AAV computed based on actual values (AVs) of the TPP over the defined period, comprising:

a first logical controller having logic to predict future average values (FAVs) of the TPP over a first future time period (FFTP) having a length of at least TPL AAV2 , and extending from a current time T 0 to an future time T AAV2 , at or prior to which the TPP will move to steady state, wherein the FAVs are predicted based on (i) the AAVs of the TPP at various times over a first prior time period (FPTP) having a length of at least TPL AAV2 and extending from a prior time of T −AAV2 to the current time T 0 , (ii) the current values of the MPPs, and (iii) the DTV; and

a second logical controller having logic (a) to establish a further target value (FTV) representing a second limit on the AAV of the TPP for a second future time period (SFTP), the SFTP having a length equal to TPL AAV1 which is less than the length TPL AAV2 and extending from the current time T 0 to a future time T AAV1 , wherein the FTV is established based on one or more of the predicted FAVs of the TPP over the FFTP, (b) to determine a target set point for each CTPP based on (i) the AAVs of the TPP at various times over a second prior time period (SPTP) having the length TPL AAV1 and extending from a prior time T −AAV1 to the current time T 0 , (ii) the current values of the MPPs, and (iii) the FTV, and (c) to direct control of each CTPP in accordance with the determined target set point for that CTPP.

2. The multi-tier controller according to claim 1 , wherein:

the target set point for each CTPP is determined by (a) predicting FAVs of the TPP over the SFTP based on (i) the AAVs of the TPP at various times over the SPTP, and (ii) the current values of the MPPs, and (b) also predicting FAVs of the TPP at various times over the SFTP based on (i) the current values of the MPPs, and (ii) the target set point for each CTPP.

3. The multi-tier controller according to claim 1 , further comprising:

a storage medium configured to store historical data representing the AAVs of the TPP over the FPTP.

4. The multi-tier controller according to claim 1 , wherein:

the FTV is established for the entire SFTP.

5. The multi-tier controller according to claim 1 , wherein:

the second logical controller 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 T 0 will comply with the DTV.

6. The multi-tier controller according to claim 1 , further comprising:

an input device configured to input, at or before the current time T 0 , an event which is to occur at or after the current time T 0 ;

wherein the first logical controller has further logic to predict the FAVs of the TPP over the FFTP based also on the input event;

wherein the second logical controller has the further logic to determine the target set point for each CTPP based also on the input event.

7. The multi-tier controller according to claim 6 , wherein:

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

8. The multi-tier controller according to claim 7 , wherein:

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

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

9. The multi-tier controller according to claim 1 , wherein:

the system is a wet flue gas desulfurization (WFGD) system that receives SO 2 laden wet flue gas, applies limestone slurry to remove SO 2 from the received SO 2 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 level of the limestone slurry applied and a parameter corresponding to a distribution of the limestone slurry applied; and

the TPP is a parameter corresponding to an amount of SO 2 in the exhausted desulfurized flue gas.

10. The multi-tier 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.

11. The multi-tier controller according to claim 1 , further comprising:

a one of a neural network process model 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 first logical controller predicts the FAVs in accordance with the one model;

wherein the second logical controller determines the target set point for each CTPP in accordance with the one model.

12. The multi-tier controller according to claim 11 , wherein:

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

13. A controller for directing operation of a system performing a process, the process having multiple process parameters (MPPs), including at least one controllable process parameter (CTPP) and at least one targeted process parameter (TPP), and having a defined target value (DTV) representing a first limit on an actual average value (MV) of the TPP over a time period (TP), comprising:

one of a neural network process model and a non-neural network process model, the one model representing a relationship between the TPP and the at least one CTPP;

first logic to predict a path corresponding to future average values (FAVs) of the TPP over a first time period (FTP) extending from a current time T 0 to a future time T F1 , prior to which the TPP will move to a steady state condition, and having a length of at least TP, based on (i) the AAVs of the TPP at various times over a first prior time period having a length of at least TP and extending from a prior time T −F1 to the current time T 0 , (ii) the current MPPs, (iii) the DTV, and (iv) the one model; and

second logic to establish a further target value (FTV) representing a second limit on the AAV of the TPP for a second time period (STP) extending from the current time T 0 to a future time T F2 and having a length less than the FTP, based on the predicted path, to determine a target set point for each CTPP based on the FTV and the one model, and to direct control of the system operations based on the target set point for each CTPP.

