Abstract: A method and apparatus for controlling a parameter associated with operation of a fossil fuel fired power generating unit. An optimizer determines optimal setpoint values for manipulated variables used to operate the power generating unit by accessing a model that predicts a parameter sensed by a plurality of sensors. The optimizer uses the model to minimize a cost valve of a cost function while observing at least one constraint associated with operation of the power generating unit.

Abstract: A virtual analyzer is provided to estimate either an attribute of a reactant applied during performance of, or an amount of a reactant exhausted by, a process having multiple process parameters (MPPs) that is performed to control an amount of a pollutant emitted into the air. The virtual analyzer includes an interface which receives signals corresponding to attributes of the MPPs. If the process is a wet flue gas desulfurization (WFGD) process, the signals include a signal corresponding to a measured pH level of the applied reactant. If the process is a selective catalytic reduction (SCR) process, the signals include a signal corresponding to a measured amount of the reactant exhausted by the process.

Abstract: A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.

Abstract: A virtual analyzer is provided to estimate either an attribute of a reactant applied during performance of, or an amount of a reactant exhausted by, a process having multiple process parameters (MPPs) that is performed to control an amount of a pollutant emitted into the air. The virtual analyzer includes an interface which receives signals corresponding to attributes of the MPPs. If the process is a wet flue gas desulfurization (WFGD) process, the signals include a signal corresponding to a measured pH level of the applied reactant. If the process is a selective catalytic reduction (SCR) process, the signals include a signal corresponding to a measured amount of the reactant exhausted by the process.

Abstract: A parameter value estimator is provided for a process performed primarily to control emission of a particular non-particulate pollutant, such as NOx and SO2, into the air. The process has multiple process parameters (MPPs) including a parameter representing an amount of the particular non-particulate pollutant emitted. The parameter value estimator includes either a neural network process model or a non-neural network process model. In either case the model represents a relationship between one of the MPPs, other than the parameter representing the amount of the emitted particular non-particulate pollutant, and one or more other of the MPPs. Also included is a processor configured with the logic, e.g. programmed software, to estimate a value of the one MPP based on a value of each of the one or more other MPPs and the one model.

Abstract: A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.

Abstract: A method and apparatus for optimizing air flow to a boiler of a power generating unit using advanced optimization, modeling, and control techniques. Air flow is optimized to maintain flame stability, minimize air pollution emissions, and improve efficiency.

Abstract: A method and apparatus for optimizing air flow to a boiler of a power generating unit using advanced optimization, modeling, and control techniques. Air flow is optimized to maintain flame stability, minimize air pollution emissions, and improve efficiency. An optimizer determines optimal setpoint values for manipulated variables associated with the power generating unit by using a model to minimize the value of a cost function while observing at least one constraint associated with operation of the power generating unit.

Abstract: An economic parameter estimator is provided for a process that has multiple process parameters (MPPs) and is performed to control emission of a pollutant into the air. The performance of the process is associated with one or more economic factors (EFs). The estimator includes either a neural network process model or a non-neural network process model. In either case, the model represents a relationship between one or more of the MPPs and an economic parameter. Also included is a processor configured with logic, e.g. programmed software, to estimate a monetary value of the economic parameter based on a value of each of the one or more MPPs, a value of each of at least one of the one or more EFs, and the one model.

Abstract: A controller is provided for directing control of a process performed to control an amount of a pollutant emitted into the air. The process has multiple process parameters (MPPs) The controller includes either a neural network process model or a non-neural network process model. Whichever type model is included, it will represent a relationship between one of the MPPs and other of the MPPs. The controller also includes a control processor having the logic to determine the validity of a measured value of the one MPP based on the one model. The control processor directs control of the process in accordance with the measured value of the one MPP only if the measured value of the one MPP is determined to be valid. On the other hand, if the measured value is determined to be invalid, the control processor may direct control of the process in accordance with an estimated value of the one MPP.

Abstract: A controller directs a process primarily performed to control emission of a particular pollutant into the air. The process has multiple process parameters (MPPs), including a parameter representing an amount of the particular pollutant. The controller includes either a neural network process model or a non-neural network process model. In either case, the model represents a relationship between a first of the MPPs and one or more of the other MPPs. The one or more other MPPs include a second of the MPPs which is other than the parameter representing the amount of the emitted particular pollutant. Also included is a processor configured with logic to estimate a value of the second MPP, and to direct control of the first MPP based on the estimated value of the second MPP and the model.

Abstract: A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.

Abstract: A controller for directing operation of an air pollution control (APC) system, requires a consumable to perform a process to control emissions of a pollutant. The process has multiple process parameters (MPPs). One or more of the MPPs is a controllable process parameters (CTPPs) and one of the MPPs is an amount of the pollutant (AOP) emitted by the system. A defined AOP value (AOPV) represents a limit on an actual value (AV) of the emitted AOP. An interface receives a value corresponding to a unit cost of the consumable. A control processor determines the cost of operating the system based on the received value corresponding to the unit cost of the consumable, and directs control of at least one of CTPPs based on the current value of that CTPP and the determined operating cost.

Abstract: Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model, the model having an input and an output and a mapping layer for mapping the input to the output through a stored representation of a system. A training data set is provided having a set of input data u(t) and target output data y(t) representative of the operation of a system. The model is trained with a predetermined training algorithm which is constrained to maintain the sensitivity of the output with respect to the input substantially within user defined constraint bounds by iteratively minimizing an objective function as a function of a data objective and a constraint objective. The data objective has a data fitting learning rate and the constraint objective has constraint learning rate that are varied as a function of the values of the data objective and the constraint objective after selective iterative steps.

Abstract: A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y.

Abstract: A method and apparatus for optimizing the operation of a single or multiple power generating units using advanced optimization, modeling, and control techniques. In one embodiment, a plurality of component optimization systems for optimizing power generating unit components are sequentially coordinated to allow optimized values determined by a first component optimization system to be fed forward for use as an input value to a subsequent component optimization system. A unit optimization system may be provided to determine goals and constraints for the plurality of component optimization systems in accordance with economic data. In one embodiment of the invention, a multi-unit optimization system is provided to determine goals and constraints for component optimization systems of different power generating units. Both steady state and dynamic models are used for optimization.

Abstract: A controller for directing operation of an air pollution control system performing a process to control emissions of a pollutant has multiple process parameters (MPPs). One or more of the MPPs is a controllable process parameter (CTPP) and one of the MPPs is an amount of the pollutant (AOP) emitted by the system. A defined AOP value (AOPV) represents an objective or limit on an actual value (AV) of the emitted AOP. The controller includes either a neural network process model or a non-neural network process model representing a relationship between each CTPP and the emitted AOP. A control processor has the logic to predict, based on the model, how changes to the current value of each CTPP will affect a future AV of emitted AOP, to select one of the changes in one CTPP based on the predicted affect of that change and on the AOPV, and to direct control of the one CTPP in accordance with the selected change for that CTPP.

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.

Abstract: A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

Abstract: A method and apparatus for controlling a non-linear mill. A linear controller is provided having a linear gain k that is operable to receive inputs representing measured variables of the plant and predict on an output of the linear controller predicted control values for manipulatible variables that control the plant. A non-linear model of the plant is provided for storing a representation of the plant over a trained region of the operating input space and having a steady-state gain K associated therewith. The gain k of the linear model is adjusted with the gain K of the non-linear model in accordance with a predetermined relationship as the measured variables change the operating region of the input space at which the linear controller is predicting the values for the manipulatible variables. The predicted manipulatible variables are then output after the step of adjusting the gain k.