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Having described the invention, the following is claimed: 1. A computer system programmed to optimize operation of at least one power generating unit comprised of a plurality of components, the computer system comprising: a plurality of component optimization systems respectively associated with each of said plurality of components, wherein each component optimization system respectively optimizes operation of a component, each component optimization system including: a model of the component, said model receiving input values associated with manipulate...
Having described the invention, the following is claimed: 1. A computer system programmed to optimize operation of at least one power generating unit comprised of a plurality of components, the computer system comprising: a plurality of component optimization systems respectively associated with each of said plurality of components, wherein each component optimization system respectively optimizes operation of a component, each component optimization system including: a model of the component, said model receiving input values associated with manipulated variables and disturbance variables, and predicting an output value for at least one controlled variable associated with operation of said component, wherein the manipulated variables are variables changeable by an operator or component optimization system to affect the at least one controlled variable, and an optimizer for determining optimal setpoint values for manipulated variables associated with control of the component, wherein said optimal setpoint values are determined in accordance with one or more goals and constraints associated with operation of the component, said optimizer determining optimal setpoint values by minimizing a cost function that mathematically represents the one or more goals, wherein said plurality of component optimization systems perform optimizations of respective components in sequential order, at least one output of an earlier performing component optimization system is passed forward as an input to a subsequent performing component optimization system, said plurality of component optimization systems in communication with a distributed control system (DCS) to provide optimal setpoint values thereto, wherein the DCS provides regulatory control of said at least one power generating unit. 2. A computer system according to claim 1, wherein said model is selected from the group consisting of the following: a steady state model and a dynamic model. 3. A computer system according to claim 1, wherein said model is selected from the group consisting of the following: a neural network model, an empirically developed model, a model developed using "first principles," a support vector machine (SVM) model, a model developed by linear regression and a model based upon heuristics. 4. A computer system according to claim 1, wherein said optimizer is selected from the group consisting of: linear programming, quadratic programming, mixed integer non-linear programming (NLP), stochastic programming, global non-linear programming, genetic algorithms, and particle/swarm techniques. 5. A computer system according to claim 1, wherein said optimizer determines said optimal setpoint values for said manipulated variables by accessing said model to minimize a cost value of the cost function while observing said constraints, said cost value affected by the value of said manipulated variables. 6. A computer system according to claim 5, 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. 7. A computer system according to claim 6, 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 value for an optimization cycle. 8. A computer system according to claim 6, 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. 9. A computer system according to claim 1, wherein said constraints include at least one of the following: a limit on emissions of NOx, a limit on emissions of SO2, a limit on emissions of CO2, a limit on emissions of CO, a limit on emissions of mercury, a limit on ammonia slip, and a limit on emissions of particulate matter. 10. A computer system according to claim 1, wherein said plurality of components includes at least one of the following: a fuel blending system, a boiler, a selective catalytic reduction (SCR) system, an electro-static precipitator (ESP) and a flue gas desulfurization (FGD) system. 11. A computer system according to claim 10, wherein for said fuel blending system: said manipulated variables include at least one of the following: amount of each fuel to be blended and amount of each fuel additive; said disturbance variables include at least one of the following: characteristics of each said fuel to be blended and characteristics of each fuel additive; and said controlled variables include at least one of the following: amount of blended fuel, heat index, nitrogen content of blended fuel, sulfur content of blended fuel, mercury content of blended fuel, and ash content of blended fuel. 12. A computer system according to claim 10, wherein for said boiler: said manipulated variables include at least one of the following: level of excess oxygen in flue gas, over-fire air (OFA) damper positions, windbox-to-furnace differential pressure (WFDP), biases to each mill, and burner tilt angles, said disturbance variables include at least one of the following: coal characteristics, fineness of mill grind, and load demand said controlled variables include at least one of the following: total mercury emissions, carbon in ash (CIA), nitrogen oxide emissions, carbon monoxide emissions, boiler efficiency, and steam temperatures. 13. A computer system according to claim 10, wherein for said SCR system: said manipulated variables include ammonia injection; said disturbance variables include at least one of the following: inlet NOx, temperature, and load; said controlled variables include at least one of the following: ammonia slip and outlet NOx. 14. A computer system according to claim 10, wherein for said ESP: said manipulated variables include average power in each field of the ESP; said disturbance variables include at least one of the following: inlet particulate matter, and load; said controlled variables include at least one of the following: opacity and output particulate matter. 15. A computer system according to claim 10, wherein for said wet flue gas desulfurization (FGD) system: said manipulated variables include the pH concentration within an absorber, amount of forced air into the absorber, and number of recycle pumps used to distribute slurry in the absorber; said disturbance variables include at least one of the following: inlet SO2 concentration and load; said controlled variables include at least one of the following: outlet SO2 concentration and gypsum properties. 16. A computer system according to claim 1, wherein said at least one output of the earlier performing component optimization system is associated with controlled variables of the earlier performing component optimization system. 