14. A method for directing performance 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 first limit on an actual average value (AAV) of the TPP over a defined time period of length TPL AAV2 , with the MV computed based on actual values (AVs) of the TPP over the defined period, comprising:

predicting future average values (FAVs) of the TPP over a first future time period (FFTP) having a length of at least TPL AAV2 and extending from a current time T 0 to an future time T AAV2 , at or prior to which the TPP will move to steady state, wherein the FAVs are predicted based on (i) the AAVs of the TPP at various times over a first prior time period (FPTP) having a length of at least TPL AAV2 and extending from a prior time of T −AAV2 to the current time T 0 , (ii) the current values of the MPPs, and (iii) the DTV;

establishing a further target value (FTV) representing a second limit on the AAV of the TPP at the end of a second future time period (SFTP), the SFTP having a length equal to TPL AAV1 which is less than the length TPL AAV2 and extending from the current time T 0 to a future time T AAv1 , wherein the FTV is established based on one or more of the predicted FAVs of the TPP over the FFTP;

determining a target set point for each CTPP based on (i) the AAVs of the TPP at various times over a second prior time period (SPTP) having the length TPL AAV1 and extending from a prior time T −AAV1 to the current time T 0 , (ii) the current values of the MPPs, and (iii) the FTV;

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

15. The method according to claim 14 , wherein:

the target set point for each CTPP is determined by (a) predicting FAVs of the TPP over the SFTP based on (i) the AAVs of the TPP at various times over the SPTP, and (ii) the current values of the MPPs, and (b) also predicting FAVs of the TPP at various times over the SFTP based on (i) the current values of the MPPs, and (ii) the target set point for each CTPP.

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

storing historical data representing the AAVs of the TPP over the FPTP.

17. The method according to claim 14 , 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 T 0 will comply with the DTV.

18. The method according to claim 15 , further comprising:

receiving, at or before the current time T 0 , an input corresponding to an event which is to occur at or after the current time T 0 ;

wherein the FAVs of the TPP over the FFTP are predicted based also on the input event;

wherein the target set point for each CTPP is determined based on the input event.

19. The method according to claim 18 , wherein:

the input represents a change in at least one of the MPPs or at least one non-process parameter (NPP) associated with performance of the process.

20. The method according to claim 14 , wherein:

the process a wet flue gas desulfurization (WFGD) process that receives SO 2 laden wet flue gas, applies limestone slurry to remove SO 2 from the received SO 2 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 level of the limestone slurry being applied and a parameter corresponding to an amount of the limestone slurry being applied; and

the TPP is a parameter corresponding to an amount of SO 2 in the exhausted desulfurized flue gas.

21. The method according to claim 14 , 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 NO x in the exhausted flue gas.

22. The method according to claim 14 , wherein:

the FAVs are predicted and the target set point for each CTPP is determined in accordance with one of a neural network process model and a non-neural network process model; and

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

23. The method according to claim 22 , wherein:

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

24. A method for directing control of the performance of a process, the process having multiple process parameters (MPPs), including at least one controllable process parameter (CTPP) and at least one targeted process parameter (TPP), and having a defined target value (DTV) representing a first limit on an actual average value (AAV) of the TPP over a time period (TP), comprising:

predicting a path corresponding to future average values (FAVs) of the TPP over a first time period (FTP) extending from a current time T 0 to a future time T F1 , prior to which the TPP will move to a steady state condition, and having a length of at least TP, based on (i) the AAVs of the TPP at various times over a first prior time period having a length of at least TP and extending from a prior time T −F1 to the current time T 0 , (ii) the current MPPs, (iii) the DTV, and (iv) one of a neural network model and a non-neural network model, the one model representing a relationship between the TPP and the at least one CTPP;

establishing a further target value (FTV) representing a second limit on the AAV of the TPP for a second time period (STP) extending from a current time T 0 to a future time T F2 and having a length less than the FTP, based on the predicted path;

determining a target set point for each CTPP based on the established FTV and the one model; and

directing control of performance of the process based on the target set point for each CTPP.