Model based control and estimation of mercury emissions

• US 8,644,961 B2
• Filed: 12/12/2005
• Issued: 02/04/2014
• Est. Priority Date: 12/12/2005
• Status: Active Grant

• Current Assignee: GE Digital Holdings LLC (GE Aerospace)
• Original Assignee: NeuCo Incorporated (General Electric Company)
• Inventors: Piche, Stephen, Wroblewski, David J.
• Primary Examiner(s): HURST, JONATHAN M
• Application Number:
  US11/301,034
• Publication Number:
  US 20070156288A1
• Time in Patent Office: 2,976 Days
• Field of Search: 700/266, 700/286, 700/28, 706/19, 706/21, 706/907
• US Class Current: 700/28
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

A method and apparatus for estimating and/or controlling mercury emissions in a steam generating unit. A model of the steam generating unit is used to predict mercury emissions. In one embodiment of the invention, the model is a neural network (NN) model. An optimizer may be used in connection with the model to determine optimal setpoint values for manipulated variables associated with operation of the steam generating unit.

Claims

1. A system for controlling operation of a fossil fuel fired steam generating unit, the system comprising:

a model of the steam generating unit embodied in a computer readable medium, said model predicting mercury emissions of the steam generating unit based upon a plurality of input values that are current values for manipulated variables and disturbance variables associated with current operating conditions of the steam generating unit, and for outputting at least one output value indicative of mercury emitted by said steam generating unit in accordance with the input current values of the manipulated variables and the disturbance variables; and

a computer control system for receiving the at least one output value from the model and determining optimal setpoint values for manipulated variables using the at least one output value of the model, and for controlling operation of the steam generating unit in accordance with the determined optimal setpoint values for the manipulated variables, said manipulated variables affecting mercury oxidation by influencing one or more of the following factors for oxidation of mercury;

proximity of compounds reacting with mercury, residence time of mercury within a reaction temperature zone, and the presence of surface structures as they relate to conversion reactions, wherein said computer control system comprises;

an optimizer for determining the optimal setpoint values for the manipulated variables to obtain a desired level of mercury emissions of the steam generating unit, said optimizer receiving said at least one output value from said model to determine the optimal setpoint values for the manipulated variables, wherein said optimizer determines a respective optimal setpoint value for each said manipulated variable by accessing said model to minimize a cost value of a cost function that is a mathematical representation of one or more goals, while observing a plurality of constraints, said cost value affected by the value for each said manipulated variable, said cost function including a predicted value indicative of elemental mercury emissions of the steam generating unit and a value associated with carbon in ash (CIA), said cost function including a term that decreases as the level of mercury oxidation increases,

wherein said computer control system repeatedly provides the model with current values of the manipulated and disturbance variables associated with current operating conditions of the steam generating unit, in order to determine updated optimal setpoint values from the at least one output value of the model.

2. A system according to claim 1, wherein said cost value is affected by the value of each said manipulated variable over a plurality of time intervals, said optimizer determining a respective value for each said manipulated variable for the plurality of time intervals in accordance with minimization of said cost value.

3. A system according to claim 2, wherein the respective value of each said manipulated variable for a first time interval of the plurality of time intervals is determined as said respective optimal setpoint value for an optimization cycle.

4. A system according to claim 2, wherein said optimizer determines said respective value for each said manipulated variable for said plurality of time intervals while observing said plurality of constraints across said plurality of time intervals.

5. A system according to claim 1, wherein said plurality of constraints include at least one of the following:

a carbon in ash (CIA) limit for fly ash generated by the steam generating unit, a CO emissions limit of the steam generating unit, a NOx emissions limit of said steam generating unit, and a mercury emissions limit of said steam generating unit.

6. A system according to claim 1, wherein said cost function further includes at least one of the following:

a predicted value for a controlled variable indicative of NOx emissions of said steam generating unit; and

a predicted value indicative of boiler efficiency of said steam generating unit.

7. A system according to claim 1, wherein said optimizer is implemented using a technique selected from the group consisting of the following:

non-linear programming, stochastic programming, global non-linear programming, genetic algorithms, and particle/swarm techniques.

8. A system according to claim 1, wherein said at least one output value indicative of mercury emitted by said steam generating unit is associated with at least one of the following:

total mercury emitted by said steam generating unit;

oxidized mercury emitted by said steam generating unit;

elemental mercury emitted by said steam generating unit; and

particulate mercury emitted by said steam generating unit.

9. A system according to claim 1, wherein said computer control system means includes a system for providing regulatory control.

10. A system according to claim 9, wherein said computer control system includes a supervisory controller in communication with said system for providing regulatory control.

11. A system according to claim 1, wherein said manipulated variables include at least one of the following:

level of excess oxygen in flue gas produced by the steam generating unit, over-fire air (OFA) damper positions of the steam generating unit, windbox-to-furnace differential pressure (WFDP) of the steam generating unit, mill bias of each mill of the steam generating unit, burner tilt angles of the steam generating unit, operational states of soot cleaning devices of the steam generating unit, chemical composition of fuel for the steam generating unit, blend of coal used as fuel in the steam generating unit, and blend of coal and additives used as fuel in the steam generating unit.