17. A computer system according to claim 16, wherein said input to the subsequent performing component optimization system is associated with disturbance variables of the subsequent performing component optimization system. 18. A computer system according to claim 16, wherein said at least one output is associated with controlled variables over a predetermined time period. 19. A computer system according to claim 1, wherein said optimizer uses model predictive control (MPC) to determine optimal setpoint values for the manipulated variables. 20. A computer system according to claim 1, wherein said system further comprises: a unit optimization system for determining optimal values of said one or more goals and said constraints used by each of said component optimization systems, wherein the unit optimization system includes: a unit model for each of said components, each said unit model receiving input values associated with manipulated variables and disturbance variables and predicting an output value for at least one controlled variable associated with operation of said component, and a unit optimizer for determining optimal setpoint values for at least one of manipulated variables and controlled variables associated with control of the component, said optimal setpoint values determined in accordance with one or more goals associated with operation of the power generating unit and constraints associated with operation of the power generating unit, wherein said optimal setpoint values determined by the unit optimizer are used to determine said one or more goals and said constraints for each of said plurality of component optimization systems. 21. A computer system according to claim 20, wherein said unit optimizer determines said optimal setpoint values by accessing said unit model to minimize a cost value of a unit cost function while observing said constraints, said unit cost function including terms related to economic data associated with operation of said power generating unit. 22. A computer system according to claim 21, wherein said economic data associated with operation of said power generating unit relates to at least one of the following: cost of fuels, cost of additives, cost of ammonia, cost of limestone used in an FGD, cost of internal electric power for the power generating unit, price of electricity, cost of NOx credits, cost of SO2 credits and price of gypsum. 23. A computer system according to claim 20, wherein said unit model is selected from the group consisting of the following: a steady state model and a dynamic model. 24. A computer system according to claim 20, wherein said unit model is selected from the group consisting of the following: a neural network model, an empirically developed model, a model developed using "first principles," a support vector machine (SVM) model, a model developed by linear regression and a model based upon heuristics. 25. A computer system according to claim 20, wherein said unit optimizer is selected from the group consisting of: linear programming, quadratic programming, mixed integer non-linear programming (NLP), stochastic programming, global non-linear programming, genetic algorithms, and particle/swarm techniques. 26. A computer system according to claim 1, wherein said goals include minimizing an emission of the at least one power generating unit. 27. A computer system according to claim 26, wherein said emission is selected from the group consisting of the following: nitrogen oxides (NOx), sulfur oxides (SOx), CO2, CO, mercury, ammonia and particulate matter. 28. A computer system according to claim 1, wherein said goals include controlling an amount of blended fuel to a setpoint value. 29. A computer system according to claim 1, wherein said goals include minimizing power consumption. 30. A method for optimizing operation of at least one power generating unit comprised of a plurality of components, the method comprising: optimizing a first component including the steps of: providing input values to a first model, wherein said first model is a model of a first component of the at least one power generating unit, said input values associated with manipulated variables and disturbance variables, wherein the manipulated variables are variables changeable by an operator or component optimization system to affect the at least one controlled variable; using said first model to predict one or more output values for one or more controlled variables associated with operation of said first component; and determining first optimal setpoint values for manipulated variables associated with control of said first component, said first optimal setpoint values determined in accordance with one or more goals and constraints associated with operation of the first component; and optimizing a second component including the steps of: providing input values to a second model, wherein said second model is a model of a second component of the at least one power generating unit, said input values associated with manipulated variables and disturbance variables, wherein at least one of the input values to the second model is one of the first optimal setpoint values determined by optimization of the first component; using said second model to predict one or more output values for one or more controlled variables associated with operation of said second component; and determining second optimal setpoint values for manipulated variables associated with control of said second component, said second optimal setpoint value determined in accordance with one or more goals and constraints associated with operations of the second component, wherein said first and second components are optimized in sequential order; and communicating said first and second optimal setpoint values to a distributed control system (DCS) that provides regulatory control of said at least one power generating unit. 31. A method according to claim 30, wherein said first and second models are selected from the group consisting of the following: a steady state model and a dynamic model. 32. A method according to claim 30, wherein said first and second models are selected from the group consisting of the following: a neural network model, an empirically developed model, a model developed using "first principles," a support vector machine (SVM) model, a model developed by linear regression and a model based upon heuristics. 33. A method according to claim 30, wherein said steps of determining first and second optimal setpoint values are performed by first and second optimizers respectively associated with said first and second models, said first and second optimizers selected from the group consisting of: liner programming, quadratic programming, mixed integer non-linear programming (NLP), stochastic programming, global non-liner programming, genetic algorithms, and particle/swarm techniques. 