12. A system according to claim 1, wherein said disturbance variables include at least one of the following:

characteristics of fuel for the steam generating unit, conditions of mill grind of the steam generating unit, load demand associated with the steam generating unit, and ambient conditions of the steam generating unit.

13. A system according to claim 1, wherein said model includes an output for outputting at least one output value associated with at least one of the following controlled variables:

total mercury emitted by the steam generating unit;

elemental mercury emitted by the steam generating unit;

total oxidized mercury emitted by the steam generating unit;

carbon in ash;

surface area fly ash particles;

particle size distribution of fly ash particles;

CO emissions of the steam generating unit;

“

heat rate”

of the steam generating unit;

“

boiler efficiency”

of the steam generating unit;

NOx emissions of the steam generating unit;

heterogeneous reaction of mercury in the steam generating unit, and reactivity of fly ash with mercury.

14. A system according to claim 1, wherein said model is updated using an output value of said model indicative of mercury emitted by said steam generating unit and a measured value indicative of mercury emitted by said steam generating unit.

15. A system according to claim 1, wherein said model is updated using feedback biasing.

16. A system according to claim 1, wherein said model is retrained on-line using a regression based approach.

17. A system according to claim 16, wherein said regression based approach is selected from the group consisting of the following:

a backpropagation algorithm, a conjugate gradient method, and an optimization technique.

18. A system according to claim 1, wherein said model is executed on-line or in a simulation.

48. A system according to claim 1, wherein said cost function includes terms over a future time horizon.

19. A method for controlling mercury emissions produced by a fossil fuel fired steam generating unit, the method comprising:

(a) obtaining current values of manipulated variables and disturbance variables associated with current operating conditions of the steam generating unit, said manipulated variables affecting mercury oxidation by influencing one or more of the following factors for oxidation of mercury;

proximity of compounds reacting with mercury, residence time of mercury within a reaction temperature zone, and the presence of surface structures as they relate to conversion reactions;

(b) inputting the current values of the manipulated variables and the disturbance variables into a model of said steam generating unit for predicting mercury emissions, said model generating at least one output value indicative of mercury emissions produced by said steam generating unit in accordance with the input current values of the manipulated variables and the disturbance variables;

(c) sending the at least one output value to a control means for controlling operation of the steam generating unit, said control means determining optimal setpoint values for manipulated variables using the at least one output value of the model by minimizing a cost value of a cost function that is a mathematical representation of one or more goals, while observing a plurality of constraints, said cost value affected by the value of said manipulated variables, said cost function including a predicted value indicative of elemental mercury emissions of the steam generating unit and a value associated with carbon in ash (CIA), said cost function including a term that decreases as the level of mercury oxidation increases;

(d) controlling operation of said steam generating unit with the control means, using the determined optimal setpoint values for the manipulated variables;

(e) repeating steps (a), (b), (c) and (d) during operation of the steam generating unit, as current operating conditions of the steam generating unit change.

20. A method according to claim 19, wherein said control means includes a regulatory control system.

21. A method according to claim 19, wherein said model is executed on-line or in a simulation.

22. A method according to claim 20, wherein said control means includes a supervisory controller in communication with said regulatory control system, said supervisory controller receiving said at least one output value.

23. A method according to claim 19, wherein said manipulated variables include at least one of the following:

level of excess oxygen in flue gas produced by the steam generating unit, over-fire air (OFA) damper positions of the steam generating unit, windbox-to-furnace differential pressure (WFDP) of the steam generating unit, mill bias of each mill of the steam generating unit, burner tilt angles of the steam generating unit, operational states of soot cleaning devices of the steam generating unit, chemical composition of fuel for the steam generating unit, blend of coal used as fuel in the steam generating unit, and blend of coal and additives used as fuel in the steam generating unit.

24. A method according to claim 19, wherein said disturbance variables include at least one of the following:

characteristics of fuel for the steam generating unit, conditions of mill grind of the steam generating unit, load demand associated with the steam generating unit, and ambient conditions of the steam generating unit.

25. A method according to claim 24, wherein said characteristics of the fuel including at least one of the following:

ash content volatile matter, moisture content, viscosity, constituent minerals, and BTU content.

26. A method according to claim 19, wherein said model is selected from the group consisting of one or more of the following:

a steady state model and a dynamic model.

27. A method according to claim 19, wherein said model is selected from the group consisting of one or more of the following:

a neural network model, an empirically developed model, a model developed using “

first principles,” and

a model based upon heuristics.

28. A method according to claim 19, wherein said at least one output value indicative of mercury emissions is associated with at least one of the following:

total mercury emitted by the steam generating unit;

elemental mercury emitted by the steam generating unit; and

total oxidized mercury emitted by the steam generating unit.

29. A method according to claim 19, wherein said model generates at least one output value associated with at least one of the following:

carbon in ash;

surface area fly ash particles;

particle size distribution of fly ash particles;

CO emissions of the steam generating unit;

“

heat rate”

of the steam generating unit;

“

boiler efficiency”

of the steam generating unit;

NOx emissions of the steam generating unit;

heterogeneous reaction of mercury in the steam generating unit, and reactivity of fly ash with mercury.

30. A method according to claim 19, wherein said model is updated using an output value of said model indicative of mercury emitted by said steam generating unit and a measured value indicative of mercury emitted by said steam generating unit.

31. A method according to claim 19, wherein said model is updated using feedback biasing.

32. A method according to claim 19, wherein said model is retrained on-line using a regression based approach.

33. A method according to claim 32, wherein said regression based approach is selected from the group consisting of the following:

a backpropagation algorithm, a conjugate gradient method, and an optimization technique.

49. A system according to claim 19, wherein said cost function includes terms over a future time horizon.

34. A method for controlling operation of a fossil fuel fired steam generating unit, the method comprising:

(a) generating a model of the steam generating unit for predicting mercury emissions of the steam generating unit;

(b) obtaining current values of manipulated variables and disturbance variables associated with current operating conditions of the steam generating unit, said manipulated variables affecting mercury oxidation by influencing one or more of the following factors for oxidation of mercury;

proximity of compounds reacting with mercury, residence time of mercury within a reaction temperature zone, and the presence of surface structures as they relate to conversion reactions;

(c) inputting the current values of the manipulated variables and the disturbance variables into the model of the steam generating unit for predicting mercury emissions, said model generating at least one output value indicative of mercury emitted by the steam generating unit in accordance with the input current values of the manipulated variables and the disturbance variables;

(d) determining optimal setpoint values for the manipulated variables using the at least one output value of the model and minimizing a cost value of a cost function that is a mathematical representation of one or more goals, while observing a plurality of constraints, said cost value affected by the value of said manipulated variables, said cost function including a predicted value indicative of elemental mercury emissions of the steam generating unit and a value associated with carbon in ash (CIA), said cost function including a term that decreases as the level of mercury oxidation increases;

(e) controlling operation of the steam generating unit using the optimal setpoint values for the manipulated variables; and

(f) repeating steps (a) through (e) during operation of the steam generating unit, as current operating conditions of the steam generating unit change.

35. A method according to claim 34, wherein said cost value is affected by the value of said manipulated variables over a plurality of time intervals, said optimal setpoint values determined at a first time interval of the plurality of time intervals.

36. A method according to claim 35, wherein the respective value of each said manipulated variable for a first time interval of the plurality of time intervals is determined as said respective optimal setpoint value for an optimization cycle.

37. A method according to claim 35, wherein said respective value for each said manipulative variable is determined for said plurality of time intervals while observing said plurality of constraints across said plurality of time intervals.

38. A method according to claim 34, wherein said plurality of constraints include at least one of the following:

a carbon in ash (CIA) limit for fly ash generated by the steam generating unit, a CO emissions limit of the steam generating unit, a NOx emissions limit of said steam generating unit, and a mercury emissions limit of said steam generating unit.

39. A method according to claim 34, wherein said cost function further includes at least one of the following:

a predicted value for a controlled variable indicative of NOx emissions of said steam generating unit; and

a predicted value indicative of boiler efficiency of said steam generating unit.

40. A method according to claim 34, wherein said manipulated variables include at least one of the following:

level of excess oxygen in flue gas produced by the steam generating unit, over-fire air (OFA) damper positions of the steam generating unit, windbox-to-furnace differential pressure (WFDP) of the steam generating unit, mill bias of each mill of the steam generating unit, burner tilt angles of the steam generating unit, operational states of soot cleaning devices of the steam generating unit, chemical composition of fuel for the steam generating unit, blend of coal used as fuel in the steam generating unit, and blend of coal and additives used as fuel in the steam generating unit.

41. A method according to claim 34, wherein said disturbance variables include at least one of the following:

characteristics of fuel for the steam generating unit, conditions of mill grind of the steam generating unit, load demand associated with the steam generating unit, and ambient conditions of the steam generating unit.

42. A method according to claim 34, wherein said model of the steam generating unit outputting at least one output value indicative of at least one of the following:

total mercury emitted by the steam generating unit;

elemental mercury emitted by the steam generating unit;

total oxidized mercury emitted by the steam generating unit;

carbon in ash;

surface area of fly ash particles;

particle size distribution of fly ash particles;

CO emissions of the steam generating unit;

“

heat rate”

of the steam generating unit;

“

boiler efficiency”

of the steam generating unit;

NOx emissions of the steam generating unit;

heterogeneous reaction of mercury in the steam generating unit, and reactivity of fly ash with mercury.

43. A method according to claim 34, wherein said model is updated using an output value of said model indicative of mercury emitted by said steam generating unit and a measured value indicative of mercury emitted by said steam generating unit.

44. A method according to claim 34, wherein said model is updated using feedback biasing.

45. A method according to claim 34, wherein said model is retrained on-line using a regression based approach.

46. A method according to claim 45, wherein said regression based approach is selected from the group consisting of the following:

a backpropagation algorithm, a conjugate gradient method, and an optimization technique.

47. A method according to claim 34, wherein said model is executed on-line or in a simulation.

50. A system according to claim 34, wherein said cost function includes terms over a future time horizon.