34. A method according to claim 30, wherein said steps of determining first and second optimal setpoint values includes: minimizing a cost value of a cost function while observing said constraints, said cost value affected by the value of said manipulated variables. 35. A method according to claim 34, wherein said cost value is affected by the value of each said manipulated variable over a plurality of time intervals. 36. A method according to claim 30, wherein said constraints include at least one of the following: a limit on emissions of NOx, a limit on emissions of SO2, a limit on emissions of CO2, a limit on emissions of CO, a limit on emissions of mercury, a limit on ammonia slip, and a limit on emissions of particulate matter. 37. A method according to claim 30, wherein said plurality of components includes at least one of the following: a fuel blending system, a boiler, a selective catalytic reduction (SCR) system, an electro-static precipitator (ESP) and a flue gas desulfurization (FGD) system. 38. A method according to claim 37, wherein for said fuel blending system: said manipulated variables include at least one of the following: amount of each fuel to be blended and amount of each fuel additive; said disturbance variables include at least one of the following: characteristics of each said fuel to be blended and characteristics of each fuel additive; and said controlled variables include at least one of the following: amount of blended fuel, heat index, nitrogen content of blended fuel, sulfur content of blended fuel, mercury content of blended fuel, and ash content of blended fuel. 39. A method according to claim 37, wherein for said boiler: said manipulated variables include at least one of the following: level of excess oxygen in flue gas, over-fire air (OFA) damper positions, windbox-to-furnace differential pressure (WFDP), biases to each mill, and burner tilt angles, said disturbance variables include at least one of the following: coal characteristics, fineness of mill grind, and load demand said controlled variables include at least one of the following: total mercury emissions, carbon in ash (CIA), nitrogen oxide emissions, carbon monoxide emissions, boiler efficiency, and steam temperatures. 40. A method according to claim 37, wherein for said SCR system: said manipulated variables include ammonia injection; said disturbance variables include at least one of the following: inlet NOx, temperature, and load; said controlled variables include at least one of the following: ammonia slip and outlet NOx. 41. A method according to claim 37, wherein for said ESP: said manipulated variables include average power in each field of the ESP; said disturbance variables include at least one of the following: inlet particulate matter, and load; said controlled variables include at least one of the following: opacity and output particulate matter. 42. A method according to claim 37, wherein for said wet flue gas desulfurization (FGD) system: said manipulated variables include the pH concentration within an absorber, amount of forced air into the absorber, and number of recycle pumps used to distribute slurry in the absorber; said disturbance variables include at least one of the following: inlet SO2 concentration and load; said controlled variables include at least one of the following: outlet SO2 concentration and gypsum properties. 43. A method according to claim 30, wherein said steps of determining first and second optimal setpoint values are performed using model predictive control (MPC). 44. A method according to claim 30, wherein the method further comprises: determining optimal values of said one or more goals and said constraints used to determine said first and second optimal setpoint values. 45. A method according to claim 44, wherein said step of determining optimum values of said one or more goals and said constraints includes: providing input values to a plurality of unit models, wherein each of said plurality of unit models is a unit model of a respective component of the at least one power generating unit, said input values associated with manipulated variables and disturbance variables; using each of said plurality of unit models to predict one or more output values for one or more controlled variables associated with operation of each of said plurality of components; determining optimal values for at least one of manipulated variables and controlled variables associated with control of each of said plurality of components, said optimal values determined in accordance with one or more goals and constraints associated with operation of the power generating unit. 46. A method according to claim 45, wherein said plurality of unit models are used to minimize a cost value of a unit cost function while observing said constraints, said unit cost function including terms related to economic data associated with operation of said power generating unit. 47. A method according to claim 46, wherein said economic data associated with operation of said power generating unit relates to at least one of the following: cost of fuels, cost of additives, cost of ammonia, cost of limestone used in an FGD, cost of internal electric power for the power generating unit, price of electricity, cost of NOx credits, cost of SO2 credits and price of gypsum. 48. A method according to claim 45, wherein each of said plurality of unit models is selected from the group consisting of the following: a steady state model and a dynamic model. 49. A method according to claim 45, wherein each of said plurality of unit models is selected from the group consisting of the following: a neural network model, an empirically developed model, a model developed using "first principles," a support vector machine (SVM) model, a model developed by linear regression and a model based upon heuristics. 50. A method according to claim 45, wherein said step of determining optimal values of said one or more goals and said constraints is performed by unit optimizers respectively associated with each of said plurality of unit models, said unit optimizers selected from the group consisting of the following: linear programming, quadratic programming, mixed integer non-linear programming (NLP), stochastic programming, global non-linear programming, genetic algorithms, and particle/swarm techniques. 51. A method according to claim 30, wherein said goals associated with operation of said first and second components include minimizing an emission of the at least one power generating unit. 52. A method according to claim 51, wherein said emission is selected from the group consisting of the following: nitrogen oxides (NOx), sulfur oxides (SOx), CO2, CO, mercury, ammonia and particulate matter. 53. A method according to claim 30, wherein said goals associated with operation of said first and second components include controlling an amount of blended fuel to a setpoint value. 54. A method according to claim 30, wherein said goals associated with operations of said first and second components include minimizing power consumption.
